P1-01
Development status of ABINIT-MP program in 2019
Yuji Mochizuki 1,2 Kota Sakakura 3 Hiromasa Watanabe 4 Kouichiro Kato 5
fullmoon@rikkyo.ac.jp k-sakakura@ay.jp.nec.com hi-watanabe@hpc.co.jp
kouichiro.kato@mizuho-ir.co.jp

Yoshinobu Akinaga 6 Kaori Fukuzawa 2,7 Koji Okuwaki ,7 Tatsuya Nakano 8
akinaga@vinas.com k-fukuzawa@hoshi.ac.jp k-okuwaki@hoshi.ac.jp nakano@nihs.go.jp
1

2

3

4
5

6
7

8

Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
Institute of Industrial Science, University of Tokyo, 4 -6-1 Komaba, Meguro-ku, Tokyo
153-8505, Japan
1st Government and Public Solutions Division, NEC Corporation, 5-7-1 Shiba,
Minato-ku, Tokyo 108-8001, Japan
HPC SYSTEMS Inc., Kaigan 3-9-15, Minato-ku, Tokyo 108-0022, Japan
Mizuho Information & Research Institute Inc., 2 -3 Kanda-nishiki-cho, Chiyoda-ku,
Tokyo 101-8443, JapanVINAS Co. Ltd., 2-3-31 Dojima, Kita-ku, Osaka 530-003, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
Division of Medicinal Safety Science, National Institute of Health, 3-25-26 Tonomachi
Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan

Keywords: Fragment molecular orbital, FMO, ABINIT-MP, Interaction energy analysis
The ABINIT-MP program [1] has been widely used in Japan, especially in the pharmaceuticalcommunities, e.g., FMO Drug Design Consortium (FMO-DD) [2]. At the Open Version 1 Revision
10 in Spring, 2018, the decomposition analysis of pair interaction energy (PIEDA) [3] was madeusable in ABINIT-MP, as was in GAMESS-US [4]. It has been recently known that PIEDA-based
regression analyses of drug activities are frequently better than those with parent IFIE values. As
other utilities, a couple of orbital-wise analysis tools, FILM [5] and CAFI [6], have also been
available from Revision 10. The latest release of ABINIT-MP is Revision 15 in Spring 2019,providing the fragmentation at the sp2-carbon of peptide bond [7], higher-order correlated
calculations (such as CCSD(T)) [8], and non-empirical HF+D type correction. In this presentation,
we will brief the status of ABINIT-MP in 2019.
[Acknowledgment] This work was supported by MEXT as a social and scientific priority issue #6 to be
tackled by using post-K computer (FS2020) and also as a grant-in-aid (Kaken-hi) No. 16H04635.

[1] Tanaka, S., Mochizuki, Y., Komeiji, Y., Okiyama, Y., Fukuzawa, K., Electron-Correlated
Fragment-Molecular-Orbital Calculations for Biomolecular and Nano Systems, Phys. Chem.
Chem. Phys., 16:10310-10344, 2014.
[2] Fukuzawa, K., et al., <https://fmodd.jp>.
[3] Fedorov, D. G., Kitaura, K. Pair Interaction Energy Decomposition Analysis, J. Comp. Chem. 28:
222-237, 2007.
[4] Fedorov D. G., The fragment molecular orbital method: theoretical development,
implementation in GAMESS, and applications, WIREs, Comp. Mol. Sci., e1322, 2017.
[5] Ishikawa, T., Mochizuki, Y., Amari, S., Nakano, T., Tokiwa, H., Tanaka, S., Tanaka, K.
Fragment interaction analysis based on local MP2, Theor. Chem. Acc., 118:937-945, 2007.
[6] Mochizuki, Y., Fukuzawa, K., Kato, A., Tanaka, S., Kitaura, K., Nakano, T., A configuration
analysis for fragment interaction, Chem. Phys. Lett., 410:247-253, 2005.
[7] Akinaga, Y., Nakano T., Fukuzawa, K., Kato, K., Mochizuki, Y., to be published.
[8] Mochizuki, Y., Yamashita, K., Nakano, T., Okiyama, Y., Fukuzawa, K., Taguchi, N., Tanaka S.,
Higher-order correlated calculations based on fragment molecular orbital scheme , Theor. Chem.
Acc., 130:515-530, 2011.

P1-02

Prediction of log P for cyclodextrin-drug complexes using
computational chemistry
Masao Fujisawa 1

Hirohito Ikeda 2

fujisawa@waka.kindai.ac.jp

ikeda@fukuoka-u.ac.jp

Takayoshi Kimura 3
kimura@chem.kindai.ac.jp

1

2

3

Department of Biotechnological Science, Kindai University, 930 Nishimitani, Kinokawa -City,
Wakayama, Tokyo 649-6493, Japan
Faculty of Pharmaceutical Science, Fukuoka University, 8 -19-1 Nanakuma Fukuoka 814-0180,
Japan
Department of Chemistry, Kindai University, Kowakae, Higashi -osaka 577-8502, Japan

Keywords: log P, DFT, molecular dynamics, Inclusion complex
It is important to examine the effect of hydrophobicity on the interaction between organic
pollutants and water molecules accurately to understand the mechanism of removal of organic
pollutants from water. Cyclodextrin (CyD) is used as a carrier to improve the solubility of poorly
water-soluble drugs. The accumulation of accurate and quantitative values of the changes i nthermodynamic functions on molecular inclusion of guest molecules into CyDs in aqueous
solutions have been carried out systematically by microcalorimetry[1 -3], in order to clarify themechanisms of molecular recognition and discrimination in aqueous solu tions. However,determining the solubilities of CyD-drug complexes experimentally is difficult. Thus, log P
partition coefficients of inclusion complexes obtained from the difference between their solvation
Gibbs energy in two-phase octanol/water are utilized to estimate the hydrophobicity of complexes.
In this paper, we report the contribution of dispersion forces to the solvation Gibbs energy of
isolated molecules in aqueous solution. Solvation Gibbs energies were determined using
vibrational frequency calculations with the dispersion-corrected density functional theory (DFT-D).
The difference between the solubilities of mefenamic acid and a CyD -mefenamic acid complex inwater was estimated using predicted log P. Water solubility of 2-hydroxypropyl-beta-CyD
improved on the formation of the CyD inclusion complex. Our prediction method reproduced thisphenomenon well. Moreover, the slight difference between the solubilities of mefenamic acid andniflumic acid could be reproduced well. In addition, the epinep hrine system will be discussed on
the day of annual meeting[4].
[1] Kimura T., Yukiyama T., Fujie S., Fujisawa M., Aki H., Kamiyama T. , Enthalpy and entropychanges on molecular inclusion of pentane derivertives into α-cyclodextrin cavities in aqueous
solutions,, Journal of Inclusion Phenomena and Macrocyclic Chemistry , 70(3-4):269-278, 2011.
[2] Kimura T., Yukiyama T., Fujisawa M., Thermodynamic properties of inclusion complexes of
α-cyclodextrin + aliphatic nitriles (H(CH 2 )nCN: n = 1-8) in aqueous solution, Journal of
Thermal Analysis and Calorimetry, 108(2):695-704, 2012.
[3] Ikeda H., Fukushige Y., Matsubara T., Inenaga M., M. Kawahara M., Yukawa M., Fujisawa M.,
Yukawa E., Aki H., Improving water solubility of nateglinide by complexation of β -cyclodextrin,
Journal of Thermal Analysis and Calorimetry, 123(3):1847-1850, 2016.
[4] Rayenne D., Djameleddine K., Quantum mechanical study of complexation of dopamine andepinephrine with β-cyclodextrin using PM6, ONIOM and NBO analysis, Journal of Computational
and Theoretical Nanoscience, 9(10):1571-1576, 2012.

P1-03

Binding Energy Calculation of Protein-Peptide Complex
Using Unbiased MD Simulations and MSM Analysis
Hiroaki Hata 1
hiroakihata@bio.titech.ac.jp

Masayoshi Nishiyama 3
1

2

3

4

Yasutaka Nishihara 2

Ikuro Kawagishi 4

Akio Kitao 1

School of Life Science and Technology, Tokyo Institute of Technology, M6-13, 2-12-1,
Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi,
Bunkyo-ku, Tokyo 113-0032, Japan
Department of Physics, Kindai University, 3-4-1 Kowakae, Higashiosaka, Osaka 577-8502,
Japan
Department of Frontier Biosciences, Hosei Univesity, Koganei, Tokyo 184-0002, Japan

Keywords: Molecular dynamics simulation, Protein-peptide binding, Markov state model
Association/dissociation among protein molecules plays central roles in functional regulation ofliving organisms. It is of great biological and pharmaceutical interest to characterize the dynamics
of interactions among proteins. Molecular dynamics (MD) simulations are powerful tool forinvestigation of the protein-protein interaction in atomic detail. However, direct observation of
association/dissociation events of protein molecules using conventional MD simulations are often
difficult due to the limitation of the simulation time [1]. Therefore, some widely used MD methods,
such as the steered MD, utilize external forces to enhance the event [2], although the artificial
force may induce unnatural behavior of molecules.
We developed a MD method called PaCS-MD which efficiently enhances conformational
fluctuations not by adding bias forces but by repeating short MD simulations and conformational
selections [3]. Recently, we showed that the Markov state model (MSM) analysis can be directly
applied to the PaCS-MD trajectory for calculation of binding energy, association/dissociation rate
constants, and residence time [4]. In this presentation, we applied the PaCS-MD/MSM method tothe protein complex of bacterial flagella motor protein FliM and the chemotaxis signaling protein
CheY. The binding of CheY onto the N-terminal segment of FliM has been known to regulate therotational direction of bacterial flagella motor. We successfully observed multiple dissociation
pathways of the complex using PaCS-MD, and calculated the standard binding energy using MSM.
We found that the binding energy is significantly dependent on the hydrostatic pressure condition
and the calculated pressure dependence agreed well with experimental observations. This result
indicated that the binding of CheY-FliM, and consequently the rotation of flagella motor, can be
modulated by applying pressure. We will also discuss the mechanism how the environmentalpressure modifies the interaction between CheY-FliM.
[1] Pan, A. C. et al. Atomic-level characterization of protein–protein association. Proc. Natl. Acad.
Sci. 116, 4244–4249 (2019).
[2] Cuendet, M. A. & Michielin, O. Protein-protein interaction investigated by steered molecular
dynamics: The TCR-pMHC complex. Biophys. J. 95, 3575–3590 (2008).
[3] Harada, R. & Kitao, A. Parallel cascade selection molecular dynamics (PaCS-MD) to generate
conformational transition pathway. J. Chem. Phys. 139, 035103 (2013).
[4] Tran, D. P. & Kitao, A. Dissociation Process of a MDM2/p53 Complex Investigated by Parallel
Cascade Selection Molecular Dynamics and the Markov State Model. J. Phys. Chem. B 123,
2469–2478 (2019).

P1-04
Interaction Analyses between Calcite/Apatite and Peptides
by Fragment Molecular Orbital Method
Ryo Hatada 1 Kouichiro Kato 2 Koji Okuwaki 3 Kaori Fukuzawa 3,4 Yuji Mochizuki 1,4
hatada@rikkyo.ac.jp kouichiro.kato@mizuho-ir.co.jp k-okuwaki@hoshi.ac.jp
k-fukuzawa@hoshi.ac.jp fullmoon@rikkyo.ac.jp

1

2

3

4

Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
Mizuho Information & Research Institute Inc., 2 -3 Kanda-nishiki-cho, Chiyoda-ku,
Tokyo 101-8443, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
Institute of Industrial Science, University of Tokyo, 4 -6-1 Komaba, Meguro-ku, Tokyo
153-8505, Japan

Keywords: Biomineral, Fragment molecular orbital method, Data driven analyses
In the natural world, organisms produce various bio-related minerals (e.g., pearls, shells, bones
and exoskeletons), and such actions are called as biomineralizations. Most of biominerals consistof inorganic crystals and biomolecules, providing a coexistence of lightweight and strong
properties. Therefore, fundamental understanding for the complex inorganic -bio hybrid structuresor the adsorption mechanism of biomolecules on inorganic crystals should be helpful not only toelucidate the mechanism of biomineralization but also to design novel functional materials. In ourprevious study, we have performed the fragment molecular orbital (FMO) method to analyze theinteraction between calcite crystal (CaCO 3 ) and DDGSDD motif having the corresponding specificadsorption ability [1]. A similar FMO-based analysis [2] was carried out for the system ofhydroxyapatite (Ca 5 (PO 4 ) 3 (OH)) and ESQES motif [3]. These two studies revealed that the role inadsorption of each residue is greatly different and also that the C-terminal side plays vital roles inadsorption.
In this study, we have investigated the contribution of each energy component to the interaction
between crystal and peptide motif, based on the pair interaction energy decomposition (PIEDA)
analysis derived from FMO calculations. In addition, we extracted the interaction energies between
residues and close fragments in crystal as well as the distance and arrangement information
between residues and crystal. Such data were subjected to the multiple regression analysis bywhich feature parameters could be identified. As a result, it was found that the nature of interaction
between calcite and DDGSDD motif is more ionic than that between hydroxyapatite and ESQES
motif and thus that the distance parameters among residues and close fragments in crystal are
principal features. In addition, it was suggested that the presence of ionized carboxyl group(-COO - ) of D or E is important in both systems. Detailed results will be reported on thepresentation day.
[Acknowledgment] This work was supported by MEXT as a social and scientific priority issue #6 to be
tackled by using post-K computer (FS2020) and also by a grant-in-aid (Kaken-hi) No. 16H04635.
[1] Gotliv, B., Kessler, N., Sumerel, J. L., Morse, D. E., Tuross, N., Addadi, L., Weiner, S., Asprich: A
Novel Aspartic Acid-Rich Protein Family from the Prismatic Shell Matrix of the Bivalve Atrina
rigida, ChemBioChem., 6: 304-314, 2005.
[2] Kato, K., Fukuzawa, K., Mochizuki, Y., Modeling of hydroxyapatite -peptide interaction basedon fragment molecular orbital method, Chem. Phys. Lett., 629: 58-64, 2015[3] He, G., Dahl, T., Veis, A., George, A., Nucleation of apatite crystals in vitro by self-assembled
dentin matrix protein 1, Nat. Mater., 2: 552-558, 2003.

P1-05

Fragment Molecular Orbital Method Applied to
Factor Xa Inhibitors
Kazufumi Ohkawa

okawa.kf@om.asahi-kasei.co.jp

Laboratory for Medicinal Chemistry, Pharmaceuticals Research Center, Asahi Kasei Pharma
Corporation, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan

Keywords: Fragment Molecular Orbital, Factor Xa, antithrombotic
Factor Xa(FXa) occupies the central position in the blood coagulation cascade and theirimportance is reflected in the wide clinical use of FXa inhibitors. One of the second generation
FXa inhibitors, Rivaroxaban has chlorothiophene as P1 motif instead of benzamidine which is
popular in first generation FXa inhibitors. Both benzamidine and chlorothiophene could bind S1
pocket. As a basic P1 group, benzamidine interact with ASP189 by salt bridge formation. For thebinding energy of chlorothiophene as neutral P1 group, has been ascribed to (1) direct chlorine
atom interaction with TYR228 and (2) polarization of the C-H bond ortho to the chlorine atom,
leading to a favorable interaction with ASP189.
An in-depth energetic analysis by Fragment Molecular Orbital method (FMO) suggests that thenature of the interaction energy between chlorothiophene and S1 pockets is due primary to (2)polarization of the C-H bond ortho to the chlorine atom, leading to a favorable interaction with
ASP189.
We also applied FMO to several data sets, including a set of reported FXa inhibitors. We foundthat a strong correlation between binding energy (Total-IFIE) and the activity value on 50%
inhibitory concentration (IC50) of the inhibitors.
[1] K Kitaura, E Ikeo, T Asada, T Nakano, and M Uebayasi, Fragment Molecular Orbital Method:
An Approximate Computational Method for Large Molecules, Chem. Phys. Lett., 313:701– 706,
1999.
[2] S Roehrig., et al., Discovery of the novel antithrombotic agent 5-chloro-N-({(5S)-2-oxo-3[4-(3-oxomorpholin-4-yl)phenyl]-1,3-oxazolidin-5-yl}methyl)thiophene- 2-carboxamide (BAY59-7939): an oral, direct factor Xa inhibitor, J. Med. Chem., 48, 19:5900-5908, 2005.
[3] C Chan., et al., Factor Xa Inhibitors: S1 Binding Interactions of a Series ofN-{(3S)-1-[(1S)-1-Methyl-2-morpholin-4-yl-2-oxoethyl]-2-oxopyrrolidin-3-yl}sulfonamides, J.
Med. Chem., 50, 7:1546-1557, 2007.
[4] D. J. P. Pinto., et al., Factor Xa Inhibitors: Next-Generation Antithrombotic Agents, J. Med.
Chem., 53, 17:6243-6274, 2010.
[5] Y. N. Imai, Y. Inoue, I. Nakanishi, and K. Kitaura, Cl–π interactions in protein–ligand
complexes, Protein Sci., 17, 7:1129–1137, 2008.

P1-06
Influence of phosphorylation on structure and electronic statesof tau-protein: MD and ab initio fragment MO simulations
Katsumi Suzuki 1
k-suzuki@klab.cs.tut.ac.jp

1

2

Riku Sato 1
sato@klab.cs.tut.ac.jp

Pavel Karpov 2
karpov@nas.gov.ua

Sergiy Shulga 2

Yaroslav Blume 2

Noriyuki Kurita 1

Shulga5@i.ua

blume@nas.gov.ua

kurita@klab.cs.tut.ac.jp

Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
Institute for Food Biotechnology and Genomics, National Academy of Sciences of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Tau-protein; Aggregate; Phosphorylation; Alzheimer’s disease;
Molecular simulation; Molecular dynamics; Fragment molecular orbital
Alzheimer’s disease (AD) is the most common neurodegenerative disease characterized byneurofibrillary tangles (NFTs) in a brain. It is considered that NFTs are caused by the aggregationof abnormally phosphorylated tau-proteins in neurons. The phosphorylation sites of tau-protein
were classified into three categories [1]: ones identified only in the brains of AD patients, ones
identified in the brains of both AD patients and healthy people, and ones identified only in thebrains of healthy people. However, it is not clear why such a classification occurs and how thephosphorylation of tau-protein affects its structure and aggregation.
In the present study, we investigated the influence of phosphorylation on the structure andelectronic states of tau-protein at atomic and electronic levels, using molecular dynamics (MD)and ab initio fragment molecular orbital (FMO) calculations in water. As a target structure of
tau-protein, we employed the repeat 1 domain among the microtubule binding repeat (MBR) of
tau-protein. In the domain, the peptide composed of 89 residues from the 186 th to 275 th residues in
the N-terminal of MBR was selected, because this region has many phosphorylation sites [2].
Based on the PDB structure (PDB ID: 5N5A), we employed protein 3D structure prediction
program I-TASSER to make the initial structure of peptide. One serine residue among the twelve
ones was phosphorylated to make twelve initial structures of phosphorylated tau-protein. Amongthem, eight sites are identified only in the brains of AD patients, while the other four sites are
identified in the brains of both AD patients and healthy people. We attempted to elucidate thedifferences in the influence of phosphorylation depending on the site of phosphorylation, using thefollowing molecular simulations.
We first carried out 100 ns MD simulations for the twelve phosphorylated tau-protein as wellas the non-phosphorylated tau-protein at 300 K in explicit waters, in order to reveal the influenceof phosphorylation on the structure. The snapshot at 100 ns was optimized by MM method, and theelectronic states of the optimized structure were investigated by ab initio FMO calculations. Basedon the results of FMO, we investigated how the interactions between amino acid residues of
tau-protein are changed by the phosphorylation of one serine residue in the tau-protein. The detailsof the results obtained by the MD and FMO calculations will be shown in the poster.
[1] L. Martin, X. Latypova, and F. Terro, “Post-translational modifications of tau protein:
Implications for Alzheimer’s disease,” Neurochem. Int. 58, 458–471, 2011.
[2] M. Morishima-Kawashima et al., “Proline-directed and Non-proline-directed Phosphorylationof PHF-tau,” J. Biol. Chem. 270, 823–829, 1995.

P1-07

Determination of stable protonation states of amino acidresidues in metalloproteinase-inhibitor complex:
ab initio molecular simulations
Takuya Ezawa 1
ezawa@klab.cs.tut.ac.jp

Satoshi Sugiyama 1

Ayami Ara 1

sugiyama@klab.cs.tut.ac.jp

ara@klab.cs.tut.ac.jp

Shirley Weiyan Chen 2

Ingebrigt Sylte 3

Noriyuki Kurita 1

wchen003@citymail.cuny.edu

ingebrigt.sylte@uit.nokurita@cs.tut.ac.jp

Department of Computer Science and Engineering,
Toyohashi University of Technology, Toyohashi, 441-8580, Japan2
Department of Biomedical Engineering,
The City College of New York, 160 Convent Avenue New York, NY 10031, U.S.
3
Department of Medical Biology, Faculty of Health Sciences,
UiT The Arctic University of Norway, NO-9037 Tromso, Norway
1

Keywords: Metalloproteinases; Bacterial virulence; Molecular simulation; Fragment molecularorbital; Protein-ligand interactions; Inhibitor; Antimicrobial agent
Pseudolysin (PLN) is a metalloproteinase secreted from bacteria and degrades extracellular
proteins to produce bacterial nutrition. It is thus expected that inhibitors against PLN can suppress
the growth of bacteria and their pandemic spread. In addition, since these inhibitors do not attack
to bacteria directly, there is a reduced risk for producing drug-resistant bacteria. PLN has a Zn ion
around its ligand-binding pocket (LBP), so that there is a possibility that the interactions between
Zn and some amino acid residues as well as ligand are significantly changed depending on the
protonation states of the residues around Zn. Therefore, we here considered some types of the
protonation states for the Glu and His residues existing around Zn and elucidated which
protonation state is more stable, based on ab initio fragment molecular orbital (FMO) calculations.
In addition, specific interactions between PLN residues, Zn and its inhibitor were investigated inan electronic level using the ab initio FMO method to reveal the following points.
(1) Although Glu141 existing near the LBP of PLN can have a protonated state [1],non-protonated Glu141 is more preferable than the protonated one in the PLN-inhibitor
complex [2] from the viewpoint of the PLN structure around Zn.
(2) His223 existing near LBP can have Hid or Hie protonated state depending on the ligand.
(3) In proposing novel ligands for PLN, two types of protonation states for His223 should beconsidered to elucidate the specific interactions between PLN and ligand.
The FMO calculations are underway now for the PLN complexed with the other inhibitors, inorder to make clear the reason for the wide range of binding affinity between PLN and its
inhibitors observed by the previous experiments [2].
[1] X. Hu, S. Balaz, and W. H. Shelver, A practical approach to docking of zincmetalloproteinase inhibitors, J. Mol. Graph. Model., 22 (2004) 293–307.
[2] A. M. Kany et al., Tackling pseudomonas aeruginosa virulence by a hydroxamic acid-based
LasB inhibitor,” ACS Chem. Biol., 13 (2018) 2449–2455.

P1-08Regulation mechanism of agonistic / antagonistic activities ofvitamin D receptor analyzed by generalized ensemble method
Takafumi Kudo 1
w185412d@yokohama-cu.ac.jp

Tsutomu Yamane 1
tyamane@yokohama-cu.ac.jp

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

Mitsunori Ikeguchi 1,2
ike@yokohama-cu.ac.jp

1

Graduate School of Medical Life Science, Yokohama City University,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan
2
RIKEN Medical Sciences Innovation Hub Program,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan

Keywords: Molecular dynamics simulation, Generalized ensemble method, Vitamin D receptor
Vitamin D receptor (VDR) is one of the nuclear receptors (NR) and an important target for drug
discovery. The mechanism of the agonistic/antagonistic activities of VDR is not clear on the basisof X-ray crystallography. According to X-ray crystal structures of other NRs and experimental
results, the ligand-binding domain (LBD) undergoes the conformational change upon ligand binding,and a local conformational change around helix 12 is key to regulating agonistic/antagonistic
activities. However, since the crystal structures of agonist and antagonist complexes of VDR-LBD
were almost identical, it is difficult to discuss the differences of their activities on the basis of
crystal structures.
In order to clarify the regulation mechanism of agonistic/antagonistic activities of VDR ligands,
molecular dynamics (MD) simulations for VDR-LBD were performed. However, due to the limited
time scale of the conventional MD simulations, it was difficult to observe large conformationalchanges. Therefore, gREST [1], one of the generalized ensemble methods, was employed for theefficient conformational sampling of VDR-LBD. In the gREST simulations, spontaneous
conformational changes of VDR were observed, indicating a mechanism underlying the differencesof agonistic/antagonistic activities. A typical conformation of the antagonist b ound form has asimilar feature to that observed by MD-SAXS analysis [2].
[1] Kamiya, M., Sugita Y., J. Chem. Phys., 149, 072304-1-11, 2018
[2] Anami, Y., Shimizu, N., Ekimoto, T., Egawa, D., Itoh, T., Ikeguchi, M., Yamanoto, K., J. Med.
Chem., 59, 7888-7900, 2016.

P1-09The inhibition mechanism of HSP90 function
by a medium molecular drug
Lisa Matsukura 1

Kazuto Mochizuki 2

liz@miyashita-lab.net

m1743011@edu.cc.uec.ac.jp

Naoyuki Miyashita 1,2,*
miya@waka.kindai.ac.jp

Masumi Taki 2
taki@pc.uec.ac.jp

Shinichi Watanabe 2
shin.watanabe@uec.ac.jp

12

Department of Biological System Engineering, Graduate school of Biological Oriented Scienceand Technologies, KINDAI University, 930 Nishimitani, Kinokawa, Wakayama 649−6493,
Japan
Department of Engineering Science, Graduate School of Informatics and Engineering,
University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585, Japan

Keywords: Molecular dynamics Simulation, Ligand Docking
HSP90 is known as one of the anticancer drug targets. The previous study has suggested theactivity of HSP90 is suppressed by cryptand-aptamer, which consists of 14 residue peptide andcryptand compound. A peptide, which has 14 residue peptide, and the cryptand-aptamer bind to the
HSP90. The cryptand-aptamer has a higher binding affinity. However, the inhibition mechanism of
HSP90 function has not been understood yet. In this study, we investigated the structural differences
between the peptide and cryptand-aptamer using 20µs molecular dynamics simulations; we
performed docking simulation of HSP90 and the most reliable structure of cryptand-aptamer toestimate the docking mode; we carried out the molecular dynamics simulation of the complex
structure to understand the inhibition mechanism of HSP90 function by the cryptand-aptamer.
Our results have shown that the turn structure in the peptide is more unstable than that in thecryptand-aptamer, and the cryptand-aptamer binds to the beta-strand side of HSP90. The stabilityof the turn structure of the medium molecular drug affects the binding affinity. The closed
conformation of HSP90 usually has the domain swapping around N-terminal beta-strand[1]. Our
result suggests that the cryptand-aptamer inhibits the domain swapping in the closed conformationof HSP90.

Fig. 1 The interaction between N-terminal region and the cryptand-aptamer.
a) HSP90 without the drug, b) HSP90 with the drug.
[1] Ali M.M., et al., Crystal structure of an HSP90–nucleotide–p23/Sba1 closed chaperone complex, Nature, 440:1013-1017, 2006.

P1-10

Molecular Dynamics Simulations of HIV Tat protein andAmyloid-β peptides
Kazumi Omata 1
komata@hosp.ncgm.go.jp

12
3

Hisashi Okumura 2,3
hokumura@ims.ac.jp

Department of Data Science, National Center for Global Health and Medicine, 1-21-1 Toyama,
Shinjuku, Tokyo 162-0052, Japan
Institute for Molecular Science, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, JapanThe Graduate University for Advanced Studies, 38 Nishigonaka, Myodaiji, Okazaki, Aichi
444-8585, Japan

Keywords: HIV Tat protein, Amyloid-β, Ligand docking, Molecular dynamics, neurocognitive
dysfunction
Continuing from CBI 2018, we show the results of molecular dynamics (MD) simulations about
the docking of an HIV transactivator of transcription (Tat) protein and amyloid-β (Aβ) peptides
and identify binding sites of these molecules.
Recently, the experiments by Hategan et al. suggested that HIV Tat binds to the exterior surfaces ofAβ fibrils, increasing β-sheet formation and lateral aggregation into thick multifibrillar structures[1]. As a result, the fibers increase their rigidity and mechanical resistance. They also showed thatthe aggregate of Tat-Aβ complexes synergistically induced neurotoxicity both in vitro and inanimal models. Apart from such a study, Okumura and Itoh examined the structure of Aβ fibrilsby all-atom MD simulations, and demonstrated that structural fluctuations at the fibril ends play
an important role for the fibril formation [2]. Based on the knowledge about the structure of Aβfibrils derived from their MD simulations, the present study has attempted to find which parts ofAβ peptides are tightly associated with HIV Tat proteins, using MD simulations.
The all-atom MD simulations were carried out using the Generalized-Ensemble Molecular
Biophysics (GEMB) program [3]. The AMBER99SB force field was used for the HIV Tat and Aβ,
and the TIP3P rigid-body model was used for the water molecules. The electrostatic potential was
calculated by the particle mesh Ewald method. Temperature was controlled at 298 K with the
Nosé-Hoover thermostat, and pressure was controlled at 0.1 MPa with the Andersen barostat. The
symplectic quaternion scheme was employed for the rigid-body water molecules.
We have investigated three sets of initial condition. In the first set, a Tat protein was placed near
one of the two beta sheets constituting the Aβ. The Tat moved and was bound to the N-terminal
soft region of the Aβ. The second set in which a Tat was placed near the other beta sheet also
exhibited the movement and binding of the Tat to the N-terminal region. Considering these results,
in the third set, we placed a Tat protein near the loop region of the Aβ which is far from the
N-terminal soft region. Again, this set provided a similar movement and binding to the soft region.
These results allow us to conclude that the binding of a Tat and Aβ occurs only in the soft regionof the Aβ. In addition, we found that this binding may be attributed to hydrophobic interactions
among several residues in Tat and Aβ.
[1] Hategan, A., et al., Nature Structural & Molecular Biology 24, 379–386, 2017.
[2] Okumura, H. and Itoh, S. G., Scientific Reports 6, Article number:38422, 2016.
[3] Okumura, H., Proteins, 80: 2397-2416, 2012.

P1-11
Dynamics of the transmembrane region of β-secretasein raft environment
Kaori Yanagino(KINDAI Univ.)

CancelledP1-12Development of the CHARMM force field for Cyclosporine A
and application to molecular dynamics simulations using amembrane-water system
Tsutomu Yamane 1
tyamane@yokohama-cu.ac.jp

Ryo Takahashi 2
i140382e@yokohama-cu.ac.jp

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

1
2

3

Akari Ito 2
i160060f@yokohama-cu.ac.jp

Mitsunori Ikeguchi 1,3
ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan
Department of Medical Life Science, Faculty of Sciences, International Arts and Science
Studies, International College of Arts and Sciences, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, JapanRIKEN Medical Sciences Innovation Hub Program, 1-7-29 Suehiro-cho, Tsurumi-ku,
Yokohama 230-0045, Japan

Keywords: CHARMM force field, cyclosporin A, cyclic peptide, membrane permeability,
molecular dynamics simulation
Cyclosporin A (CsA), which is found in Tolypocladium inflatum in 1969 [1], is a cyclic
peptide consisting of 11 residues and used as an immune suppresser. CsA is permeable tobiological membranes despite its high molecular weight (M. w. ~1200). CsA has two types ofconformations with a hydrophobic surface (closed form) and with a hydrophilic surface(open form), and the membrane permeability of CsA is possibly related to structuralchanges between these conformations during membrane permeation process. CsA contains
unnatural residues (N-methyl amino acids), which contribute to the structural change of
CsA.
Here, we developed the CHARMM force field [2] of CsA. Specifically, we developed CMAP
terms of N-methyl amino acids, which are cross-terms of backbone dihedral angle  and  ofamino acids, by high-level QM calculations. MD simulations in water and chloroform
solvent were performed and these results were compared with crystal structures and NOEfrom NMR measurements. Also, we performed MD simulati ons of CsA and its metabolite,
cyclosporin E (CsE) in the water-membrane system and analyzed the dynamics in the lipid
bilayer.

[1] Svarstad, H. et al., Biodiversity and Conservation, 9: 1521–1541, 2000.
[2] Huang, J. et al., Nature Methods, 14:71-73, 2016.

P1-13

Analyzing intramolecular interaction using canonical
Kohn-Sham molecular orbital calculation in protein
Toshiyuki Hirano 1
t-hirano@iis.u-tokyo.ac.jp

1

Fumitoshi Sato 1
satofumi@iis.u-tokyo.ac.jp

Institute of Industrial Science, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

Keywords: Canonical Molecular Orbital Calculation, Interaction energy
We have developed a QM application, ProteinDF [ 1], which is designed for canonical Kohn-Shammolecular orbital (CMO) calculations of large molecular system. By using the ProteinDF, some
CMO calculations of protein have been reported [ 2-5]. The CMO of protein provides usefulknowledge, which are hard to observe in experiments, such as molecular orbitals, energy level,
charge density and so on. Our previous CMO calculation results showed that the CMO of protein
was widely spread throughout the whole molecular region, and suggested that the molecularproperties were related to the spread of the CMOs. On the other hand, for phenomenon analysis andmolecular design, visualization of characteristic interactions between amino acid residues would
assist in understanding. Energy decomposition analysis approaches [6], which are one of theinteraction energy calculation methods, have been well-known and needed to evaluate some CMO
calculations of complex and its subunits. In the case of protein, however, there are a lot ofinteraction calculations among subunits so that the computational cost of each subunit is expensive.
The purpose of this study is to estimate the interaction energies among amino acid residues and/or
substrates from the result of CMO calculation with as little effort as possible.
In this study, the interaction energy was estimated based on the energy density analysis (EDA)
method [7], which divided the total energy of molecular system into atomic energies. The totalenergy of electrons in HF calculation is obtained by the sum of dot product of the density matrix
and the sum of core Hamiltonian and Fock matrix. Similar to Mulliken population analysis, the sumof the corresponding matrix elements is estimated to be the interaction energy. One of theadvantages of this method is that the interaction energy is given by the sum of one electron term,
Coulomb term, and XC term (for DFT). In order to calculate the XC interaction energy, the matrix
elements of the energy were needed; however, only the sum of the XC energy is given in the grid
method, which is commonly used to estimate XC term in many DFT application. In this study, thegrid-free method was applied, and so it was suited for representation of AO based XC energy matrix.
Analyzing intramolecular interactions in s ome proteins have been performed. The Mulliken
population of the amino acid residue could only determine the charge density of itself. However, by
the analysis method of this research, it was possible to quantify specific Coulomb interactions
between amino acid residues. Not only Coulomb interaction but also interaction of one electron
term and exchange correlation term could be observed. We will show the analyzing method andthese results.
[1] https://github.com/ProteinDF/ProteinDF[2] T. Inaba, et.al., Chem. Phys. Lett., 434, 331-335 (2007).
[3] Y. Tokita, et. al., Angew. Chem. Int. Ed., 50, 11663 (2011).
[4] K. Chiba, et.al., Int. J. Quant. Chem., 113, 2345-2354 (2013).
[5] Y. Yagi, et.al., Trans. Jpn. Soc. Sim Tech., 4, 127 (2012).
[6] M. Phipps, et.al., Chem. Soc. Rev., 44, 3177 (2015).
[7] H. Nakai, Chem Phys. Lett., 363, 73-79 (2002).

P1-14
Homology Modeling of the Protein Structure in the Solution using
Deep Auto Encoder and Trajectories of the Long-time Molecular
Dynamics Simulation of Template Proteins
Masaya Furue 1

Lisa Matsukura 1

shiver@miyashita-lab.net

liz@miyashita-lab.net

Mitsutaka Nemoto 1
nemoto@waka.kindai.ac.jp

1

Naoyuki Miyashita 1
miya@waka.kindai.ac.jp

Department of Biological System Engineering, Graduate school of Biological Oriented Scienceand Technologies, KINDAI University, 930 Nishimitani, Kinokawa, Wakayama 649−6493,
Japan

Keywords: Deep Learning, Protein Structure Prediction, ,Homology Modeling, Molecular
Dynamics Simulation
A lot of primary sequences of the proteins have been elucidated by next-generation sequencer and soon, while the tertiary structures of proteins usually have been solved by the NMR spectroscopy, X-ray
structure analysis, and Cryo-EM. The total number of known tertiary structures of proteins are less than
the total number of known primary sequences. Homology modeling is the methods to predict the tertiary
structure of target protein using template protein structures, which have known tertiary structures, and
MODELLER[1] and SWISS - MODEL [2] are two of famous homology modeling programs. Recently,
a machine learning and deep learning has been widely used as tools for image recognition, self-driving
and so on. We have developed a homology modeling program, mDeepHoMe, using deep learning
methods (Fig. 1). The trajectories of the molecular dynamics simulations of template structures are usedas the learning data in mDeepHoMe. PDB data often have the structure differences between a structurein PDB and the structure in the solution which is after molecular dynamics simulations. In this study,
we have tried to predict the equilibrated structure of target protein. We performed the long-time
molecular dynamics simulations of template proteins. The trajectories was classified by principal
component analysis, and we used it to the learning data of mDeepHoMe.

Fig. 1

Schematic figure of mDeepHoMe Program[1] Fiser A, Sali A, et al, Methods in Enzymology 374, pp 461-491, (2003)[2] Andrew Waterhouse et al, Nucleic Acids Research, 46, pp.W296–W303, (2018)[3] Li H et al, J Proteomics Bioinform 9, pp306-313, (2016)

P1-15Cluster Analysis of Amino Acid by Inter-Fragment Interaction
Energy Using Non-Metric Multidimensional Scaling Method
Abe Yuki

Yamanaka Masanori

csyk16009@g.nihon-u.ac.jp

yamanaka.masanori@nihon-u.ac.jp

Department of Physics, College of Science and Technology, Nihon University, 1-8 Kanda-Surugadai,
Chiyoda-ku, Tokyo 101-8308, Japan

Keywords: Fragment molecular orbital method, Dimensional scaling, Inter fragment interaction energy
Fragment molecular orbital (FMO) method is a method of calculating the energy and the electron density by
dividing a molecular system into fragments, calculating the electronic states of fragments and fragment pairs,
and gathering them [1]. Using the method and the Auto-FMO protocol the FMO data base is released [2,3]. In
this work, we investigate the inter-fragment interaction energy (IFIE) matrix by the non-metricmultidimensional scaling method and perform a cluster analysis for the amino acids and the ligand. The nonmetric multidimensional scaling is a method of embedding an object under conditions that preserve thedistance relationship between elements in a certain space, which is one of the multivariate analysis, mainly
used for the compression of large amounts of information [4]. The method is essentially non-linear analysis,
which will be compensate for linear analysis such as the principal component analysis. As an example, we
analyzed a kinase-ligand complex, PDB ID: 2X8E, FMODB ID: XYQ2X). The amino acids and the ligand aredecomposed into strongly correlated clusters in a non-linear fashion. A part of this research was done inactivities of the FMO drug design consortium (FMODD).

124
156

261

36
96
35

227
160

254

177
99

162
67
66 12

28
18

257 209

13

149

125
139

-20
127

105

52

115

111

142

198

114

116
86

74

31

205

15

20

207
218

56
157

166

123

61

60

40

41

137

182
179

153

194
180

165

178

242
39

189
101

184

196

192

185

175
79

191
-20

181
188

119

163

206

216

195

200

199
51

224

47

58

255

259
42

2

33
176

135

71

87

236

3

19
140

251
213

187

44

20
214

239

10

144

65

220

248

70
141

43

9

54

85

8
130

34

38

129
253 14151249

186 190
193
183

48
-10
49

159

155
133

6
122

11

95

82

84

73
164

37

50
23

243

81

32

252

22

63

169

24
25
80

244

208

76
17

223

172
78

75

211 10

126

7

222
92
88

83

256

219

173

77
245

201

210

262

93

90

204
59

53

64

240
94

-10

258

138

57

16
132

246

4
29

69

233
131

120

148

150

128

26

230
229

152

108

232

235

154
107

10

147

221

174

237

228

113

112
143

226

234

89
118

103

202

72

21

46
97

109
197

168
238

170
134

117

136

215
1

110

104

102
106

62

55

212

250

100

45
247

30

171

260

145

91 5

158

20

241
98

68

27
203
217

161

167

225

146

The result of embedding the IFIE matrix in two dimensions (left) and three dimensions (right) using nonmetric multidimensional scaling. The number from 1 to 261 represent the amino acid which form kinase andthe ligand is 262.
[1] K. Kitaura et.al., Chem. Phys. Lett. 313, 701, (1999).
[2] http://drugdesign.riken.jp/FMODB/[3] C. Watanabe, et.al., Chem-Bio Informatics Journal, 19, 5, (2019)[4]Modern Multidimensional Scaling, Springer, New York. EEG (2000)

P1-16Prediction of Inter-Fragment Interaction Energies in Janus Kinase
by Neural Network with Geometrical Information on Ligand and Residues
Shusuke Tokutomi 1

Takahiro Hirata 1

tokutomi@eniac.scitec.kobe-u.ac.jp

thirata@eniac.scitec.kobe-u.ac.jp

Kohei Shimamura 1

Kaori Fukuzawa 2

Shigenori Tanaka 1

shimamura@port.kobe-u.ac.jp k-fukuzawa@hoshi.ac.jp tanaka2@kobe -u.ac.jp

1

Graduate School of System Informatics, Kobe University, 1 -1 Rokkodai, Nada-ku, Kobe
657-8501, Japan2
Department of Physical Chemistry, School of Pharmacy and Pharmaceutical Sciences, Hoshi
University, 2-4-41 Ebara, Shinagawa, Tokyo 142-8501, Japan

Keywords: Janus kinase; Ligand binding; Fragment Molecular Orbital (FMO) method;
Inter-Fragment Interaction Energy (IFIE); Machine learning
Inter-Fragment Interaction Energy (IFIE) obtained by Fragment Molecular Orbital (FMO)
method can quantitatively measure the strength of interaction between ligand and a residue inprotein, which is therefore useful for drug screening. However, it has not been clarified so far
whether IFIE can be predicted using only geometrical (atomic distance) information in biopolymer
complexes without resort to explicit FMO calculations.
In this research we have tested a hypothesis that the ligand-residue IFIE can be predicted byusing a neural network model with the atomic distances between ligand and the surrounding 50
residues as the feature quantities (descriptors) in Janus kinase (JAK). We have used a
JAK1-Tofacitinib complex (PDB code: 3EYG).
If this issue can be elucidated, we can expect the following three gains:- To clarify what information is important for IFIE between ligand and residue in JAK.
- To obtain IFIE for various structures without FMO calculations .
- To anticipate that it is possible to construct high-precision molecular force field using FMO.
This research was conducted according to the following procedure:
1. Execution of Molecular Dynamics simulation to obtain 150 structures used for data set .
2. Execution of FMO calculation to obtain IFIE which is the target variable in this research.
3. Acquisition from 150 structures of distance information between ligand and residue atoms usedas feature values.
4. Construction and evaluation of neural network model with MLP Regressor in Scikit-Learn.
In 3 and 4 above, by repeating trial and error, the optimal parameters were searched. This was
performed for each 50 residues. The model was evaluated by dividing the training data set and test
data set to 7:3 and using the value of the coefficient of determination (R 2 ).
The results were categorized into three types: residues that could be learned, those that wereoverlearned, and those that were not learned. The R 2 value for the residues that could be learned
was as high as about 0.8 in the test data set. The fact that there are residues that can be welllearned should show an ability of the present approach to improve the results for residues that are
overlearned or unlearned.
In the poster presentation, we will discuss these three types and how to improve the accuracyof prediction for overlearned and unlearned cases.

P1-17
Specific interactions between retinoic acidreceptor-related orphan receptor and its ligands: molecular
dynamics and ab initio fragment molecular orbital calculations
Shusuke Suzuki 1

Syunya Nakamura 1

Ryosuke Saito 1

s_suzuki@klab.cs.tut.ac.jp

nakamura@klab.cs.tut.ac.jp

saito@klab.cs.tut.ac.jp

Kentaro Kawai 2Midori Takimoto-Kamimura 3

Noriyuki Kurita 1

kentaro.kawai@pharm.setsunan.ac.jp

m.kamimura@teijin.co.jp

kurita@cs.tut.ac.jp

1 Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
2 Faculty of Pharmaceutical Sciences, Setsunan University
45-1, Nagaotoge-cho, Hirakata, Osaka, 573-0101, Japan
3 Teijin Institute for Bio-Medical Research, Teijin Pharma Ltd.,
4-3-2 Asahigaoka, Hino, Tokyo, 191-8512, Japan

Keywords: Molecular simulation; Molecular dynamics; Fragment molecular orbital;
Protein ligand interactions; Conformational change; ROR
Retinoic acid receptor-related orphan receptor gamma (RORγT) is an important transcription
factor involved in the differentiation of T helper cell 17 (Th17) and the production of theinflammatory cytokine, interleukin 17 (IL -17). Since IL-17 is considered to be a cause of
inflammatory diseases, RORγT has drawn much attentions as an important drug target for thetreatments of these diseases [1], and many therapeutic agents have been developed as a potent
inhibitor against RORγT. A recent experimental study [2] revealed that minor structural change totertiary sulfonamide RORγT ligands leads to opposite mechanism of action as agonist or
inverse-agonist to RORγT. However, the reason for this significant difference in the actions of two
similar RORγT ligands is not clarified yet.
In the present study, to elucidate the effect of ligand binding on the structure and conformationof helix 12 (H12) of RORγT, we performed molecular dynamics (MD) simulations and f ragment
molecular orbital (FMO) calculations for some RORγT+ligand complexes. As the target ligands,
we employed agonist 3SN and inverse-agonist 3SX; 3SN enhances the RORγT activity, while 3SX
suppresses the activity by controlling the conformation of H12 existing at the terminal of theligand binding domain of RORγT.
At first, we optimized the structures of the comple xes in explicit waters by molecularmechanics (MM) calculations and investigated the interactions between RORγT residues and theligand by ab initio FMO calculations. The FMO results show that the interactions between RORγT
and the ligands are similar to each other. Therefore, the reason for the opposite mechanism ofactions of 3SX and 3SN cannot be explained by the molecular simulations based on MM and FMO
methods.
To search widely for the stable conformation of H12 depending on t he ligand bound to RORγT,
we conducted MD simulations for the complexes in explicit waters . In addition, somecharacteristic structures were picked up from the MD snapshots, and the specific interactions
between RORγT residues and the ligands were investigated by ab initio FMO calculations. The
results of MD and FMO calculations will be shown in our poster.
[1]

[2]

A. M. Jetten, S. Kurebayashi, and E. Ueda, “The ROR nucl ear orphan receptor subfamily:
critical regulators of multiple biological processes.,” Prog. Nucleic Acid Res. Mol. Biol., vol. 69,
pp. 205–47, 2001.
O. René et al., “Minor Structural Change to Tertiary Sulfonamide RORc Ligands Led to
Opposite Mechanisms of Action,” ACS Med. Chem. Lett., vol. 6, no. 3, pp. 276–281, Mar. 2015.

P1-18

Validation of conformer generation in solution using
NMR data.
Paul C. D. Hawkins 1

Stanislaw Wlodek 1

phawkins@eyesopen.com

stan@eyesopen.com

Jan Kihlberg 2

Mate Erdelyi 2

jan.kihlberg@kemi.uu.se

1
2

mate.erdelyi@kemi.uu.se

OpenEye Scientific, 9 Bisbee Court, Santa Fe, NM 87501, USA.
Department of Chemistry – BMC, University of Uppsala, Uppsala, SE-752 37, Sweden.

Keywords: Conformation generation, solution conformations, validation, NMR.
The qualit y of models for predi cti on of mol ec ula r pr operties i n sol uti on rele va nt t o dr ug
discovery is criti cally dependent on t he qua lit y of t he confor ma ti ons use d t o bui ld t hos emodels. Unfort unatel y confor mati on ge nera tors ha ve, hist orica ll y, bee n va lida te d a ga i nst
confor mati ons found i n t he s oli d state a nd thei r perfor ma nc e i n s ol uti on ha s si mpl y be e nassumed t o be t he sa me as it is for the s oli d sta te .
We have t herefore bui lt a fra mework t o va lida te a nd co mpa re c onfor ma ti on ge nera tors us i ng
sol uti on NMR data. This fra mework c omprises i nput NMR c onfor ma ti ona l da ta (NOEs a n dscalar coupli ngs), a confor mati on ense mbl e genera tor a nd a met hod to i de nti fy thos econfor mati ons fr om t he ense mb le t ha t best ma tc h t he NMR d a ta a s we ll a s theirprobabiliti es.
Part of t his fra mewor k is the new t ool NMR_FIT, whi c h is a user -frie ndl y a nd fa sterrei mpl ementati on of t he well -establishe d NAM FIS me t hod. 1 We ha ve va li da te d NMR_FI T
a gainst t he reference i mple mentati on on a va riet y of mole c ules, a nd us ed it t o c ompa r e
di fferent met hods of confor mati on ge nera ti on i n s ol uti on.

[1] Cicero, D. O.; Barbato, G.; Bazzo, R., NMR Analysis of Molecular Flexibility in Solution:
A New Method for the Study of Complex Distributions of Rapidly Exchanging
Conformations. Application to a 13-Residue Peptide with an 8-Residue Loop. Journal of
the American Chemical Society 1995, 117, 1027-1033.

P1-19
Molecular simulations on aggregation mechanismof microtubule associated Tau proteins
Riku Sato 1

sato@klab.cs.tut.ac.jp

Takuya Ezawa 1

ezawa@klab.cs.tut.ac.jp

Sergiy Shulga 2
Shulga5@i.ua

12

Katsumi Suzuki 1

k-suzuki@klab.cs.tut.ac.jp

Shusuke Suzuki 1

s_suzuki@klab.cs.tut.ac.jp

Yaroslav Blume 2

blume@nas.gov.ua

Karpov Pavel 2

karpov@nas.gov.ua

Noriyuki Kurita 1

kurita@cs.tut.ac.jp

Department of Computer Science and Engineering, Toyohashi University of Technology,
1−1 Hibarigaoka, Tempaku−cho, Toyohashi, Aichi, 441−8580, Japan
Institute for Food Biotechnology and Genomics, National Academy of Sciences of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Alzheimer’s disease, Microtubule associated protein, Tau protein, Paired helical filament,
Aggregation, Molecular simulation, Molecular dynamics, Fragment molecular orbital

Alzheimer's disease (AD), a dementia characterized by memory impairment, disorientationand loss of judgment occur, has been a serious problem worldwide. There are various hypotheses
about the cause of AD. In the tau hypothesis, the aggregation and accumulation of
hyper-phosphorylated microtubule associated protein Tau (MAPT) is considered to be related withthe onset of ADs. MAPT originally plays an important role in regulating microtubule stabilization.
However, when it is hyper-phosphorylated, it aggregates to form an insoluble fiber called as apaired helical filament (PHF). Therefore, the elucidations of the MAPT aggregation mechanism aswell as the aggregation properties of PHFs are expected to be helpful in the development of novel
therapeutic agents for inhibiting the onset of ADs.
Since MAPT is an intrinsically disordered protein, it is difficult to determine its 3D structureby X-ray crystallography. However, recently, the 3D structure of PHF was identified bycryo-electron microscopy and registered in the Protein Data Bank (PDB ID: 6HRE)[1]. The
structure is a hexamer of MAPT, in which three pairs of C-shaped MAPTs are stacked.
In the present study, we first carried out 100 ns molecular dynamics (MD) simulations at 300
K for the hexamer in an explicit water box, in order to investigate the stability of hexamer in water.
In addition, we picked out some characteristic structures of the hexamer to investigate theirelectronic states using ab initio fragment molecular orbital (FMO) calculations. The same analyses
were conducted for MAPT monomer, dimer paired in the direction perpendicular to helical axis
and trimer stacked in the helical direction.
The MD simulation for the MAPT hexamer indicates that the stacked trimer keeps its structure
and conformation, while the MD simulation for the MAPT monomer elucidates that some β sheet
structures in MAPT were easily broken. Accordingly, it is suggested that C-shaped MAPT in the
structure of PHF cannot be formed in the monomer state. The details of structural changes ofmonomer, dimer, trimer and hexamer will be shown in the poste r. In addition, the change in theinteractions between MAPT residues in these structures will be discussed.
[1] B. Falcon, et al., Acta Neuropathologica, 136: 699-708, 2018.

P1-20

Selectivity of phosphodiesterase-10A inhibitor forphosphodiesterase family elucidated by free energy
perturbation approach
Toru Ekimoto 1

ekimoto@yokohama-cu.ac.jp

Tsutomu Yamane 1

tyamane@yokohama-cu.ac.jp

12

Chisa Yuasa 1

i130699c@yokohama-cu.ac.jp

Mitsunori Ikeguchi 1,2

ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, JapanRIKEN Medical Sciences Innovation Hub Program, 1-7-29 Suehiro-cho, Tsurumi-ku,
Yokohama 230-0045, Japan

Keywords: Molecular dynamics simulation, Binding free energy, Free energy perturbation,
Phosphodiesterase
Tuning of binding selectivity to isoform proteins is one of the important issues in the drug design.
Phosphodiesterase (PDE) hydrolyzes cyclic-AMP and cyclic-GMP, which play important roles in signaltransductions as second messengers, and there are 11 isoforms in human. Because each isoform
involves specific signal transductions, it is required for the drug design of PDE to improve not onlybinding affinity to the targeted PDE but also selectivity for other isoforms. In PDE10A, the selectivity
pocket [1] is well known as an important area to generate selectivity, and a lot of drug candidates were
designed so that the selectivity pocket is filled with a part of the ligand [2]. However, TAK-063
designed by Takeda pharmaceutical Co., Ltd. exhibits high selectivity despite the fact that TAK-063
does not bind to the selectivity pocket [3], suggesting other structural factors except for the selectivity
pocket.
To understand structural factors for the selectivity, we calculated the binding-free-energy changes
upon single mutations of residues at the binding site using free energy perturbation (FEP) exhaustively.
By sequence alignment of 11 isoforms, residues with different sequences among PDE10A and isoforms
were detected (28 residues in total). At each residue, FEP calculations of the single mutation were
executed by the number of different sequences among PDE10A and other isoforms (85 mutations in
total). From the FEP analyses, key residues generating the selectivity were detected successfully, and a
combination of residues contributing to the selectivity for each isoform was specified. In addition, thedetected residues could be classified in terms of structural features and the interaction manner with
TAK-063.
[1] Verhoest, R.P. et al., J. Med. Chem., 52:5188-5196, 2009.
[2] Chappie, A.T., J. Med. Chem., 55:7299-7331, 2012.
[3] Kunimoto, J. et al., J. Med. Chem., 57:9627-9643, 2014.

P1-21De Novo Binding Prediction using gREST

1

2

3

Suyong Re 2

Ai Niitsu 1

Hiraku Oshima 2suyongre@riken.jp

ai.niitsu@riken.jp

hiraku.oshima@riken.jp

Motoshi Kamiya 3

Yuji Sugita 1,2, 3

motoshi.kamiya@riken.jp

sugita@riken.jp

Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Hirosawa
2-1, Wako, Saitama 351-0198, Japan
Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics
Research, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
Computational Biophysics Research Team, RIKEN Center for Computational Science, 6-7-1
Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan

S. Re and A. Niitsu are equally contributed to this work.

Keywords: Protein-ligand binding, Molecular dynamics simulation, generalized replica-exchangewith solute tempering
Molecular Dynamics (MD) simulations treat molecular interactions and flexibility of aprotein-ligand system at atomic resolution, and are increasingly applied to predict protein-ligand
recognition. However, de novo prediction of protein-ligand recognition presents a significant
challenge due to the timescale gap between MD simulations and actual protein-ligand bindings.
Conventional simulations can observe only a few binding events even using MD-specialized
supercomputers. In this work, we apply the generalized replica-exchange with solute tempering
method, gREST [1], combined with a flat-bottom potential to overcome the difficulty. gREST is a
generalization of REST [2] and provides a versatile framework for defining the solute. Forprotein-ligand interactions, the solute region is defined in terms of selected energy components of
the ligand and selected protein binding site residues to enhance protein structural flexibility and
expedite dynamics relevant to binding. The application of a flat-bottom potential centered on thecavity can further enhances the sampling of binding events by avoiding the irrelevant encounters.
We apply the method to the ligand binding with T4 lysozyme Leu99Ala mutant [3]. The method
samples bindings in multiple poses and correctly predicts X-ray structures. Based on free-energy
profiles, we are able to predict whether the molecule binds to the target protein or not. Furthermore,a coupling of the bindings with protein conformations are clearly illustrated. The protocol is
generally applicable to various proteins having buried cavities with limited access for ligands withdifferent shapes, sizes and chemical properties.
[1] Kamiya, M.; Sugita, Y. J. Chem. Phys. 2018, 149, 072304.
[2] Wang, L. et al. J. Phys. Chem. B 2011, 115, 9431–9438. Moors, S. L. C. et al. J. Chem. Theory
Comput. 2011, 7, 231–237. Terakawa, T. et al. J. Comput. Chem. 2011, 32, 1228–1234.
[3] Niitsu, A. et al. submitted.

P1-22A binding free energy calculation method along a modified
thermodynamic path which avoids exhaustive enumerationof multiple protein-ligand poses

1

2

3

4

Yoshitake Sakae 1

Bin W. Zhang 2sakae@rist.or.jp

bin.w.zhang@temple.edu

Ronald M. Levy 2,3Nanjie Deng 4

ronlevy@temple.edu

nanjie.deng@gmail.com

Division of Computational Science and Technology, Research Organization for Information
Science and Technology, 1-18-16
Hamamatsucho, Minato-ku, Tokyo 105-0013, Japan
Center for Biophysics and Computational Biology, Temple University
Philadelphia, Pennsylvania 19122, United States
Department of Chemistry and Institute for Computational Molecular Science, Temple
University
Philadelphia, Pennsylvania 19122, United States
Department of Chemistry and Physical Sciences, Pace University
New York, NY 10038, United States

Keywords: Binding free energy, Ligand docking, Double decoupling, Molecular simulation, DrugdesignWe propose a free energy calculation method for       
receptor-ligand binding, which have multiple binding
poses that avoid exhaustive enumeration of the poses. 
For systems with multiple binding poses, the standard
procedure is to enumerate orientations of the binding
poses, restrain the ligand to each orientation, and then,      
calculate the binding free energies for each binding pose.          
In this study, we modify a part of the thermodynamic  
cycle in order to sample a broader conformational space    of the ligand in the binding site. This modification leads   
to the more accurate free energy calculation without
performing separate free energy simulations for eachbinding pose.
    
We applied our modification to several host-guest     
systems as tests. One is β-cyclodextrin and ligands,
which contain the carbonyl group. Another is a F i g u r e : Procedures of alchemical binding
protein-ligand system, phenol binding to T4 lysozyme.
The results showed that the binding free energies free energy calculation methods ofobtained from our method without knowing the binding
conventional (left) and our method (right).
poses were in good agreement with the benchmark
results obtained by explicit enumeration of the binding poses.
[1] Sakae, Y., Zhang, B. W., Levy, R. M., and Deng, N., Journal of Computational Chemistry,
submitted.

P1-23

Improvement of Carbohydrate Force Field
for Molecular Dynamics
Makoto Ikejo1

Hirofumi Watanabe2

ikejo@eniac.scitec.kobe-u.ac.jp

hirofumi.watanabe@port.kobe-u.ac.jp

Shigenori Tanaka1
tanaka2@kobe-u.ac.jp
1

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada, Kobe 657-8501, Japan
2
Education Center on Computational Science and Engineering, Kobe University,
7-1-48, Minatojimaminamimachi, Chuo-ku, Kobe 650-0047, Japan

Keywords: Force field, Glycan, Parameter fitting, Machine learning
Although sugar chains have been actively studied in recent years as the “third” biological molecule,details of their structures and functions have not been elucidated yet. Molecular dynamics (MD)simulation is a useful tool to clarify the relationship between structure and function of biomolecules,
and force field for carbohydrate called GLYCAM [1] is introduced in MD calculation software AMBER.
In GLYCAM force field, force field parameters are determined to reproduce the energy variation ofquantum mechanics (QM) calculation. However, when comparing the relative energies of QM andMM calculations, it is observed that deviations of about 4 kcal/mol have occurred. In this study, inorder to better reproduce the energy of QM calculation (DFT / 6-31 ++G (2d, 2p)) for -D-Glucose
molecule in vacuum, the atomic charge and dihedral angle parameters were newly fitted using amachine learning library called TensorFlow [2] (see Figure).

Target moleculeComparison of ΔΔE before and after parameter fitting[1] Kirschner, K. N., et al., GLYCAM06: ageneralizable biomolecular force field.

Carbohydrates, J. Comput. Chem., 29(4):622-55, 2008.
[2] M Abadi, et al., Tensorflow: A system for large-scale machine learning, 12th USENIX
Symposium on OSDI’16, 2016.

P1-24

Systematic construction of the cosolvents sets forcosolvent MD (CMD) with the large-scale computation
Keisuke Yanagisawa 1

yanagisawa@bi.a.u-tokyo.ac.jp

Tohru Terada 2 3

tterada@iu.a.u-tokyo.ac.jp

1
2
3

Yoshitaka Moriwaki 1

moriwaki@bi.a.u-tokyo.ac.jp

Kentaro Shimizu 1 2

shimizu@bi.a.u-tokyo.ac.jp

Department of Biotechnology, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Interfaculty Initiative in Information Studies, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan

Keywords: cosolvent MD, probability map, structural similarity, large-scale computation
Cosolvent MD (CMD) is an MD simulation of a protein in explicit water molecules mixed
with cosolvent molecules. The simulation has been used for hotspot detection [1,2], bindingsite identification [3] and binding energy estimation [3,4]. Existing methods utilize small
molecules which represent functional groups of compounds, such as isopropanol andbenzene. Mahmoud et al. demonstrated that the spatial probability distribution map ofcosolvent atoms (Pmap) was highly dependent on its chemical context and suggested that awider variety of cosolvent molecules should be used in CMD [4]; however, the number ofcosolvents is practically limited by the computational resources available. For efficient useof the resources, we need a non-redundant set of the cosolvent molecules that covers as
wide a range of chemical space as possible.
In the present study, we extracted typical functional groups from FDA approved drugs andgenerated >100 cosolvent structures . For each cosolvent molecule, we conducted 10-ns
CMD simulations 20 times to generate a Pmap. After that, Pmap similarity-basedhierarchical clustering was done to analyze the relationship between the cosolvent
structure and the Pmap. We found that cosolvents having similar structures tended to beclustered into the same cluster, suggesting that Pmap similarity could be predicted fromstructural similarities without time-consuming simulations. We here present a method toconstruct the optimum set of cosolvent molecules for given computational resources using
the result of the hierarchical clustering.
[1] Ghanakota P et al., Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots
at Protein−Protein Interaction Interfaces, J. Chem. Inf. Model. 58:784−793, 2018.
[2] Ung PMU et al., Identifying Binding Hot Spots on Protein Surfaces by Mixed-Solvent
Molecular Dynamics: HIV-1 Protease as a Test Case, Biopolymers 105:21−34, 2016.
[3] Ustach VD et al., Optimization and Evaluation of Site-Identification by Ligand Competitive
Saturation (SILCS) as a Tool for Target-Based Ligand Optimization, J. Chem. Inf. Model.
59:3018−3035, 2019.
[4] Mahmoud AH et al., Improving Atom-Type Diversity and Sampling in Cosolvent Simulations
Using λ-Dynamics, J. Chem. Theory Comput. 15:3272−3287, 2019.

P1-25

A Method for Comparing Structural Ensembles:
Applications to Molecular Dynamics Trajectory Data
Takashi Amisaki 1
amisaki@tottori-u.ac.jp
1

Department of Biological Regulation, Faculty of Medicine, Totto ri University,
86 Nishi-cho, Yonago, Tottori 683-8503, Japan

Keywords: Structural superposition, Random effects model, L1 regu larization
Molecular dynamics simulations are often used to discover the outcomes of adding some
perturbations to a protein molecular system. The perturbations include, for example, amino acidmutations and variations on ligands. The impacts of the perturbations may be investigated by
two-stage superposition of conformers in the ensembles (trajectories). Namely, conformers are
first superimposed within each ensemble and then the average structures of the ensembles aresuperimposed. Deviations seen on thus superimposed structures may imply differences due to theperturbations. On the other hand, the method proposed in this work simultaneously superimposes
the all conformers using a random effects model (REM) distinguishing between inter- andintra-ensemble variabilities. In addition, distinctions between ensembles can be highlighted bymeans of L1 regularization on inter-ensemble variability. The geometrical parameters (e.g.,
rotation matrices) and variance components are jointly estimated/shrunken. In other words, themethod seeks portions for intra-ensemble variability while adjusting the orientations of theconformers. If this regularization is turned off and the sizes of ensembles are large enough, thenthe estimates by the random effects model coincide to those by the two-stage method.
The proposed method was applied to twenty trajectories, each 42ns long, of hMTH1 which is an
enzyme that sanitizes nucleotide pools. The trajectories were obtained by twenty simulations
under different ligand-binding statuses and/or different protonation states of Asp119 and Asp120.
The two aspartate residues have been reported to relate to the recognition of ligand molecules.
Two results are shown in the figure. The left panel shows the REM superposition of the twenty
ensembles (42 conformer each) without the L1 regularization on inter-ensemble variability. The
thin lines are the ensemble averages. The thick line represents the overall average structure.
Large inter-ensemble variability can be seen at the loops as well as at the helices. By contrast, inthe right panel, which is the result under the regularization with a certain strength, large variability
was limited mainly to the loops. The results exemplified the effectiveness of the proposed method.

P1-26

Fast prediction of binding hotspots on a peptide ligand incomplex by Digital Annealer
Yoshiaki Tanida 1
tanida.yoshiaki@fujitsu.com

Kentaro Takai 2takai.kentaro@fujitsu.com

Sakiko Mori 2
mori.sakiko@fujitsu.com

Hiroyuki Sato 1

Mutsuyo Wada 2

Atsushi Tomonaga

Azuma Matsuura 1

Akihiko Ueda 2

Seiji Fukui 2

Nozomu Kamiya 2

Shunji Matsumoto 2

Hirotaka Oshima 1

1

2

Fujitsu Laboratories Ltd., Atsugi, Kanagawa 243 -0197, Japan
Fujitsu Ltd., 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan

Keywords: ligand-receptor complex, cyclic peptide, binding hotspot, combinatorial optimization
problem, digital annealer

In middle molecule drug discovery, understanding peptide-protein interactions are of paramountimportance for computer-based de novo design. Residue properties in complex are particularly key
in binding affinity of a peptide to a target protein. Thus, it is necessary to calculate the interactions
between residues and protein to understand their binding features and find out which regions
should be modified to improve the affinity. However, calculating the interaction between middle
molecules and proteins from the first principle s is very time-consuming. To solve this problem, we
assume that if the peptide molecule in the complex is bound in its natural form, it gives strong
binding [1, 2].
In this report, we propose a simple method to fast search for hotspots in peptide-protein binding
based on a packing optimization [3, 4]. Furthermore, our method can be easily accelerated by
special purpose hardware, so called “digital annealer”.

[1] M-H Hao, O. Haq and I. Muegge, J. Chem. Inf. Model., 47, 2242 (2007).
[2] K. A. Brameld, † B. Kuhn, ‡ D. C. Reuter and M. Stahl, J. Chem. Inf. Model, 48, 1 (2008)[3] C. Lee and S. Subbiah, J. Mol. Biol., 217, 373 (1991).
[4] R. Tanimura, A. Kidera and H. Nakamura, Protein Science, 3, 2358 (1994).

P1-27

Advanced methods to predict the property of cyclic peptides:
reproduction of actual conformations
Sakiko Mori 1
mori.sakiko@fujitsu.com

Atsushi Tomonaga
Mutsuyo Wada 1
1
2

Kentaro Takai 1
takai.kentaro@fujitsu.com

Hiroyuki Sato 2

Akihiko Ueda 1

Yoshiaki Tanida 2
tanida.yoshiaki@fujitsu.com

Azuma Matsuura 2

Seiji Fukui 1

Shunji Matsumoto 1

Hirotaka Oshima 2
Nozomu Kamiya 1

Fujitsu Ltd, 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan
Fujitsu Laboratories Ltd., Atsugi, Kanagawa 243 -0197, Japan

Keywords: conformation search, cyclic peptides, Digital Annealer
Drug discovery in middle sized molecules such as cyclic peptides is an attracting area for their
particular properties against small molecules or antibodies. It is important to examine the conformation
related to properties of cyclic peptides for the discovery [1]. As peptides have high internal degree of
freedom compared to small molecules, the systematic search needs extensive calculations. Therefore,
an exhaustive and efficient conformation analysis is required.
Our developed conformation analysis methods consist of three steps.
(i)
Generation of cyclic glycine conformers by our “Ring Expansion Method” [2].
(ii) Substitution of an alanine or a proline residue for a glycine residue and addition of the methylgroup to –NH of amide bonds in a way corresponding to a target cyclic peptide (called themain chain conformer).
(iii) Sequential addition of side chain conformers to the main chain conformer.
To check the accuracy of our system, we
performed the conformation analysis for the
7-mer peptide (DQSEPHP) measured by NMR
in water (PDB entry 6BEW), and got stableconformers contained similar one to the NMR
structure. The lowest RMSD between PDB
and calculated conformers is 1.42Å under the
dielectric constant (DC) set to 80 (theexperimental condition).
In the case of the larger peptides, Gramicidin
S (PDB entry 1TK2) and Cyclosporin A (PDB
entry 1CWA), the lowest RMSD are 1.48Å
and 0.67Å (Fig. 1), respectively. Here, we set
the DC to 4 since these PDB structures were
measured in the complex states with proteins.
Striving for 15-mer peptides, we are energetically developing our technique applying the special
purpose hardware, “Digital Annealer”.
[1] A. Whitty, M. Zhong, L. Viarengo, D. Beglov, D. R. Hall and S. Vajda, Drug Discovery Today, 21,712 (2016).
[2] A. Tomonaga, H. Sugiyama and A. Ueda, WO2018/154789A1.

P1-28Advanced methods to predict the property of cyclic peptides:
exhaustive and efficient conformation search
Kentaro Takai 1

Sakiko Mori 1

takai.kentaro@fujitsu.com

mori.sakiko@fujitsu.com

Atsushi Tomonaga
Mutsuyo Wada 1

Hiroyuki Sato 2

Akihiko Ueda 1

Seiji Fukui 1

Yoshiaki Tanida 2
tanida.yoshiaki@fujitsu.com

Azuma Matsuura 2

Hirotaka Oshima 2

Shunji Matsumoto 1

1

Fujitsu Ltd, 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan

2

Fujitsu Laboratories Ltd., Atsugi, Kanagawa 243-0197, Japan

Nozomu Kamiya 1

Keywords: conformation search, cyclic peptides, database, Digital Annealer
Drug discovery in middle sized molecules such as cyclic peptides is an attracting area for their
particular properties against small molecules or antibodies. As it is known that the properties of acyclic peptide depend on its conformation[1], we developed a new “Ring Expansion Method”[2]for the generation of cyclic glycine conformers and a sequential addition method of side chain
conformers using their databases (patent pending) to perform the exhaustive but practicalconformation analysis.
Our new conformation analysis methods consist of three steps.
(i) Generation of cyclic glycine conformers by “Ring Expansion Method” and creation of
their conformer databases. They are used in step (ii) and also reused for new targets.
(ii) Substitution of an alanine or a proline residue for a glycine residue and addition of themethyl group to –NH of amide bonds in a way corresponding to a target cyclic peptide(called the main chain conformer).
(iii) Sequential addition of side chain conformers to the main chain conformer using thedatabases of side chain conformers calculated in advance.
In step (i), we searched conformers of a cyclic 11-mer glycine to analyze the conformation of
Cyclosporin A (a 11-mer peptide). Using our method, the calculation time is drastically reduced
from 380 years (estimated) of the existing systematic search to 56 days (actual). While thesystematic search is exhaustive for even high-energy conformers not contributing to properties tobe measured, our search is exhaustive for only lower-energy ones.
In step (ii) and (iii), we got stable 11,472 conformers of Cyclosporin A in 4.6 days using 400
parallel calculations and the databases of cyclic glycine conformers and side chain ones. The latterdatabases help us to search stable conformers efficiently; the number of conformers generated by
the systematic search is 1.1 billion, but it is reduced to less than 1.4 million for Cyclosporin A.We
calculated the RMSD to PDB (1CWA) to confirm the validity of our methods and we found thelowest RMSD is 0.67Å.
Aiming to accelerate the conformation search and realize the analysis for cyclic 15-mer peptides,
we are energetically developing our technique applying the packing optimization method[3,4] andthe special purpose hardware, “Digital Annealer”.
[1] A. Whitty, M. Zhong, L. Viarengo, D. Beglov, D. R. Hall and S. Vajda, Drug Discovery Today,
21, 712 (2016).
[2] A. Tomonaga, H. Sugiyama and A. Ueda, WO2018/154789A1.
[3] C. Lee and S. Subbiah, J. Mol. Biol., 217, 373 (1991).
[4] R. Tanimura, A. Kidera and H. Nakamura, Protein Science, 3, 2358 (1994).

P1-29Conformational Changes and Interactions of Calcium Ion Signal
Transfer Protein Calmodulin and Calmodulin-binding Domain by
Multi-scale and Docking Simulation
Hiromitsu Shimoyama 1
shimoyamah@pharm.kitasato-u.ac.jp

1
2

Yasushige Yonezawa 2
yonezawa-wk@waka.kindai.ac.jp

School of Pharmacy, Kitasato University, Shirokane 5-9-1, Minato-ku, Tokyo 108-8641, Japan
Institute of Advanced Technology, Kindai University, Wakayama 649-6493, Japan

Keywords: signal transduction, conformational change, molecular dynamics, rigid-body docking
Calmodulin (CaM) is a major calcium ion signal transfer protein in many cellular processes. CaM
undergoes large conformational change upon calcium ion binding. The conformational change isnecessary to form complex with its target protein’s binding domain (CaMBD; about 20-residue
long peptide). However, the process of conformational change and complex forming are still not
well understood.
Recently I analyzed various structures of CaM and clarified interactions driving theconformational change [1]. In this work, mechanism of CaM’s conformational change and binding
to CaMBD is studied by combined multi-scale molecular dynamics and rigid-body docking. Our
method would be able to analyze general protein-protein interactions involved in most cellularsignal transductions.
Most cellular signal transductions are protein-protein binding/dissociations. Such phenomena are
difficult to treat by molecular dynamics (MD) because binding/dissociations in cellularenvironment would be repeatedly try-and-error processes.
For such phenomena, a rigid body docking is significantly useful because the docking can obtaincomplex structures in significantly short time compared with MD. Though the docking is useful,protein’s flexibility should be considered. Then, we tried to combine the docking and MD torealize the high performance complex-forming simulation method that considers the flexibility.
First, both protein’s conformations are coarsely sampled by coarse-grained model (CGM). After
that, CGM structures are reconstructed at atomistic resolution (AAM) by Homology modeling.
Then, all-to-all AAM docking of CaM and CaMBD are performed. The flexibility of protein are
considered by CGM-MD and translated to AAM, whereas the docking reduces computational costof binding/dissociation try-and-error.
The(a)structure
theplanehighest docking score is shown in Fig. (b)1 right
Score onofd-θ (left structure(c)  is the native
structure PDB ID 4Q5U). As can be seen from the figure,
CaMBD is bound in the similar position of native
 complex.
θFurther MD starting at the right structure would efficiently
sample conformations around the native complex and help
understanding the binding process.

θdFig. 1 the native structure (left)
a n d t h e s i m u l a t e d s t r u c t u r e score(right)[1] Shimoyama H., A structural comparison of ‘real’ and‘model’ calmodulin clarified allosteric interactions(b) Journal(c) of
 Biomolecular Structure and Dynamics, 2018.
regulating domain motion,

P1-30Autoencoder-based Analyses of Dynamic Allosteryon Proteins by Regulator Binding
Yuko Tsuchiya 1

yuko.tsuchiya@aist.go.jp

Kei Taneishi 2

kei.taneishi@riken.jp

Yasushige Yonezawa 3

yonezawa-wk@waka.kindai.ac.jp

12
3

Artificial Intelligence Research Center, Advanced Industrial Science and Technology,
2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Center for Advanced Photonics, RIKEN,
2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
High Pressure Protein Research Center, Institute of Advanced Technology, Kindai University,
930 Nishimitani, Kinokawa, Wakayama, 649-6493, Japan

Keywords: Autoencoder,mechanisms of disease

Molecular

dynamics simulation,

Dynamic allostery,

Molecular

Analyses of subtle changes in side-chain dynamic motion, and not large rigid conformationalchanges, are essential to understand the regulation of protein functions. We propose an
autoencoder-based method that can detect dynamic allostery, based on the comparison of protein
fluctuations, using distance matrices obtained from the molecular dynamics simulations inregulator-bound and -unbound forms. The method detected that the changes in dynamics byregulator binding led to the reorganization of correlative fluctuations among residue pairs. Other
correlative motions were also found as a result of dynamic perturbation. Our method would be
usefully applied to the analyses of molecular mechanisms in signal transduction and mutagenesis
systems that are involved in protein functions and severe diseases.
[1] Tsuchiya, Y., Tanekshi, K., Yonezawa, Y., Autoencoder-based detection of dynamic allosterytriggered by ligand binding based on molecular dynamics, Journal of Chemical Information andModeling, in press.

P1-31

Molecular dynamics simulation of drug efflux
in multidrug ABC transporter
Takumi Someya 1
w185419d@yokohama-cu.ac.jp

Akinori Kidera 1
kidera@tsurumi.yokohama-cu.ac.jp

1
2

Kei Moritsugu 1
moritugu@yokohama-cu.ac.jp

Hiroaki Kato 2
katohiro@pharm.kyoto-u.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan
Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida
Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan

Keywords: Multidrug ABC transporter, Drug efflux mechanism, Molecular simulation, Pathconnecting two crystal structures
Multidrug ATP binding cassette (ABC) transporter, ABCB1, is known as one of the mostimportant molecules regulating the ADME properties of drugs. ABCB1 transports a broad
spectrum of lipophilic drugs during a large scale structural change from the inward -open to theoutward-open structure, using ATP hydrolysis as the driving force. To investigate the transportmechanism as well as the chemical property determining transport activity, we performed
molecular dynamics (MD) simulations tracing the drug transport process accompanied with thestructural change, based on the crystal structures of the inward-open and the outward-open formsof CmABCB1 (a eukaryotic homolog of human ABCB1 from Cyanidioschyzon merolae), whichhave recently been solved by Kodan et al. [1,2].
The simulations of the transport process were carried out along paths connecting theinward-open to the outward-open structure, using the targeted MD. During the simulation, theprotein was forced to move to the target structure, while the drug molecule was translocated solely
by the change in protein environment without any influence from the targeting force. Using
Rhodamine 6G as the substrate, we examined how the transport behavior was affected by the initial
position of the drug and the path of the structural change, and then extended the simu lation to thetransport of various drugs with different activity. In the presentation, we will show the analysis
results on the mechanism how the protein squeezes out a drug molecule, focusing on the detailinteractions and solvation, and will compare the transport activities of various drugs.
[1] Kodan, A., Yamaguchi, T., Nakatsu, T., Sakiyama, K., Christopher, J. H., Fujioka, A., Hirokane,
R., Ikeguchi, K., Watanabe, B., Hiratake, J., Kimura, Y., Suga, H., Ueda, K., Kato, H.,
Proc.Natl.Acad.Sci. 111, 4049 (2014).
[2] Kodan, A., Yamaguchi, T., Nakatsu, T., Matsuoka, K., Kimura, Y., Ueda, K., Kato, H., Nature
Commun. 10, 1 (2019).

P1-32

Characterization of water-molecule interaction based on fragment
molecular orbital method
Hiromichi Tsurui 1
tsurui@juntendo.ac.jp

1

Department of Immunological Diagnosis, Juntendo University School of Medicine.
2-1-1, Bunkyo-ku, Tokyo 113-8421, Japan

Keywords: FMO, IFIE, three-body interaction, water-water interaction
Interaction of water molecules are characterized by fragment molecular orbital (FMO)
calculation. Water molecules of 2842 were distributed spherically with the diameter of
54 nm. Clusters of 4, 8, 12, 16 and 20 molecules on lattice points at every 60° longitude,
30° latitude and 4.5 nm, 193 in total containing the center, were calculated for theirinteraction based on FMO2 and FMO3 method. Calculation were performed on
ABINIT-MP implemented on the K computer at Riken. There were a little difference of
the interaction energies, namely 0.5 ~ 0.8%, between the calculation of FMO2 andFMO3, and it gradually increased as the size of cluster increased. The interactionenergies of clusters in absolute value increased almost linearly as the size of cluster
increased. Interaction energies of the same cluster size apparently seemed to increasein absolute value as the radius of the distribution shell increased, however, this finding
requires more intensive analysis. Effect of protein on the interaction of surrounding
water molecules are under analysis, and hopefully can be reported on the meeting.

P1-33

Computational approaches to drug-receptor binding
kinetics
Osamu Ichihara 1
osamu.ichihara@schrodinger.com

1

Schrödinger K.K., 17F Marunouchi Trust Tower North, 1-8-1 Marunouchi, Chiyoda-ku, Tokyo
100-0005, Japan

Keywords: Binding kinetics, Residence time, MD simulation, Association rate, Dissociation rate
It has been proposed by Pearlstein et al. that desolvation and resolvation of the binding sites ofproteins can be a key determinant of ligand binding kinetics. They have shown that, for a set of
inhibitors of p38 MAP kinase, calculated desolvation and resolvation costs during ligand binding
are qualitatively consistent with observed on and off rates respectively. Previously, we extended
the application of their hypothesis further to rationalizing the binding kinetics (residence time) of
the inhibitors of Staphylococcus aureus enoyl-ACP reductase (saFabI), where more complex
two-step binding mechanism is suggested to be in operation. It has been proposed that, on ligand
binding, saFabI may undergo a directional structural change, i.e. ordering of the substrate-binding
loop to form an a-helical structure, and thereby locks the inhibitor into the cavity and increases its
residence time. We calculated a set of thermodynamic parameters associated with the solvation
properties around the ligands bound to saFabI using molecular dynamics simulations (WaterMap).
The observed rate of ligand dissociation (residence time) are shown to be in excellent correlation
with the parameters calculated.

In this work, in order to obtain more insight into the role of water molecules in the ligand
binding event, we have performed a series of unbiased µ-second MD simulations to investigate themovement of the disordered substrate-binding loop, in the presence or absence of a ligand in thebinding site. The MD simulations showed that the disordered loop was folded into the ordered state
only in the presence of the ligand in the simulation time frame of a few hundred ns to µs. The
RMSDs between the X-ray structure of the ordered loop and the structures observed in thesimulations were ~1Å. On the other hand, the disordered loop failed to settle into the well-definedordered state in the absence of the ligand, being consistent with the reported crystallographic
studies. More details of the MD simulations and the role of water molecules will be discussed.

P1-34

Investigation of stabilization mechanism of amorphoussolid dispersion by fragment molecular orbital calculation
Xiaohan Ma 1
ahga4347@chiba-u.jp

Kaori Fukuzawa 2

k-fukuzawa@hoshi.ac.jp
12

Keisuke Ueda 1
keisuke@chiba-u.jp

Etsuo Yonemochi 2

e-yonemochi@hoshi.ac.jp

Kenjirou Higashi 1
ken-h@faculty.chiba-u.jp

Kunikazu Moribe 1

moribe@faculty.chiba-u.jp

Laboratory of Pharmaceutical Technology, Graduate School of Pharmaceutical Sciences, Chiba
University 1-8-1, Inohana, Chuo-ku, Chiba, 260-8675, Japan
Hoshi University, 2-4-41 Ebara, Shinagawa-Ku, Tokyo 142-8501, Japan

Keywords: Amorphous solid dispersion, Molecular dynamics, Fragment molecular orbital
calculation
-Glucosyl rutin (Rutin-G), a new functional food additive, has been proven to improve thedissolution and bioavailability of poorly water-soluble drugs. In our previous study, carbamazepine(CBZ) was used as a model drug. The amorphous solid dispersion (A SD) of CBZ and Rutin-G
effectively stabilized the amorphous state of CBZ and increased its solubility. However, thestabilization mechanism was still unknown. The present study aims to investigate the specificinteraction between CBZ and Rutin-G which contributes to the stabilization of ASD by fragment
molecular orbital (FMO) calculation. An ASD of CBZ and Rutin-G was constructed by molecular
dynamics (MD) simulation following the experimentally melt -quenching method. The similar glasstransition temperatures (T g s) obtained from MD simulation and the experiment indicated that thesimulated ASD was reasonable. In this ASD, hydrogen bonds were formed at around Tg, followedby the π-π interaction during the cooling process. The interaction energy of the ASD was
calculated by FMO calculation. The electrostatic energies dominated the interaction energy
between CBZ and sugar groups of Rutin-G, while the electrostatic and dispersion energies werecomparable in the interaction energy between CBZ and flavone skeleton of Rutin-G. In general,
electrostatic energy is the main components of hydrogen bonding energy, while the CH/π and π -πinteraction energies are predominated by dispersion energy. The total molecular interaction energy
between CBZ and the flavone skeleton of Rutin-G was slightly higher than that between CBZ andthe sugar groups. Therefore, hydrogen bonds as well as π-π interactions mainly existed between
CBZ and the flavone skeleton of Rutin-G in ASD, which could contribute to the stabilization of
ASD. FMO calculation is considered to be a potential tool to interpret the stabilization mechanismof ASD.

P1-35Interaction analysis of HIV protease-inhibitor complex
by fragment molecular orbital method
Hirofumi FUJI 1
1933310128s@kindai.ac.jp12Norihito KAWASITA 1,2
nkawashita@life.kindai.ac.jp

Graduate School of Science and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka
city, Osaka, 577-8502, Japan
Faculty of Science and Engineering, kindai University, 3-4-1 Kowakae, Higashiosaka city,
Osaka, 577-8502, Japan

Keywords: fragment molecular orbital method, inter-fragment interaction energy, enthalpy
Development of protein-protein interaction inhibitors by small molecule inhibitors has become an
attractive drug discovery target. However, it is still difficult to predict binding free energy of novel
inhibitors having strong activity in docking simulatio n. In recent years, the fragment molecularorbital (FMO) method [1] has been used to analyze the interaction between proteins and inhibitors
and the sum of inter-fragment interaction energies (IFIE) of protein-inhibitor obtained by FMO
calculation can evaluate static interactions into consideration the inter-electron interaction. If there
is a high correlation between IFIE and ΔH by FMO calculation, a regression expression between
ΔH and IFIE can be constructed. Compensation law is established for ΔH and TΔS [2] , so that finally
ΔG can be predicted only IFIE. Therefore, it is possible to establish a method that can predict ΔG
without considering entropy.
This study examined the correlation between IFIE and Δ H in the HIV protease inhibitor system.
First, 14 structures of the HIV protease inhibitor complex in the literature [3-5] were downloaded
from the protein databank (PDB), and the structure was corrected by BioStationViewer,
DiscoveryStudioViewer, and MOE. In structural optimization, CHARMm was used as a force field.
Next, the calculation was carried out by MP2/6-31G, using supercomputer K and FX100 and using
ABINIT-MP 6.0+ for software. Finally, using the sum of IFIE obtained by calculation and theexperimental value (ΔH) in the literature [3-5] , it was examined whether a correlation wasestablished between IFIE and ΔH.
Confirming the correlation between IFIE obtained by FMO calculation and the experimental
value, a good correlation of R 2 =0.5326 was obtained. Furthermore, classifying the inhibitors
charge into 0 and +1 and confirming the correlation, a better correlation value of R 2 =0.8071 was
obtained in the positively charged inhibitor complex. When confirming between the inhibitor andthe amino acid residue, there was a difference in IFIE between inhibitor and GLY49 in eachcomplex. When confirmed the structure, there were complexes with and without water mol ecules
between the inhibitor and GLY49, so we classified the complex with water as group 1 and thecomplex without water as group 2. After that, a better correlation of R 2 =0.9754 for group 1 andR 2 =0.7272 for group 2 was obtained. From these results, by calculated the complex at the same
conditions, it could be obtained the accurate correlation between IFIE and ΔH needed for the ΔG
prediction method.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp180147) . This work was supported by
Grant-in-Aid for Scientific Research (C) from JSPS KAKENHI (No. 18K06615),[1] Kitaura K. et al., Chem. Phys. Lett. 312, 319-324, (1999).
[2] John D. C. et al., Annu. Rev. Biophys. 42, 121–142, (2013).
[3] Nancy M. K. et al., ACS Chem. Biol., 7, 1536-1546, (2012).
[4] Milan K. et al., J. Mol. Biol, 374, 1005-1016, (2007).
[5] Milan K. et al., FEBS Journal, 281, 1834-1847, (2014).

P1-36

Assessment of a simple pKa estimation scheme
for drug molecules by quantum chemical calculations
Takao Otsuka 1

Noriaki Okimoto 1

totsuka@riken.jp

okimoto@riken.jp

Makoto Taiji 1
taiji@riken.jp
12Toru Matsui 2
matsui@chem.tsukuba.ac.jp

RIKEN Center for Biosystems Dynamics Research (BDR), Building B, 6-2-4 Furuedai, Suita,
Osaka 565-0874, Japan
Department of Chemistry, Graduate School of Pure and Applied Sciences, University ofTsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan

Keywords: acid dissociation constant, pKa, electronic structure calculation,
Computational prediction of acid dissociation constant, pKa is so important for pharmacokinetics
of drug molecules and/or rational drug design. There are some theoretical studies for evaluating
the pKa, such as empirical parameterized approaches [1], classical or first-principles molecular
dynamics simulations [2,3], and electronic structure calculations [4]. Recently, Matsui andco-workers have proposed a simple pKa evaluation scheme based on quantum chemical
calculations [5]. Although this scheme needs the experimental values, it can give thesemi-quantitative pKa values of specific functional groups. They have also demonstrated theavailability for amino acids and/or small proteins by using this scheme.
In this study, we report a study on this pKa evaluation scheme for applying to drug molecules. In
order to be able to apply it to drug molecules, we assessed the models for drug molecules and thecomputational conditions for quantum chemical calculations. We show the results by somemolecules systems.

[1] H. Li, A. D. Robertson and J. H. Jensen, Proteins, 2005, 61, 704.
[2] For example, A. H. Juffer and H. J. Vogel, Proteins, 2000, 41, 554.
[3] For example, M. Sprik and G. Ciccotti, J. Chem. Phys., 1998, 109, 7737.
[4] For example, J. Ho and M. L. Coote, Theor. Chem. Acc., 2010, 125, 3.
[5] T. Matsui, T. Baba, K. Kamiya, Y. Shigeta, Phys. Chem. Chem. Phys., 2012, 14, 4181.

P1-37Is hydrophobic group in osmolyte hydrophilic? (2):A time series interaction energy decomposition analysis
study by means of effective fragment potential molecular
dynamics simulation
Tamon Funakura 1
a14.fp3h@g.chuo-u.ac.jp

Yukina Uchino2
g1940628@edu.cc.ocha.ac.jp

Nahoko Kuroki 3,4

Hirotoshi Mori 1,5

kuroki.91d@g.chuo-u.ac.jp

qc-forest.19d@g.chuo-u.ac.jp

Department of Applied Chemistry, Faculty of Science and Engineering, Chuo
University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan2
Department of Chemistry and Biochemistry, Graduate School of Humanities andSciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan
3
Institute of Science and Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku,
Tokyo 112-8551, Japan
4
JST ACT-I
5
JST PRESTO
1

Keywords: Osmolyte, trimethylamine N-oxide (TMAO), ab initio effective fragment potential(EFP), time series interaction energy decomposition analysis, hydrogen bond, hydrophobicity
Molecular architecture of aqueous osmolyte solution is one of the important chemical physics tounderstand biological mechanism of biological control of osmotic pressure and protein
denaturalization. It has been considered that complemental study of spectroscopy and ab initio
molecular dynamics (AIMD) simulations is powerful tool to clarify the mechanisms. However, as
the computational cost of AIMD simulation is very expensive at the moment, there still exist very
big wall to observe slight difference in various osmolytes with the above-mentioned schemes.
Recently, in our group, we have focused on effective fragment potential (EFP) [1], which is an abinitio polarizable force field defined by a set of quantum chemical wave function of compact
constituent molecules. Combining the EFP method with the MD technique, it is possible toperform time series interaction energy decomposition analysis with relatively cheap computationalcosts. In the last CBI annual meeting 2018 [2], we have reported that methyl group of
trimethylamine N-oxide (TMAO), a well-known osmolyte [3], is actually working as hydrophilicgroup due to strong intramolecular polarization of TMAO molecule. This time, we will focus on
the hydration dynamics of TMAO from not only TMAO-water interaction but also water-waterinteraction around TMAO solutes. Applying originally developed time series interaction energy
decomposition analysis based on EFP-MD simulation, we will discuss hydrogen bonding networkand hydrophobicity of TMAO in physiological condition. [4][1] Kuroki, N., and Mori, H., Applicability of effective fragment potential version 2 – Molecular
dynamics (EFP2-MD) simulations for predicting excess properties of mixed solvents, Chemical
Physics Letters,694:82-85, 2018.
[2] Uchino, Y., Kuroki, N., and Mori, H., Is hydrophobic group in osmolyte hydrophilic? :A study byfragment based molecular theory, CBI Annual Meeting 2018, P1-10.
[3] Zou, Q., Bennion, B.J., Daggett, V., and Murphy, K.P., The Molecular Mechanism of Stabilizationof Proteins by TMAO and Its Ability to Counteract the Effects of Urea, Journal of American
Chemical Society, 124 (7):1192–1202, 2002.
[4] Uchino, Y., Kuroki, N., Funakura T., and Mori, H., in preparation, 2019.

P1-38

Artificial ion channels studies
by All-atom molecular dynamics simulations
Takahiro Osamura 1

w185408g@yokohama-cu.ac.jp

Takahiro Muraoka2

muraoka@go.tuat.ac.jp

1

2

3

4

Toru Ekimoto 1

Tsutomu Yamane 1

ekimoto@yokohama-cu.ac.jp

tyamane@yokohama-cu.ac.jp

Kazushi Kinbara 3

Mitsunori Ikeguchi 1,4

kinbara.k.aa@m.titech.ac.jp

ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan
Institute of Global Innovation Research, Tokyo University of Agriculture and Technology,
2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan
School of Life Science and Technology, Tokyo Institute of Technology,
2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550 JapanRIKEN Medical Sciences Innovation Hub Program,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan

Keywords: Molecular dynamics simulation, molecular engine, artificial ion channel
Research on artificial molecular machines has been conducted for a long time, and in 2016 theNobel Prize was awarded for achievements in the basic research on artificial molecular machines.
Molecular machines move mechanically by receiving external energies, but it has been a problem
that conventional artificial molecular machines have no function. On the other hand, a large
number of proteins present in our body are called biomolecular machines, which convert externalenergies by mechanical movements to support life activities. Under these circumstances, in recent
years, the research and development of artificial molecular machines having energy conversion
functions, termed molecular engine (ME), have been promoted. Kinbara et al. developed artificial
ion channels. [1][2][3] KME that is one of the artificial ion channels developed by Kinbara et al.
consists of hydrophobic transmembrane regions and hydrophilic PEG regions, and it has beenclarified in vitro that its function as a channel is controlled by ligand bindings. However, thedetailed mechanism, such as how ligands bind to KME, how KMEs create a pore to the membraneand how many KMEs form to be functionally, has not been clarified. Therefore, we aim to clarify
the details of the function of KME by analysis through all-atom molecular dynamics simulations inlipid bilayer membranes. Elucidating the detailed mechanism will lead to more effective KME
design. At present, we unravel that KME multimerizes in the presence of a ligand, and it ispredicted that cation-π interaction may contribute to ion permeation. [4]

[1]
[2]
[3]
[4]

Takahiro M, Tatsuya S, Tsutomu H,Masamune M, Kazushi K, Chem.Commun.,
2011, 47, 194-196
Takahiro M, Tatsuya S, Tsutomu H, Masamune M, Mashiro T, Kazuhito VT, Hiroyuki N,
Kazushi K, J. Am. Chem. Soc., 2012, 134, 19788-19794
Takahiro M, Takahiro E, Kazuhito VT, Hiroyuki N, Satoru N, Kouhei T, Rui L, Kazushi K,
J. Am. Chem. Soc., 2014, 136, 15584-15594
Robert A. Kumpf, Dennis A. Dougherty, Science, 1993, 261, 1708-1710

P1-39Thermodynamic, kinetic and computational analyses of
the recognition mechanism of a flexible protein antigen byan antibody
Ikuho KANEDA 1

Satoru NAGATOISHI 1,2

Daisuke KURODA 1

i-kaneda@protein.t.u-tokyo.ac.jp ngtoishi@ims.u-tokyo.ac.jp d-kuroda@protein.t.u-tokyo.ac.jp

Makoto NAKAKIDO 1

Jose M.M. CAAVEIRO 3

nakakido@protein.t.u-tokyo.ac.jp jose@phar.kyushu-u.ac.jp

Kouhei TSUMOTO 1,2
tsumoto@bioeng.t.u-tokyo.ac.jp

1

Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-8656, Japan
2
The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,
Tokyo 108-8639, Japan3
Department of Global Healthcare, Graduate School of Pharmaceutical Sciences, Kyushu
University, 3-1-1 Maidashi, Fukuoka, 812-8582, Japan

Keywords: Flexible epitope, Molecular dynamics simulation, Antibody engineering
Many proteins have both rigid and flexible regions, and change their conformations upon
ligand binding. With current methodologies of antibody generation, it is often difficult torationally obtain specific antibodies that recognize flexible epitopes on antigens due to the lack of
our knowledge on molecular recognition between antibodies and flexible antigens. In this study,
we focused on the interaction between an antibody and an antigen that binds metal ions, to obtain
quantitative relationships about antibody-antigen interactions where the antigen is highly flexible.
As a model system, we used the Fab region of a monoclonal antibody 5E1 whose antigen is Sonic
hedgehog (Shh) protein, which also binds to Ca 2+ and Zn 2+ ions. In a previous study, 5E1 Fab had
exhibited high affinity toward Shh regardless of the presence or absence of the metal ions [1].
However, the details of the interactions and physicochemical properties remains unclear.
In this study, to reveal the binding mechanism of 5E1 Fab and Shh, thermodynamic andkinetic parameters of the interactions were experimentally evaluated, and we observed drasticchanges of binding enthalpy and entropy depending on the existence of the metal ions. In addition,
we analyzed and discussed energetic contributions of some interface residues based on
physicochemical measurements and molecular dynamics simulations with the Ala mutants as wellas the wild-type. In addition to the interaction analyses, we also experimentally andcomputationally assessed the thermal stability of Shh with or without the metal ions, showing thedependency of the metal ions on the thermal stability of the antigen.
A co-crystal structure of Shh and 5E1 Fab with the metal ions was already solved [1]. In
this study, we also succeeded in obtaining a crystal structure of the complex without the metal ions,enabling the structure-based understanding of the interaction with or without the metal ions.
Putting together, our results could be a guideline for rational design of antibodies that recognize
flexible antigens.
[1] Henry R. Maun, et al., J. Biol. Chem., 285, 26570-80, 2010

P1-40

Ligand Binding Mechanism of an Enzyme Studied byBinding Free Energy Analyses for Mutants of the Protein
Yoshiharu Mori

Mayuko Takeda-Shitaka

moriy@pharm.kitasato-u.ac.jp

shitakam@pharm.kitasato-u.ac.jp

School of Pharmacy, Kitasato University, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641, Japan

Keywords: Enzyme, Threonyl-tRNA synthetase, Ligand binding, Binding free energy, Molecular
dynamics, Sequence alignment
Aminoacyl-tRNA synthetases (AARS) catalyze the aminoacylation of tRNA. Aminoacylation oftRNA is needed before protein synthesis in ribosomes. The function of AARS is therefore
important for cells.
In this study, we chose the threonyl-tRNA sythetase (ThrRS) of E. coli to study the mechanism of
threonine binding to ThrRS and its molecular insights. This enzyme selects a threonine moleculeand the threonine molecule is bound to ThrRS. Similar amino acids to threonine such as valine andserine are not bonded to a tRNA corresponding to threonine. We studied the selection mechanismof a threonine molecule in ThrRS using several theoretical and bioinformatics methods.
The catalytic site of ThrRS includes amino acid residues that can have several protonation states.
In addition, ThrRS has a zinc ion in the catalytic site. The coordinati on structure of the catalytic
site was modeled and confirmed by molecular dynamics simulations and quantum chemical
calculations. We performed test simulations with different protonation states and force fields. We
determined an appropriate force field and protonation states for ThrRS. We found that themolecular dynamics simulations can give accurate results, which were confirmed by the QM/MM
calculations for the catalytic site including the zinc ion and the threonine molecule .
In the molecular dynamics simulations, we estimated that several amino acid residues in TheRS
stabilize the threonine ligand. To identify the conservation of those amino acid residues, we carriedout a multiple sequence alignment for all the sequences of ThrRS in the UniProt database .
We performed further molecular dynamics simulations to calculate the free energy profile ofamino-acid binding to the catalytic site of ThrRS. To enhance sampling efficiency, we used theumbrella sampling method. Molecular dynamics simulations with umbr ella sampling allowed us toobtain the accurate free energy along with the reaction coordinate. The free energy profile of thecatalytic site for the ThrRS was obtained.
To compare the results of the molecular dynamics simulations with those of the multip le sequencealignment, we performed molecular dynamics simulations for several mutants of E. coli ThrRS and
the wildtype of S. aureus ThrRS. The binding free energy of these systems was calculated.
From the multiple alignment, we found that several amino a cids in ThrRS are highly conserved
and that some of them are positioned near to a threonine molecule in the catalytic site of ThrRS.
From the binding free energy analyses, we found that amino acid residues that are highly
conserved can trap the ligand of ThrRS due to the interactions between the ligand and thesidechain of the highly conserved amino acids. The binding free energy of E. coli ThrRS was
lowest compared with that of the mutants, where one of the conserved amino acids was replaced byother amino acids.
We studied the selection mechanism of a threonine molecule in ThrRS using molecular dynamics
simulations, quantum chemical calculations, and a multiple sequence alignment. We found thatseveral amino acid residues are highly conserved and that the se amino acid residues contribute thestability of the ligand binding in threonyl-tRNA synthetase.

P1-41

Conformation analysis of hydrogen atoms
around ligand-binding pocket
based on quantum chemical calculation
Chiduru Watanabe 1, 2
chiduru.watanabe@riken.jp

Kaori Fukuzawa 3

k-fukuzawa@hoshi.ac.jp

Motoyasu Adachi 5

Shigenori Tanaka 6adachi.motoyasu@qst.go.jp

tanaka2@kobe-u.ac.jp

Yoshio Okiyama 4okiyama@nihs.go.jp

Teruki Honma 1
honma.teruki@riken.jp

1

RIKEN Center for Biosystems Dynamics Research 1 -7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
2
JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan3
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2 -4-41 Ebara,
Shinagawa-Ku, Tokyo, 142-8501, Japan
4
National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki City,
Kanagawa, 210-9501, Japan
5
National Institute for Quantum and Radiological Science and Technology, Tokai, Ibaraki
319-1106, Japan
6
Graduate School of System Informatics, Kobe University, 1 -1 Rokkodai, Nada-ku, Kobe
657-8501, Japan

Keywords: FMO, structure refinement, hydrogen atom,
The conformational state of protein–ligand complex based on structural biology such as X-ray
crystal structure analysis is valuable information to realize rational drug design more precisely. On
the other hand, to understand biological phenomena with chemical reaction, structural r efinement
for hydrogen atoms around the ligand-binding or the active sites is one of the important issues
because of structural ambiguity in the case of a low-resolution crystal structure. From technicaladvances of protein crystallography in recent years, it is possible to use the highly reliableconformations including hydrogen atoms obtained from high-resolution X-ray and neutron
diffraction analyses. The behavior of molecules regarding chemical reactions that were so far
difficult to observe has been becoming to analyze by these high-resolution experimental data.
Additionally, since attempts of structure refinement using optimization [1] and electron density
analysis based on fragment molecular orbital (FMO) calculations have already begun, thestructures commensurate with the accuracy of quantum chemical calculation are becoming
available. Therefore, it is expected that the precise structure including these hydrogen atoms willlead to the elucidation of mechanisms of the reaction in an active center and the molecular
recognition.
In this research, to evaluate molecular conformation around ligand-binding pocket using theneutron crystal structure including coordinate of hydrogen atoms, we anal yze the stability ofenergy potential surface and the complementary comparison of FMO electron density with X -rayelectron density. The findings of this study would be useful to construct fundamental technology of
FMO-based structure refinement.
Acknowledgement
This research was done in activities of JST PRESTO Grant Number JPMJPR18GD. This research
was conducted as part of the activity of the FMO drug design consortium (FMODD).
[1] T. Tsukamoto et al., Chem. Phys. Lett. 535 (2012) 157.

P1-42

In vitro screening systems for influenza virus RNA
polymerase inhibitors
Miho Kobayashi 1
m_koba@gifu-u.ac.jp

1
2

 

Yuji O. Kamatari 1,2
kamatari@gifu-u.ac.jp

Life Science Research Center, Gifu University, 1-1 Yanagido, Gifu, 501-1193 Japan
United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University,
1-1 Yanagido, Gifu, 501-1194 Japan

Keywords: in vitro screening system, influenza, RNA polymerase, DSF, NMR
Emergence of resistant viruses against influenza drugs is a major concern and development ofanti-influenza compounds with novel mechanisms is required. Because its amino acid sequenceof the RNA polymerase was proven to be well-conserved among strains, it is good target for
influenza drugs development. It consists of three subunits (PA, PB1, PB2), and compounds
that prevent the trimer formation are thought to exert anti-influenza activity. We haveconstructed in vitro screening systems for influenza virus RNA polymerase inhibitors. First
screening system is targeted to the interaction site between PA(257–716) and PB1(1–15) [1].
Recombinant protein, PA(257–716), was expressed using E. coli and the thermal
denaturation of this protein was observed using differential scanning fluorimetry (DSF).
Because DSF offer high-throughput, minimal material consumption, and relatively low
false positive rate [2], it is suitable for the in vitro screening system. Addition of asynthetic peptide, PB1(1–15), shifted the transition of PA(257–716), indicating this system,
DSF of recombinant PA(257–716), is available to screen compounds to bind PA(257–716).
Second system is targeted to the interaction site between PB1(678–757) and PB2(1–37) [3].
Recombinant PB1(678–757) and PB2(1–37) were expressed using E. coli . FreePB1(678–757) was folded with relatively large conformational fluctuation, free PB2(1–37)
was unfolded, and the complex had fixed structure different from the free forms, indicatedby DSF and NMR. There are also available to screen compounds to bind PB1(678–757) andPB2(1–37). Third screening system is targeted to the endnuclase domain, PA(1–198) [4].
Recombinant PA(1–198) was expressed using E. coli and the thermal denaturation of thisprotein was observed using DSF. Addition of a compound, baloxavir acid [5], shifted thetransition of PA(1–198), indicating this is also available to screen compounds to bindPA(1–198). Advantages of in such vitro screening systems are, (1) the same strategy can beapplied to any other targets, (2) it is applicable to high-risk targets safely, for example,
Ebola virus, (3) it is possible to screen compounds to targets that have not yet emerged, and(4) because of the mechanism of action of the compound is clear, its optimization may bestraight forward.
[1] Obayashi, E. et al., Nature, 454:1127-1131, 2008.
[2] Kabir, A. et al., J Biochem. 161:215-222, 2017.
[3] Sugiyama, K. et al., EMBO J. 28:1803-1811, 2009.
[4] Omoto, S. et al., Sci. Rep. 8:9633, 2018.
[5] Noshi, T. et al., Antiviral Res. 160:109-117, 2018.

P1-43

Can Cryptic Binding Sites Be Characterised by Voids,
Pocket Detection, and Molecular Dynamics?
Shinji Iida 1 , Hironori Nakamura 2 , Tadaaki Mashimo 1,3s-iida@aist.go.jp, nakamura@biomodeling.co.jp, mashimo.tadaaki@imsbio.co.jp

Yoshifumi Fukunishi 4
y-fukunishi@aist.go.jp
1

2

3
4

Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26,
Aomi, Koto-ku, Tokyo 135-0064, Japan
Biomodeling Research Co., Ltd., 1-704-2, uedanishi, tenpaku-ku, Nagoya, Aichi, 468-0058,
Japan
IMSBIO Co., Ltd., 170-0013, 4-21-1, Higashiikebukuro, Toshima City, Tokyo, Japan
Molecular Profiling Research Center for Drug Discovery, AIST Tokyo Waterfront, 2-3-26
AOMI, KOTO-KU, Tokyo, Japan

Keywords: Molecular dynamics, cryptic binding site, void
For drug discovery, binding sites tell us how to design drugs, although they are often cryptic in apo
conformation especially in protein-protein interactions. Success of cryptic site detection would
impact drug design because the existence of cryptic sites expands druggable targets [1] .
Here, we aim to characterise cryptic sites and to propose a simple scheme for the detection.
We first attempt to capture characteristics of cryptic sites for a set of PDB data [1] via void
generation, pocket detection, and structural comparison. The reason why we focus on voids
interior to a protein is that a decrease in void volume could lead to the formation of cryptic sites.
We show that about 70 % of the dataset have a void within 10 Angstrom of a ligand-binding site.
To examine the possibility that cryptic sites are simply detected from an apo conformation, weapply MolSite [2], which is a software for detection of ligand binding sites , to the dataset,demonstrating that it detects some cryptic sites even though the ratio is not high. Additionally, weinvestigate several modes of cryptic site opening from the dataset: bending helix, bending beta
sheet, a single residue movement, etc.
Furthermore, we conduct atomistic molecular dynamics (MD) simulations with explicit
solvent at an ambient condition in order to detect conformers in which cryptic sites is open.
Throughout the simulations, we use Bcl-x(L) as a model of the proteins possessing cryptic sites.
We indicate that, at an ambient condition, Bcl-x(L) exposes its cryptic sites during 5 micro sec,
implying that cryptic site formation may be dominant if it does not require large conformationalchanges like the case of the cryptic site formation for Calmodulin [1]. The implication is supported
by an earlier study of a different protein indicating that about 50 % of opened states of a cryptic
site were populated [3].
[1] Peter Cimermancic et al., CryptoSite: Expanding the druggable proteome by characterizationand prediction of cryptic binding sites. J. Mol. Biol, 2016, 428(4), 709-719.
[2] Yoshifumi Fukunishi et al., Prediction of ligand-binding sites of proteins by molecular docking
calculation for a random ligand library, Protein Science, 2011, 20:95-106.
[3] Gregory R. Bowman et al., Equilibrium fluctuations of a single folded protein reveal amultitude of potential cryptic allosteric sites, PNAS, 2012, 109(29), 11681-11686.

P1-44Computational study on conformational transition ofprotein binding pocket upon ligand binding
Noriaki Okimoto 1
okimoto@riken.jp

1

Yoshinori Hirano 1
hirano@riken.jp

Makoto Taiji 1
taiji@riken.jp

BDR, RIKEN, Building B, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan, Japan

Keywords: molecular dynamics simulation, binding pocket, ligand binding

The conformation, dynamics and function of proteins are closely linked, and many proteins
undergo conformational changes upon ligand binding. The X-ray crystallographic studies
have revealed conformational differences in proteins between the liganded and unliganded
states. It is important to understand the mechanism of the conformational change on ligand
binding at atomic level. Molecular dynamics (MD) simulation is so helpful forunderstanding the conformational change in depth because this is a powerful tool inproviding description of the dynamics and energetics of biomolecules with high spatial andhigh temporal resolution.
In this study, we have performed several MD simulations of a few proteins (thelysine/arginine/ornithine-binding protein (LAOBP) 1 and so on) to understand theconformational change on ligand binding at atomic level. We have analyzed the domain
movement, protein-ligand interactions, and solvation structure. Based on the current andprevious results 2 , we have investigated the relationship between protein conformationalchange and ligand binding. We will report the detailed results.

The crystal structures of LAOBP:The left and right figures show the unliganded (apo) and liganded (holo) structures.
References1.

Oh, B. H.; Pandit, J.; Kang, C. H.; Nikaido, K.; Gokcen, S.; Ames, G. F.; Kim, S. H. J Biol Chem

1993, 268(15), 11348-11355.
2.

Kondo, H. X.; Okimoto, N.; Morimoto, G.; Taiji, M. J Phys Chem B 2011, 115(23), 7629-7636.

P1-45The computational study of cycloaddition with Rhodium
catalyst
Watanabe Kazuki 1
Watanabe-ka@phs.osaka-u.ac.jp

Yu-Shi TIAN 1
yushi-tian@phs.osaka-u.ac.jp

1

2

Kawashima Yusuke 1
kawashima-y@phs.osaka-u.ac.jp

Kawashita Norihito 2

Takagi Tatsuya 1

nkawashita@life.kindai.ac.jp

ttakagi@phs.osaka-u.ac.jp

Graduate School of Pharmaceutical Sciences, Osaka Universtity, 1-6
Yamadaoka, Suita, Osaka 565-0871, JAPAN
Faculty of Science and Engineering, Kindai University, 3-4-1
Kowakae, Higashi-osaka, Osaka 577-8502, JAPAN

Keywords: Molecular orbital calculation, Rhodium catalyst, AFIR, Metallo-cycleaddition
Although the cycloaddition reaction of VCP with olefins using transition metal catalysts has been
considered that the driving force of the reaction is the strain energy of the three-membered ring [1],[2] ,similar reactions occur when less distorted cyclobutane or cyclopentane analogs having an allenylgroup are used [3],[4] . Thus, DFT calculations (B3LYP/6-31G */SDD+ECP) were applied to reveal thecycloaddition reaction mechanism adopting an allenyl cycloalkane and cyclopentane analogue inthe previous research as a ligand. Two pathways, one contains ring cleavage(Fig. 1 [1]) and theother contains the metallocycle as an intermediate(Fig. 1 [2]), are considered. Note that Gaussian
09 was used for electronic structure calculation, and Reacion Plus Pro 1.0.1 [5] and GRRM 17 [6] wereused for reaction path search.
In the allenyl cycloalkane, there was a correlation between
strain magnitude of the partial structure cycloalkane and thereaction pathway. Specifically, allenyl cyclopropane having alarge three-membered ring distortion was superior to thereaction pathway of [1]. On the other hand, those having a
relatively small strained structure such as allenyl cyclobutane
and pentane as a partial structure were superior to the reaction
pathway of [2], which was particularly remarkable in allenyl
cyclopentane.
Therefore, next, in order to verify the superiority of theallenyl structure, a reaction model close to a real reaction
system is created with reference to the previous researches,
and the AFIR method implemented in the chemical reaction
path automatic search program GRRM17 is a more detailed
study was conducted using this.
Fig. 1.Modeling of the reaction[1] Paul A. Wender, H. Takahashi, J. Am. Chem. Soc., 117, 4720-4721 (1995).
[2] Zhi-Xiang Yu, Paul A. Wender, K. N. Houk, J. Am. Chem. Soc., 126, 9154-9155 (2004).
[3] F. Inagaki, K. Sugikubo, Y. Oura, C. Mukai, Chem. A Eur. J., 17, 9062-9065 (2011).
[4] C. Mukai et al., J. Am. Chem. Soc., 134, 19580-19583 (2012).
[5] Henkelman G. et al., J. Chem. Phys., 113, 9978 (2000).
[6] S. Maeda et al., J. Comput. Chem., 39, 233-250, (2018).

P1-46

Analysis of target DNA recognition mechanism by
Nrf2-small Maf heterodimer using fragment molecularorbital (FMO) method
Toru Sengoku 1
tsengoku@yokohama-cu.ac.jp

1
2

Hirotomo Moriwaki 2

Chiduru Watanabe 2

hirotomo.moriwaki@riken.jp

chiduru.watanabe@riken.jp

Teruki Honma 2

Kazuhiro Ogata 1

honma.teruki@riken.jp

ogata@yokohama-cu.ac.jp

Department of Biochemistry, School of Medicine, Yokohama City University, 3-9 Fukuura,
Kanazawa-Ku, Yokohama City, Kanagawa, 236-0004, JapanRIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama City, Kanagawa, 230-0045, Japan

Keywords: FMO, Transcription factor, Neurodegenerative disease, Crystallography
Nrf2, a transcription factor belonging to the CNC -bZIP family, plays a central role in oxidative
stress response. Nrf2 forms a heterodimer with other bZIP proteins (small Maf proteins) andspecifically recognizes its target sequence (CNC -small Maf binding element: CsMBE) in thevicinity of the target gene to control transcription. The optimal CsMBE is
TGCTGA(G/C)TCA(T/C), whereas Nrf2-small Maf heterodimers can bind slightly different
sequences. Recently, A SNP (rs242561) associated with neurodegenerative diseases such as
Parkinson's disease have been found in the CsMBE in the regulatory region of the MAPT gene(encoding Tau protein). rs242561 is located at the first base on the CsMBE (bold position, C or T),
and the low-risk T allele increases the binding level of Nrf2-small Maf heterodimer and theexpression level of MAPT gene. Thus, transcriptional control of target genes by Nrf2 -small Maf
heterodimers is quantitatively influenced by slight sequence differences in the CsMBE, which is
linked to the risk of neurodegenerative diseases.
To analyze in detail the effects of CsMBE sequence differences on the interaction with Nrf2 -small
Maf heterodimers, we determined the crystal structure of the Nrf2-MafG (a type of small Mafgroup factor) heterodimer recognizing the optimal CsMBE. In the s tructure, the first T-A base pairdoes not form a hydrogen bond with either Nrf2 or MafG; thus, it was not clear how theheterodimer recognizes this base pair. Therefore, we employed the FMO analysis and found that
the T base and Arg57 of MafG form a base-specific interaction mainly driven by dispersion forces.
Our study elucidates how the disease-related SNP affects the binding of transcription factors andultimately disease risk, and shows that the FMO method is a useful tool for quantitative analysis offunctions of biomacromolecules.

P1-47

Interaction Analysis between HEL and HyHEL10
by Fragment Orbital Method

1

Norihito KAWASHITA 1

Shotaro YAMAGUCHI 1

kawashita@life.kindai.ac.jp1410330081u@kindai.ac.jp

Faculty of Science and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka City,
Osaka 577-8502, Japan

Keywords: Antibody, Antigen, Fragment molecular orbital (FMO) method, Inter-fragmentinteraction energy (IFIE)
It is expected that the novel drug can be efficiently developed by the simulation in thecomputer, while the development cost is suppressed. Fragment molecular orbital(FMO) method
which is one of the methods of in silico drug discovery, and by shortening the calculation time, thehigh-precise interaction analysis of the biopolymer has become feasible in sho rt time.
In this study, we focused on egg white lysozyme and its antibody, HEL/HyHEL -10, as an
example of antigen-antibody complex in which there are few examples of analysis by FMO method,and examined the evaluation method of the interaction between an tigen and antibody, and collected
the information which contributes to the molecular design of antibody medicine.
Eight structures of the antigen-antibody complex in the literature [2] was downloaded fromthe Protein Structure Data Bank (PDB), and the structure was modified by MOE. In the structural
optimization, Amber10:EHT was used as theforce field. Modifications were made in two ways: optimized after elimination of solvent
water and optimized including solvent water. The computer used the supercomputer K with the
ABINIT-MP software and performed the calculation at MP2/6-31G*.
We compared the calculated inter-fragment interaction energies (IFIE) with the experimental
values in the literature, and analyzed them on the basis of structural changes in amino acid residues
and changes in IFIE in response to changes in antibodies. Comparing the calculated IFIE with theexperimental values, it was found that the optimized ones including solvent water had a better
correlation with the experimental values. Therefore, it seems to be better to carry out t he structuralmodification without removing solvent water. Next, we compared the structures of 2dqf and WT(wild-type) which showed large differences in IFIE by MP2 correlation. Aromatic amino acids
such as tyrosine, which are commonly found in antigen -antibody binding sites in WT, had π-πinteractions with other residues, but no such interactions were found at the mutation site in 2dqf, inwhich tyrosine was mutated to alanine. And, the interaction by tyrosine residue showed the goodcorrelation, when the IFIE by the MP2 correlation was indicated that the assessment using MP2
correlation was important in the antigen-antibody complex.
This research was done in activities of the FMO drug design consortium (FMODD). The
results were obtained using the K computer (project ID: hp170183 and hp180147). PIEDA
calculation was done by using MIZUHO/BioStation software package.
[1] Lin et al., Science, 290: 150-153, 2008.
[2] Congreve et al., J. Med. Chem. 50: 1124-1132, 2007.
[3] Chemical Computing Group, MOE. 2015.
[4] Nakano et al., Chem. Phys. Lett. 351: 475-480, 2002.

P1-48Combined computational and experimental study
on the binding mechanism of RNA aptamer
to human Immunoglobulin G
Keisuke Masukawa 1

Hisae Yoshida 1,2

Seiichiro Ishii 1

Yuuki Yatabe 3

ceke18021@g.nihon-u.ac.jp

Takeshi Ishikawa 4

Taiichi Sakamoto 3

Kenji Yamagishi 1

1

College of Engineering, Nihon University, 1 Nakagawara, Tokusada, Tamura, Koriyama,
Fukushima 963-8642, Japan
2
JSPS Research Fellow, Kojimachi Business Center Building, 5-3-1 Kojimachi, Chiyoda-ku,
Tokyo 102-0083, Japan
3
Faculty of Advanced Engineering, Chiba Institute of Technology, 2 -17-1 Tsudanuma, Narashino,
Chiba 275-0016, Japan
4
Faculty of Engineering, Kagoshima University, 1-21-24 Koorimoto, Kagoshima, Kagoshima,890-8580, Japan

Keywords: RNA aptamer, Molecular interaction, Dynamical feature
RNA aptamers are short, single-stranded oligonucleotides that bind to specific target molecules
such as proteins, nucleic acids, and small molecules. The single strand of RNA aptamers can foldinto various secondary structure motifs such as stems, hairpins, bulges, and pseudoknots, andcombination of these secondary structure motifs creates unique tertiary structures that enable the
RNA aptamer to interact with high affinity and specificity for its target molecule. RNA aptamers,
therefore many potential applications in medicine and technology.
Recently, an optimized 23-nucleotide aptamer was designed, which was shown to bind with highaffinity to the Fc domain of human Immunoglobulin G (IgG) [1]. The crystal structure of the IgG
complexed with the RNA aptamer has been determined [2]. However, the structural ba sis of thebinding mechanism of the RNA aptamer with IgG is poorly understood.
In this study, to clarify the binding mechanism of RNA aptamer, we have utilized computational
simulations and experimental approaches. At first, we have performed the ab initio fragment
molecular orbital calculation and experimental isothermal titration calorimetry analysis for RNA
aptamer/IgG complex to elucidate the molecular interaction between RNA aptamer and IgG.
Furthermore, we performed themolecular dynamics calculations
and experimental nuclear magnetic
resonance analysis for the RNA
aptamer - IgG complex in order toelucidatethe
conformational
behaviors and the dynamical
features of aptamers.
[1] S. Miyakawa, et al., RNA, 14, 1154
(2008).
[2] Y. Nomura, et al., Nucleic Acids
Research, 38, 7822 (2010).

Figure. Combined computational and experimental study

P1-49Investigation of drug resistance acquisition mechanism of
influenza cap-dependent endonuclease against Baloxavir
marboxil by molecular dynamics simulation
Ryunosuke Yoshino 1,2
yoshino.r.aa@md.tsukuba.ac.jp

Nobuaki Yasuo 3yasuo@cbi.c.titech.ac.jp

Masakazu Sekijima 3
sekijima@c.titech.ac.jp

1

Transborder Medical Research Center, University of Tsukuba, 1 -1-1
Tennodai, Tsukuba, Ibaraki 305-8577, Japan

2

Center for Computational Sciences, University of Tsukuba, 1 -1-1
Tennodai, Tsukuba, Ibaraki 305-8577, Japan
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3 -23-4259
Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan

3

Keywords: Baloxavir marboxil, Influenza virus, Cap-dependent endonuclease, Molecular
dynamics
Baloxavir marboxil (BXM), with the trade name Xofluza, has been approved by the FDA in
October 2018 as antiviral drug for influenza virus [1, 2]. This drug developed by Shionogi Inc andinhibits RNA replication of influenza virus by binding to RNA replication cap-dependent
endonucleases (CEN) [2]. However, in clinical trials, drug resistant viruses were detected with I38
mutation [2, 3].
In order to clearly the reduction of drug sensitivity by the I38 mutant variants, we conducted
molecular dynamics (MD) simulation to compare the interaction mode between wild type andmutant type. MD simulation results suggest that the methyl groups of I38 side chain interacts witharomatic ring of BXM through a CH-pi interaction. In contrast, the MD simulation results for the
mutation models indicated that the I38T mutation results in a loss of interaction. The side chain methylgroup of isoleucine is connected to Cα atom via three single bonds. In the case of threonine, 2 single
bonds connect the methyl group with Cα atom. Therefore, MD simulation suggest that the methylgroup of Thr38 is at a distance too far to interact with aromatic ring of BXM.
[1] Food and Drug Administration. FDA approves new drug to treat influenza. 2018.
https://www.fda.gov/news-events/press-announcements/fda-approves-new-drug-treat-influenza[2] Omoto, S., et al., Characterization of influenza virus variants induced by treatment with theendonucleaseinhibitor
baloxavir
marboxil ,Scientific
Reports.
8:9633,doi:
10.1038/s41598-018-27890-4 2018.
[3] Hayden, F.G., et al., Baloxavir marboxil for uncomplicated influenza in adults and adolescents.
N. Engl. J. Med., 379:913–923, 2018.

P2-01

Suitable chemical library for academic researchersin Japan
Hirotatsu Kojima
kojima@mol.f.u-tokyo.ac.jp

Drug Discovery Initiative, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Academic drug discovery, Library design, Chemical screening
Drug Discovery Initiative (DDI) of the University of Tokyo was launched to promoteacademic drug discovery. We are maintaining the largest public chemical library in Japan, which
consists of about 280,000 compounds including non-commercial unique samples from universities
and deposited samples from pharmaceutical companies. The samples of the chemical library havebeen chosen mainly based on druggability and structural diversity, and added every year since2007. We have been making improvements of the chemical library such as exclusion ofpromiscuous hitters based on reported data.
The chemical samples are provided to any researchers in Japan who are willing toconfidentially disclose their research goals and report the assay results to us. The general samples
themselves are free of charge, but payment for microplates and shipping is needed. We do notclaim any right to the results of screening in the absence of intellectual collaboration. We haveprovided more than 25 million samples to more than 600 users so far.
It is difficult for many academic researchers to carry out random screening of more than
10,000 samples. In such cases, we can provide "core library," which is a diverse 9,600-sample set.
While taking into consideration the users' circumstances, "advanced core library" has been recently
prepared in order to encourage larger-scale assays, which is another diverse 22,400-sample set.
Short training courses on chemical screening are held several times a year to help beginners.
The researchers may also access HTS facilities in DDI. Our organization founded the Lead
Exploration Unit in 2016, which has started a hit-to-lead synthesis service, as well as ADMET
support, upon request.

P2-02

Classification QSAR with Vanishing Kernels and a single
parameter
Francois Berenger1
beren314@bio.kyutech.ac.jp

1

Yoshihiro Yamanishi1
yamani@bio.kyutech.ac.jp

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering,
Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.

Keywords: Ligand-Based Virtual Screening, Classification, QSAR, Kernel Density Estimate.
In this poster, a technique to rank-order molecules is shown. Being able to rank-order molecules allows to
perform classification QSAR. The technique described here needs a single parameter (called kernel
bandwidth) to be optimized during model training.
In essence, Kernel Density Estimates (KDE) are revisited and used to rank-order molecules. In order tomake KDE compute-efficient, two modifications are proposed: i) using ``vanishing kernels''; i.e. kernel
functions with a bounded support and ii) using the Tanimoto distance between chemical fingerprints as aradial basis function in order to work in one dimension. We call this modified construction “Vanishing
Ranking Kernels”[1].
Equipped with this construction and using two real-world datasets, one from toxicology and one from HighThroughput Screening (HTS) experiments, we show that Ranking Kernels can compete in performance witha state of the art deep-learning implementation for molecules.
Ranking Kernels are conceptually simple. They only require a single parameter to be optimized and hence
are fast to train. Once trained, they also define a Boolean Applicability Domain[2], for free. In our
experiments, this AD allows to screen candidate molecules at least 69% faster.
[1] Berenger, F., & Yamanishi Y. Ranking Molecules With Vanishing Kernels. Manuscript to be submitted,
2019.
[2] Berenger, F., & Yamanishi, Y. (2018). A Distance-Based Boolean Applicability Domain forClassification of High Throughput Screening Data. Journal of chemical information and modeling,59(1), 463-476.

P2-03

Automated Assessment of Binding Affinity via Free
Energy Perturbation
Giovanna Tedesco 1

giovanna@cresset-group.com

Paolo Tosco 1

2

Antonia Mey 2

paolo@cresset-group.com

antonia.mey@ed.ac.uk

Mark Mackey 1

Julien Michel 2

mark@cresset-group.com
1

Maximilian Kuhn 1,2

max@cresset-group.com

julien.michel@ed.ac.uk

Cresset, New Cambridge House, Cambridgeshire, UK
University of Edinburgh, Department of Chemistry, Edinburgh EH9 3FJ, UK

Keywords: Free Energy Perturbation, Relative Binding Affinity
Free Energy Perturbation (FEP) calculations allow for prediction of relative binding affinitychanges within a congeneric ligand series. 1 In these calculations a molecule is gradually converted
into a structurally closely related ligand via a non-physical (‘alchemical’) pathway. By assessing
the free energy difference (ΔΔG) between the end states of such transformations, accuracies ofabout 1 kcal/mol compared to experimental values can be achieved for large datasets. 2,3
However, routine application of FEP calculations is still hampered by a lack of automation.
Though several open-source applications strive to make FEP more accessible (e.g., the Siremolecular simulation package 4,5 ), installing and using these relatively complex tools requires
expertise.
To make this method accessible and user-friendly, a fully automated workflow for performing FEP
calculations has been implemented in Flare, Cresset’s structure-based design platform. 6
The workflow was validated using different datasets, including the FEP+ dataset as a benchmark. 2,3
Results obtained with this method were broadly comparable to published reports, considering theoverall reduced simulation time.
Full control over the simulation parameters is available within the Flare FEP project GUI andthrough the Flare Python API, to enable power users to explore and identify the ideal conditions
for a given set of ligands and their target protein.
[1] Cournia, Z. et al., Journal of Chemical Information and Modeling, 57: 2911-2937, 2017.
[2] Wang, L. et al., Journal of the American Chemical Society, 137:2695-2703, 2015.
[3] Song, L.F. et al., Journal of Chemical Information and Modeling, 59:3128-3135, 2019.
[4] Woods, C. et al., Sire molecular simulations framework, http://siremol.org, 2016.
[5] Mey, A.S.J.S. et al., Journal of Computer-Aided Molecular Design 32:199-210, 2018[6] http://www.cresset-group.com/flare/P2-04

Discovery of novel selective CSF-1R Inhibitorswith de novo drug design of FBDD and MD simulation.
Mutsuyo Wada 1

Atsushi Tomonaga

Shunji Matsumoto 1

mutsu@fujitsu.com

a.tomonaga1215@jcom.zaq.ne.jp

shun@fujitsu.com

Yuichiro Tabunoki 2
y-tabuno@kowa.co.jp

Ayumu Okuda 2
a-okuda@kowa.co.jp

Tatsuhiko Kodama 3kodama@lsbm.org
1
2

3

Fujitsu Ltd., 1-2-70 Konan, Minato-ku, Tokyo 108-0075, Japan
Kowa Co., Ltd., Tokyo New Drug Research Laboratories, 2-17-43, Noguchi-cho,Higashimurayama-sh, Tokyo 22, 189-0022, Japan
Laboratory for Systems Biology and Medicine, Research Center for Advanced Science andTechnology, The University of Tokyo, Building 4, Komaba Research Campus, 4-6-1 Komaba,
Meguro-ku, Tokyo 153-8904, Japan

Keywords: de novo drug design, FBDD, in silico screening, MD simulation
The conventional low-molecular-weight drug discovery technology search for the compounds
shows a certain threshold of reference inhibitory activity from the market or original chemical
libraries. However, the hit compound has the problem with a novelty, diversity and selectivity to
the target protein. We report that novel CSF-1R inhibitors were identified with in silico drug
design technology based on FBDD (named OPMF [1]) and molecular dynamics (MD) simulation.
In this study, we designed the various compounds with OPMF using different types of DFG-out(PDB: 3KRJ, 3LCO) structures of CSF-1R, an anti-cancer target protein [2, 3], synthesized 8compounds and obtained 3 novel hit compounds (IC50: < approximately 5 μM, hit rate 38%)
measuring the inhibition of phosphorylation in CSF-1R enzyme assay. The modified compoundsof the highest activity of hit compounds were assessed with the hydrogen bond analysis of MD
simulation. We identified the highly potent inhibit ors that increased formation time of hydrogenbond with D796 and E633 of CSF-1R. The compounds showed the kinase-selective inhibition
and the suppression of CSF-1 dependent cell growth.
We showed that de novo drug design with OPMF and ligand optimization and selection with MD
simulation were useful for screening and optimizing the novel and selective compounds for leaddiscovery.

Fig. Two X-ray co-crystal structures of CSF-1R[1] T. Yamashita, et al., Chem Pharm Bull (Tokyo) . 2014; 62(7): 661-7[2] C. R. Illig, et al., J Med Chem. 2011 Nov 24; 54(22): 7860-83.
[3] M. J. Meyers, et al. Bioorg Med Chem Lett. 2010 Mar 1; 20(5): 1543-7.

P2-05Predicting pKa Using a Combination of Quantum andMachine Learning Methods
Peter Hunt 1

Sumie Tajima 2

peter@optibrium.com

12

Layla Hosseini-Gerami 1

tajima@hulinks.co.jp

info@optibrium.com

Tomas Chrien 1

Matthew Segall 1

tomas@optibrium.com

matt@optibrium.com

Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road
Cambridge, CB25 9PB, UK
Hulinks Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-0015, Japan

Keywords: Dissociation of a proton, pKa prediction, Radial Basis Function method
The dissociation of a proton from a heteroatom has a significant impact on the charge distribution
and interactions of a molecule. These influence many important molecular properties, including
binding to target and off-target proteins, absorption, distribution, metabolism and excretion(ADME) and pharmacokinetic (PK) properties such as solubility, tissue or cellular distribution andpermeability. Therefore, the ability to predict the propensity of a molecule to lose or gain a proton
in water is crucial for the development of new chemical entities with desirable PK, ADME andbinding properties. We describe a method for prediction of the acid dissociation constant(pK a ) of a heteroatom that combines quantum-mechanical (QM) descriptors, calculatedusing the semi-empirical AM1 method, with machine learning to generate an accuratequantitative structure-activity relationship (QSAR) model. The QM descriptors capture thegeometric and electronic properties of the environment of site of (de)protonation, in thecontext of the whole molecule. This provides greater discrimination between potentially
acidic or basic sites and greater transferability than simple fragment descriptors. The
resulting model achieves a coefficient of determination of greater than 0.9 and aroot-mean-square error less than one log unit on an external test set containing both monoand multi-protic compounds. We also present results and comparisons with other methods
for published benchmarking sets.

P2-06
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P2-07

Analysis of subtype selectivity in estrogen -like compounds
Yuya Seki 1
m1814@hoshi.ac.jp

Satoshi Anzaki 2
anzaki@eniac.scitec.kobe-u.ac.jp

Takayuki Furuishi 1
t-furuishi@hoshi.ac.jp
1
2

Tsukasa Kato 1
s151080@hoshi.ac.jp

Shigenori Tanaka 2

Koji Okuwaki 1

tanaka2@kobe-u.ac.jp

k-okuwaki@hoshi.ac.jp

Kaori Fukuzawa 1

Etsuo Yonemochi 1

k-fukuzawa@hoshi.ac.jp

e-yonemochi@hoshi.ac.jp

Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Kobe University Graduate school, 1-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan

Keywords: Estrogen Receptor β, Subtype Selectivity, Protein-Ligand Interaction, Fragment
Molecular Orbital (FMO) Method, Pair Interaction Energy Decomposition Analysis (PIEDA)
Estrogen receptor (ER) has two subtypes (α, β) and it is known that phytoestrogen is selective forERβ while 17β-Estradiol binds strongly to both subtypes. Complex structures between ER andvarious ligands have been revealed by X-ray crystallography. The difference between these two
subtypes in the ligand binding pocket is that two hydrophobic amino acid residues (M336L,
I373M) are mutated. However, ligand binding properties and the mechanism of their subtype
selectivity have not been well understood. In this study, we exhaustively analyzed the PDB
complex structures of ERα/β and ligand using the fragment molecular orbital (FMO) method. We
examined changes in the shape of the ligand binding pocket for each subtype in terms of theinteraction with the ligand and surrounding amino acid residues.
We calculated 34 PDB structures of the ER-ligand complex at the FMO2-MP2/6-31G* level. We
analyzed the inter-fragment interaction energies (IFIE) and their energy component (PIEDA)
between the ligand and surrounding amino acid residues. We also performed a clustering of ligands
by similarity of interactions with receptor using the Visualized Cluster Analysis (VISCANA).
As a result of the cluster analysis based on the binding modes of ERβ- ligand complex, several
findings were obtained: the hydrogen bond with E305 and the CH/π interaction with F356 were
essential for ligand binding. The β-selective ligand interacted with H475 via a strong hydrogenbond. The amino acid residue (I373M) mutated between subtypes formed CH/π interaction with
H475. The orientation of the side chain of I376 was different among the subtypes . Suchhydrophobic amino acid residues, I373, I376,
β selectivity interaction
and F377 of ERβ formed a β-selective
Hydrogen bondEssential
OHhydrophobic pocket. This pocket of ERβ was
M336
H475
interactions
found to be smaller than that of ERα, and it
N
Hydrogen bondis suggested that a highly planar structure of
I373
O
E305
ligand with π electrons can be fitted to thisHOhydrophobic pocket of ERβ. Therefore, aI376
Ffunctional group with π electrons that can
R346
F377
β-selective
F356
form an interaction network in thehydrophobic pocket
CH/πinteractionhydrophobic pocket is a characteristic of: Interaction between amino acid residues: Interaction with ligand
β-selective ligands.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp 190119). PIEDA calculation was done by using
MIZUHO/BioStation software package.

P2-08

FMO analysis of binding property of estrogen receptor andβ selective ligands
Tsukasa Kato
s151080@hoshi.ac.jp

Takayuki Furuishi
t-furuishi@hoshi.ac.jp

Yuya Seki
m1814@hoshi.ac.jp

Kaori Fukuzawa

Etsuo Yonemochi

k-fukuzawa@hoshi.ac.jp

e-yonemochi@hoshi.ac.jp

School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2 -4-41 Ebara,
Shinagawa-Ku, Tokyo, 142-8501, Japan

Keywords: estrogen receptor β, β-selective ligand, phytoestrogens, protein-ligand interaction,
fragment molecular orbital (FMO) method, Molecular docking
The estrogen receptor (ER) has two subtypes, ERα and ERβ. It is known that 17β -estradiol, which
is an endogenous estrogen, has no selectivity, but phytoestrogens are several tens of times andprinaberel, a synthetic ligand, is about 200 times more selective for ERβ. In this study, fragment
molecular orbital (FMO) method is used to evaluate the interaction between ERβ and its selective
ligand to clarify the relationship between them and β selectivity.
Docking calculations with ten ligands were performed using the ERβ-prinaberel complex
structure (PDBID: 1X7B) as a template in the Molecular Operating Environment (MOE).
Amber10:EHT was used for the molecular force field in the doking calculations, and FMO
calculations were performed at the FMO2-MP2/6-31G* level. The interaction energies between theligand and the amino acid residues of ERβ were calculated, and the docking scores werere-evaluated. ABINIT-MP was used for the FMO calculation program, and K supercomputer wasused for calculations.
Comparing the docking score by the molecular force field and that by the FMO method, the best
position (pose1) of both methods were the same pose as the crystal structure. I n pose1, stablehydrogen bonds were found between the hydroxyl group of the phenol group and Glu305, and thehydroxyl group of the benzoxazole group and His475. Other poses were found lacking thesehydrogen bonds. In the FMO calculation, the interaction energy between Glu305 and the ligand
with hydrogen bond was about -40 kcal/mol, whereas when the hydrogen bond was not formed, itchanged dramatically to +4 kcal/mol. On the other hand, the force field showed no significant
change in any pose, indicating that the FMO score can properly evaluate hydrogen bonding.
Similarly, docking calculations were performed on 10 compounds with various β -selectivity(mainly flavonoids and steroids), and the interactio ns of β-selective ligands were evaluated.
Correlation was seen between calculated binding energy and IC 50 values. In particular, it was
shown that there is a strong
correlation between His475
and β selectivity.
This research was done inactivities of the FMO drug
design consortium (FMODD).
The results were obtainedusing the K computer (project
ID: hp190119).
Figure 1. Correlation between calculated and experimental
values

P2-09High-speed geometry optimization for protein active site with thefragment molecular orbital method
Koji Okuwaki 1
k-okuwaki@hoshi.ac.jp

Tatsuya Nakano 3nakano@nihs.go.jp

Naoki Watanabe 2
naoki.watanabe@mizuho-ir.co.jp

Yoshio Okiyama 3

Yuji Mochizuki 4, 5

okiyama@nihs.go.jp

fullmoon@rikkyo.ac.jp

Chiduru Watanabe 6

Teruki Homma 6

Kaori Fukuzawa 1, 5

chiduru.watanabe@riken.jp

honma.teruki@riken.jp

k-fukuzawa@hoshi.ac.jp

1

School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2 -4-41 Ebara,
Shinagawa-Ku, Tokyo, 142-8501, Japan
2
Mizuho Information and Research Institute Inc., 2 -3 Kanda Nishiki-cho, Chiyoda-ku,
Tokyo, 101-8442, Japan
3
National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki City,
Kanagawa, 210-9501, Japan
4
Department of Chemistry, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo,
171-8501, Japan
5
Institute of Industrial Science, The University of Tokyo , 4-6-1 Komaba, Meguro-ku, Tokyo,
153-8505, Japan
6
Center for Biosystems Dynamics Research, RIKEN, 1 -7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa, 230-0045, Japan

Keywords: Fragment Molecular Orbital Method, Frozen Domain, Geometry Optimization, Protein–
Ligand Complex

Ratio (FDD/POpt)

Computational time (s)In the analysis of protein–ligand interactions, preparing highly accurate structures are crucial for
precise drug design. Although high-resolution X-ray structures have been obtained in recent years,
several structures still have an ambiguous near the active site. Therefore, refinement of X-ray
structure by using quantum chemical calculation is highly expected. In our ABINIT-MP program
for the fragment molecular orbital (FMO) calculation, partial optimization (POpt) has been possible
for selected residues of the active site. However, because of the high computational cost, POpt has
not been used for a large structure. In this study, frozen domain (FD) and FD dimer (FDD) methods[1] were introduced to realize high-speed geometry optimization.
In this study, we optimized several protein–ligand complexes consist of 89 to 441 residues at the
HF/6-31G* level; Estrogen receptor, Neuraminidase, and G protein-coupled receptor. The optimized
region contained the ligand and surrounding 4 or 5 residues. The computational time of one
optimization cycle is shown in Fig. 1. Note that 2 nodes (56 cores) of TSUBAME3.0 were used forthe calculation. As the size of the entire
FDD
system increased, the calculation time for
FD
POpt increased, but the calculation costs for
25000
5.0
POpt
FD and FDD were not significantly affected.
20000
4.0
Ratio (FDD/POpt)
In the case of a system with 441 residues, thePerformance of FDD was increased 4.2 times
15000
3.0
compared to its POpt.
This research was done in activities of the
10000
2.0
FMO drug design consortium (FMODD,
project ID: hp190119) and was supported by
5000
1.0
AMED-BINDS
under
Grant
Number
JP19am0101113j.
00.0[1] D. G. Fedorov et al., J. Phys. Chem. Lett.,
89
243
334
378
441
Number of Fragments
2 (2011) 282.
F ig. 1. Comparison between POpt and FD method

P2-10Development of FMODB for analyzing protein-ligand
interactions in 2019
Daisuke Takaya 1
daisuke.takaya@riken.jp

Chiduru Watanabe 1,2

Kikuko Kamisaka 1

chiduru.watanabe@riken.jp

kikuko.kamisaka@riken.jp

Teruki Honma1

honma.teruki@riken.jp
1

2

RIKEN Center for Biosystems Dynamics Research 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan

Keywords: FMO, database, IFIE, PIEDA, protein-ligand interaction.
To aid in structure-based drug design, it is important to understand protein-ligand interactions
such as hydrogen bonds, electrostatic interactions, and the van der Waals interactions. Our grouphas been focusing on not only molecular mechanics force fields but also quantum mechanics (QM)
which calculate the effects of donating and withdrawing electrons and can appropriately deal with
the CH-π, π-π, and cation-π interactions. Fragment molecular orbital (FMO) method [1] enables us
to efficiently perform ab initio QM calculations for large-biomolecules, and its fragmentation
technique is capable of analyzing an inter-fragment interaction energy (IFIE).
The IFIE data provides useful information for analyzingprotein-ligand interactions. To analyze IFIE data by a statistical
approach, the accumulation of a large-scale of FMO calculations isessential. Therefore, we have developed FMODB [2], which is adatabase for providing results of FMO calculations anduser-friendly web interfaces to access IFIE/PIEDA data. Currently,
the interface is constructed for analyzing a ligand-binding
interaction; however, the interface will be improved for analyze
various molecular interaction in our plan. In this poster, we introduce new features of the FMO
database and recent applications of the FMODB data.
Acknowledgement
This research was done in activities of the FMO drug design consortium,
http://eniac.scitec.kobe-u.ac.jp/fmodd/index.html. The results of FMO calculations were obtainedusing the K computer (project ID: hp190119). PIEDA calculations were done by using
MIZUHO/BioStation software package. This research was partially supported by Platform Projectfor Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug
Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP19am0101113.
Finally, DT and CW acknowledge JSPS KAKENHI Grant Number 18K06619.
1. K. Kitaura et al., Chem. Phys. Lett. 1999, 313, 701-706.
2. FMODB URL: https://drugdesign.riken.jp/FMODBP2-11Analysis of FMO-based intramolecular interactionenergies for structural stability of apo structures
Kikuko Kamisaka 1
kikuko.kamisaka@riken.jp

Daisuke Takaya 1
daisuke.takaya@riken.jp

1

Shunpei Nagase 1
shunpei.nagase@riken.jp

Hitomi Yuki 1
hitomi.yuki@riken.jp

Chiduru Watanabe 1
chiduru.watanabe@riken.jp

Teruki Honma 1
honma.teruki@riken.jp

RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,Japan

Keywords: FMO, IFIE, SBDD
In structure-based drug design (SBDD), such as in silico drug discovery, it is expected that
understanding the structural stability and the function of the target protein using the three-dimensional
structural simulations leads to rational drug design. However, if there is not enough structural data of
the protein-ligand complexes, it is desirable to understand accurate structural information around thebinding site based on available structures such as apo proteins, before the evaluation of theligand-binding by docking or molecular dynamics simulations.
The fragment molecular orbital (FMO) method is an approach of dividing a large biomolecule
into small fragments in the unit of an amino acid residue and a ligand in a protein-ligand system ora protein-protein system. The FMO method enables to calculate an electronic state of the wholesystem based on quantum mechanics (QM). Inter-fragment interaction energy (IFIE) based on
FMO calculation can accurately estimate not only the intermolecular interaction between the target
protein and the ligand molecule but also the intramolecular interaction of the biomolecule in unitsof an amino acid residue. Identifying which sites are energetically stable and which interactions
may be involved in a ligand binding helps us to understand the capability of the target protein
structure in SBDD.
In recent years, with the improvement of computer performance, analyses of interaction energies
using the FMO method in a protein-ligand system have increased. On the other hand, there are only
a few reports [1][2] of intramolecular interaction analysis regarding structural stability, which isimportant for constructing secondary and tertiary structures for a protein. In this research, we
performed FMO calculations and analyzed how the structural characters such as secondary
structures and the type of an amino acid residue affect the inter- and intramolecular interactionenergies for structural stability. We mainly used apo-protein structures extracted from the protein
data bank. The calculation was performed using the Auto FMO protocol [3]. The FMO calculation
results are registered in the FMODB [4]. This research was done in activities of the FMO drug
design consortium, http://eniac.scitec.kobe-u.ac.jp/fmodd/index.html.
[1] “Visualization analysis of inter-fragment interaction energies of CRP–cAMP–DNA complex basedon the fragment molecular orbital method”, I. Kurisaki, et al., Biophysical Chemistry, 2007 130, 1-9.
[2] “Fragment molecular orbital (FMO) study on stabilization mechanism of neuro -oncological ventral
antigen (NOVA)–RNA complex system”, I. Kurisaki, et al., Molecular Structure (THEOCHEM), 2010,
962, 45-55.
[3] “Development of an automated fragment molecular orbital (FMO) calculation protocol toward
construction of quantum mechanical calculation database for large biomolecules ”, C. Watanabe, et al.,Chem-Bio Informatics Journal, 2019, 19, 5-18.
[4] https://drugdesign.riken.jp/FMODB/

P2-12

Transformation-driven Molecule Generation as Another
Benchmarking Model
Ryuichiro Hara

Makoto Oku

hara@lifematics.co.jp

oku@lifematics.co.jp

Lifematics Inc., Sanshin Hatchobori Bldg. 5F, 2-25-10 Hatchobori, Chuo-ku, Tokyo 104-0032, Japan

Keywords: Virtual screening library, Chemical Space, Metrics of Generative Model
Sane molecule sampling from whole the not-yet-seen galactic drug chemical space has long beensought by computational and medicinal chemists for efficiency demand of their drug discovery
tasks. The space is anticipated to be of well over 10 63 sub-Lipinski molecules in size which makes
it totally impractical for scientists to collect all the possible compounds as a library in hand. On
the other end, modern software technology in conjunction with ever-growing compute resource haspromisingly allowed us to screen through billions of molecules in ligand- and structure-basedmanner, however, such an ironically minuscule set of molecules is required to consort both thediversity and moderate concentration of prospective molecules in a limited reasonable size forsuccess of screening tasks.
Transformations in a substantial set of molecules are collectively and efficiently extracted bycertain graph analysis of molecules, such as matched molecular pair (MMP) 1,2) or finite differential
graph analysis, which in turn applied to self set of molecules to generate molecules on-the-fly.
This methodology in molecule generation is proved to be practically serviceable with reasonable
retrosynthetic convenience for medicinal chemistry study. 3) Compared to such like morechallenging recurrent generative network 4) on molecule string representations, thetransformation-derived de novo molecules inherit sensible motifs (often of chemical importance)from their parents. When applied complicated cuts for retrosynthetic fragmentations, visuallyobserved are balanced core replacement and side chain growth without overly unwanted
divergence off the Lipinski-rule if appropriately filtered. Besides this legitimate expectation withextremely fast process of generating molecules, the question still remains how far the emerging
molecules satisfactorily fill the chemical space to persuade medicinal chemists to be soaked ontrust. Along this context, we herein seek for an effective assessment methodology based on Fréchet
ChemNet (FCN) distance 5) for various set of generated compounds on Guacamol benchmarking
system 6) to measure the diversity in chemical space.
[1] Hussain, J.; Rea, C.; Computationally Efficient Algorithm to Identify Matched Molecular Pairs(MMPs) in Large Data Sets., J. Chem. Inf. Model, 50 (3), 339, 2018.
[2] Dalke, A.; Hert, J.; Kramer, C.; mmpdb: An Open-Source Matched Molecular Pair Platform forLarge Multiproperty Data Sets, J. Chem. Inf. Model, 58 (5), 902, 2018.
[3] León, A. de la V.; Bajorath, J.; Matched molecular pairs derived by retrosynthetic
fragmentation, Med. Chem. Commun., 5, 64, 2014.
[4] Gupta, A.; Müller, A. T.; Huisman, B. J. H.; Fuchs, J. A.; Schneider, P.; Schneider G;
Generative Recurrent Networks for De Novo Drug Design, Mol Inf., 37, 1700111, 2018.
[5] Preuer, K.; Renz, P.; Unterthiner, T.; Hochreiter, S.; Klambauer, G.; Fréchet ChemNet Distance:
A Metric for Generative Models for Molecules in Drug, J. Chem. Inf. Model, 58 (9), 1736, 2018.
[6] Brown, N.; Fiscato, M.; Seglar, M. H. S.; Vauchar, A. C.; GuacaMol: Benchmarking Models forde Novo Molecular Design, J. Chem. Inf. Model, 59 (3), 1096, 2019.

P2-13

Inter-lobe motion of EGFR kinase: Determinants ofstructural variation in the crystal structures
Kei Moritsugu
moritugu@yokohama-cu.ac.jp

Yoshihiko Nishino
nisshy155416@yokohama-cu.ac.jp

Akinori Kidera
kidera@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan

Keywords: Protein kinase, EGFR, KLIFS database, Structural bioinformatics, Molecular dynamics
simulation, MSES
Protein kinases play important roles in cellular signaling processes, and have been one of thebest studied drug targets. The structural information of protein kinases can be retrieved from a
database based on protein data bank, KLIFS, compiling comprehensive data on protein-druginteractions for almost 300 kinase species that bind more than 3,000 distinct compounds. A wholevariety of crystal structures in the database gives us a marked impression that protein kinase isremarkably flexible or intrinsically dynamic, and leads us to a naïve question: What are thedeterminants of these complex structural variation? Drug interactions, mutations, crystal
environment, or some other causes?
In this study, we try to answer this question by focusing on the kinase domain of epidermal
growth factor receptor (EGFR) as the target (a set of crystal structures consisting of 148 PDB
entries and 206 domain structures are deposited in KLIFS), using database analyses and molecular
dynamics simulations. EGFR kinase is a receptor tyrosine kinase that has been extensively studied
for understanding the mechanism of activation occurring during the dimer formation and fordeveloping inhibitors for cancer treatments.
We found that the inter-lobe motion between N-lobe and C-lobe constitutes the most significant
structural variation, which can definitely be described by opening/closing and twisting motions of
the beta-sheet in N-lobe against C-lobe. Hinge/linker moves collectively with these lobe motions,affecting the binding pose of ATP competitive inhibitors. The descriptors of the lobe motion allow
us to classify the 206 kinase structures rigorously into nine groups. Molecular dynamic simulation
using the enhanced sampling method (MSES) revealed that a monomer of EGFR kinase stays in theinactive state due to the interaction between a short helix formed in A-loop and N-lobe, whereas
αC-helix largely fluctuates between the αC-in and αC-out states. Asymmetric dimer, regarded as
the active state, maintains large structural variation in the 121 crystal structures, due to strong
crystal packing that forms various head-to-tail chains with three-fold symmetry (active) – four-foldsymmetry (inactive) in the crystal. Catalytic activation is understood to be regulated byexchanging the salt-bridge partner of K721 from D831 (inactive) to E738 (active). DFG-out
structures occur in the mutants of L834R/V924R (or MIG6 binding) to keep the interaction
between A-loop and N-lobe. These findings were firstly derived from the present survey of thewhole EGFR crystal structures, and yield quite different view from the conventional studies
focusing only on the local structures close to the drug binding sites.

P2-14

AUTOMATED PREDICTIVE MODELING OF
BIOACTIVES

1
2Toshikazu DEWA 1Matthew CLARK 2

t.dewa@elsevier.com

m.clark@elsevier.com

Elsevier Japan KK. 4F, 1-9-15 Higashi-Azabu, Minato-ku, Tokyo 106-0044, JAPANElsevier Inc., 1600 John F Kennedy Boulevard, Suite 1600, Philadelphia, PA 19103, USA

Keywords: Drug Discovery, Predictive Model, QSAR, Automation
We built a KNIME workflow to generate prediction models using bioactivity data stored in
Elsevier Reaxys Medicinal Chemistry. By combining Reaxys API and KNIME, one can
automatically generate predictive models with various search criteria and descriptors for any target
molecules. In this presentation, we will present use case of the prediction models.

P2-15
Insights into the Mechanism of NA-I117V-MediatedOseltamivir Resistance in H5N1 Avian Influenza Virus
Mohini Yadav 1
s1879602EC@s.chibakoudai.jp

Manabu Igarashi 2
igarashi@czc.hokudai.ac.jp

Norifumi Yamamoto 1
norifumi.yamamoto@p.chibakoudai.jp

12

Department of Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma,
Narashino, Chiba 275-0016, Japan
Research Center for Zoonosis Control, Hokkaido University, Kita-20, Nishi-10, Kitaku, Sapporo, Hokkaido 001-0020, Japan

Keywords: Influenza, Neuraminidase, H5N1, NA-I117V, Drug Resistance, Oseltamivir
Resistance
Influenza is a contagious respiratory illness that infects the nose, throat, and lungs,
causing mild to severe illness. Glycoproteins, hemagglutinin (HA) and neuraminidase (NA),of the influenza virus play essential roles in its replication process. HA binds to sialic acid(SA) at the end of a sugar chain present on the surface of a host cell, and then the virus istaken into the cell. NA on the virus surface plays a role of scissors to cleave the bond
between SA and the sugar chain, which allows the daughter virus to liberate to begin
infecting the surrounding cells.
Anti-flu drugs that inhibit the interaction between NA and SA have been d eveloped.
Currently, four types of NA inhibitors (oseltamivir, zanamivir, peramivir, laninamivir)
have been used for the treatment of influenza in Japan. But resistance against available
anti-flu drugs is emerging rapidly. Hence, it’s crucial to find out the detailed mechanismof drug resistance to develop anti-flu drugs which are less prone to resistance.
Most of the receptor mutations that lead to drug resistance also affect its enzymatic
activity to a significant extent. But an Ile-to-Val substitution at position 117 in influenza
A virus subtype H5N1 neuraminidase (NA-I117V) causes a reduction in susceptibility tooseltamivir carboxylate while maintaining its enzymatic activity. 1
In this study, to clarify the binding affinities of SA to the NA-I117V, we computed thecorresponding binding free-energies using the molecular mechanics Poisson-Boltzmann
surface area (MM-PBSA) method. To clarify the influence of the I117V mutation to the
enzymatic reactivity of NA, we analyzed the minimum energy path (MEP) along with the
enzymatic reaction using the QM/MM hybrid method combining quantum mechanics (QM)
and molecular mechanics (MM).
[1] Takano, R., Kiso, M., Igarashi, M., Le Q.M., Sekijima M., Ito, K., Takada, A., Kawaoka,
Y., Molecular Mechanisms Underlying Oseltamivir Resistance Mediated by an I117V
Substitution in the Neuraminidase of Subtype H5N1 Avian Influenza A viruses, The
Journal of Infectious Diseases, 207:89-97, 2013.

P2-16

Biotherapeutics modeling and developability assessment
by Bio-MOE
Kentaro Kamiya 1 , Takashi Ikegami 1 , Ryoichi Kataoka 1 , Nels Thorsteinson 2 , Will Long 2
kamiya.kentaro@molsis.co.jp
12Life Science Department, MOLSIS Inc., 3 -19-9 Hacchobori, Chuo-ku, Tokyo 104-0032, Japan
Chemical Computing Group ULC, 1010 Sherbrooke St. West, Suite #910, Montreal QC,
Canada H3A 2R7

Keywords: Antibody modeling, Protein property calculation, Liability detection
In recent years, the development of various biotherapeutics including antibody drugs has beensignificantly increased. It is important to estimate molecular interactions and physicochemical
properties with the 3D structure at the early stage of drug discovery in order to clarify problems
and lead to rational drug design. Especially, it is crucial for developing biotherapeutics to control
physicochemical properties relevant to aggregation, solubility and viscosity. To design a better
biotherapeutics, it is necessary to predict sites where the chemical modifications such as degradation
and/or isomerization should be introduced.
The antibody modeling application implemented in Molecular Operating Environment 1 (MOE)
automatically assigns the appropriate templates to each CDR and framework of the target antibody
sequence and builds the homology models (Fig. 1) from multiple antibody sequences in a database.
To investigate the homology models, MOE detects the protein surface patches (Fig. 2) closely
related to aggregation and solubility and calculate s over 30 kinds of protein properties.
Bio-MOE, a custom application package of MOE, contains a series of applications useful forbiologics modeling. It can detect the protein surface patches under user defined pH values, calculate
protein properties considering flexible conformations of CDR loops, detect liabilities such as
chemical modification sites, search humanized antibody sequences from the human germline
sequences and so on.
In this study, we present various in silico methods for antibody modeling and developability
assessment with MOE and Bio-MOE.

Figure 1. Humanized antibody modeland visualized mutation sites.

Figure 2. 3D and 2D diagrams of protein patches at each pH
calculated from modeled antibody structure.

[1] Molecular Operating Environment (MOE), 2018.01; Chemical Computing Group ULC, 1010
Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2019.

P2-17

Design and Implementation of an Easy-to-Use High
Performance Computing Service System for Large-Scale
Molecular Dynamics Simulations
Yoshio Nakao 1
ynakao@fujitsu.com

Toyokuni Sumita 2
sumita.toyokuni@fujitsu.com
1
2

Tetsuya Ito 1
itou.tetsuya@fujitsu.com

Yusuke Sugihara 2
ysugihara@fujitsu.com

Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan
Fujitsu Kyushu Systems Limited, 5-13 Higashihie 1-Chome, Hakata-ku, Fukuoka 812-0007, Japan

Keywords: Molecular Dynamics Simulation, Absolute Binding Free Energy Calculation, High
Performance Computing
In recent years, computational power has been increasing rapidly by such a rate of ten times fasterevery three years, and also large-scale computational resources are available as of spreading cloudcomputing services. In this environment, we have developed a high performance computing (HPC)
service system for pharmaceutical researchers to conduct large-scale simulations easily.
The system is designed so that users unfamiliar with HPC can easily use an absolute binding freeenergy calculation solution [1-2] , called MAPLECAFEE V2. The absolute binding energy calculation
based on molecular dynamics simulation [1-4] is a promising tool for in silico drug design, however
it requires sophisticated knowledge and skills on application settings and on parallel computing
tuning. For example, it is required to execute efficiently se veral tens of molecular dynamics
simulation processes corresponding to different thermodynamic states by tuning execution
parameters concerning parallel computing while each of that process needs to appropriate
simulation parameter settings by each steps of different functions (e.g., energy minimization,
temperature and pressure control, etc.).
We then use a workflow framework [6] to describe simulation flows in an automatically executable
form, and have developed a calculation process management function on the server. With provided
workflow data, the user can execute calculation with the KNME workb ench on his/her desktop PC
while the calculation processes are executed and maintained by the HPC system.
In the poster, we introduce the system and its features in details with several usage examples.
[1] Fujitani, H., et al., Direct calculation of the binding free energies of FKBP ligands. Journal ofChemical Physics, AIP, 123:084108, 2005.
[2] Fujitani, H., et al., Massively Parallel Computation of Absolute Binding Free Energy with
Well-Equilibrated States. Physical Review E, APS, 78:021914, 2009.
[3] Boresch, S., et al., Absolute binding free energies: a quantitative approach for their calculation.
Journal of Physical Chemistry B, ACS, 107:9535-9551, 2003.
[4] Wang, J., et al., Absolute binding free energy calculations using molecular dynamics
simulations with restraining potentials. Biophysical Journal, BPS, 91:2798 -2814, 2006.
[5] Fujitani, H., et al., High-level ab initio calculations to improve protein backbone dihedralparameters. Journal of Chemical Theory and Computation, ACS, 5:1155-1165, 2009.
[6] https://www.knime.com/

P2-18Development of a Library Containing Billions of VirtualCompounds
Aki Hasegawa 1

Yasuhiro Fujihara 2

Noriaki Okimoto 1

Yoshinori Hirano 1

ahasegawa@riken.jp

fujihara@rist.or.jp

okimoto@riken.jp

hirano@riken.jp

Gentaro Morimoto 1

Makoto Taiji 1

gentaro.morimoto@riken.jp

taiji@riken.jp

1

2

3

Kimito Funatsu 3
funatsu@chemsys.t.u-tokyo.ac.jp

Laboratory for Computational Molecular Design, RIKEN Center for Biosystems Dynamics
Research (BDR), Bldg. B, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan
Research Organization for Information Science and Technology (RIST) Tokai, 2-4, Shirakata,
Tokai-mura, Ibaraki 319-1106, Japan
Department of Chemical System Engineering, School of Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Virtual library, Virtual screening, Big data
Virtual compound libraries of various scales have been developed in private, public, commercial
or non-commercial form so far [1,2,3]. In virtual screening from these libraries, it is important how
to find promising lead compounds against drug target proteins at low cost and efficiency. However,there is a problem that the hit rate of lead compounds is still low, so it is necessary to increase thisproblem. Recently, it has been reported that it is possible to find compounds comparable to highly
optimized drugs by increasing the number of compounds contained in the virtual library while
including many more diverse structures [4,5]. One solution is considered to be a significant
increase in the number of compounds. In addition, since the hit compound needs to be actuallyavailable, at least enough information to evaluate the synthesis route of the compound must be
obtained. Based on the above, we have developed a library containing billions of virtualcompounds with hints on synthesis routes. We calculated predictive assay values for all compounds
in the library using a program [6] that employs deep learning techniques. We also implemented afunction to export synthetic route information to a high-speed transition state evaluation system[7]. We will introduce a virtual library as part of a platform that links the flow from virtual
screening to synthesis route evaluation.
[1] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enumeration of 166 Billion
Organic Small Molecules in the Chemical Universe Database GDB-17. J. Chem. Inf. Model.
2012, 52 (11), 2864–2875.
[2] Peng, Z. Very Large Virtual Compound Spaces: Construction, Storage and Utility in Drug
Discovery. Drug Discovery Today: Technologies. September 2013, pp e387-94.
[3] Hoffmann, T.; Gastreich, M. The next Level in Chemical Space Navigation: Going Far beyond
Enumerable Compound Libraries. Drug Discov. Today 2019, 24 (5), 1148–1156.
[4] Gloriam, D. E. Bigger Is Better in Virtual Drug Screens. Nature 2019, 566 (7743), 193–194.
[5] Lyu, J.; Wang, S.; Balius, T. E.; Singh, I.; Levit, A.; Moroz, Y. S.; et al. Ultra-Large Library
Docking for Discovering New Chemotypes. Nature 2019, 566 (7743), 224–229.
[6] Hamanaka, M.; Taneishi, K.; Iwata, H.; Ye, J.; Pei, J.; Hou, J.; Okuno, Y. CGBVS-DNN:
Prediction of Compound-Protein Interactions Based on Deep Learning. Mol. Inform. 2016.
[7] Hori, K.; Sumimoto, M.; Murafuji, T. Quantum Chemistry-Assisted Synthesis RouteDevelopment. AIP Conference Proceedings; AIP Publishing LLC, 2015; Vol. 1702, p 090019.
This work was supported by JST CREST Grant Number JPMJCR1311 including AIP challenge program,and AWS Research Grants.

P3-01

IsdH: mechanism of action and novel antibacterial strategies
Sandra Valenciano Bellido 1

Jose Manuel Martínez Caaveiro 2

s-valenciano@protein.t.u-tokyo.ac.jp

jose@phar.kyushu-u.ac.jp

Kouhei Tsumoto 1
tsumoto@bioeng.t.u-tokyo.ac.jp
12Faculty of Bioengineering, University of Tokyo, Japan
Kyushu University, Fukuoka, Japan

Keywords: Staphylococcus aureus, IsdH linker-NEAT3, protein structure, heme extraction
mechanism, heme rotation, antibody, VHH, inhibition.
The bacterium Staphylococcus aureus is the major cause of deadly hospital-acquired infections
worldwide 1 . The Isd (Iron surface determinant) system is a group of proteins that obtain iron fromthe host organism 2,3 , helping the bacterium to proliferate, and therefore a promising antibacterial
target. The protein IsdH binds to hemoglobin and acquires the heme 4,5 . IsdH comprises three NEAT
(Near-iron transporter) domains connected by linkers of unknown function 6,7 . The objectives ofthis study are deciphering the molecular mechanism of heme extraction, to explain the role of thelinker region and to obtain an antibody that recognizes IsdH specifically, inhibiting the heme
binding. The first evidence of the structure of IsdH linker-NEAT3 bound to heme was reported inthis research. The heme extraction mechanism of IsdH has bee n deciphered and the role of thelinker as key for heme extraction has been explained through structure analysis. A novel VHH thatbinds to IsdH linker-NEAT3 specifically and that inhibits the heme binding was obtained. The
complex crystal structure of the VHH bound to linker-NEAT3 was resolved and it was stablishedthat the obtained antibody specifically recognizes the heme binding pocket.

[1] Mody, L. et al. Multidrug-resistant Organisms in Hospitals: What Is on Patient Hands and in
Their Rooms? Clin. Infect. Dis. 1–8 (2019). doi:10.1093/cid/ciz092[2] Smith, A. D. & Wilks, A. Extracellular heme uptake and the challenges of bacterial cell
membranes. Curr Top Membr. 1–28 (2013). doi:10.1016/B978-0-12-394390-3.00013-6.
Extracellular[3] Hazmanian, S. K. et al. Passage of heme-iron across the envelope of Staphylococcus aureus.
Science.. 299, 906–909 (2003).
[4] Vu, N. T. et al. Selective binding of antimicrobial porphyrins to the heme -receptor
IsdH-NEAT3 of Staphylococcus aureus. Protein Soc. 22, 942–953 (2013).
[5] Dickson, C. F. et al. Structure of the Hemoglobin-IsdH Complex Reveals the Molecular Basisof Iron Capture by Staphylococcus aureus *. J. Biol. Chem. 289, 6728–6738 (2014).
[6] Sjodt, M. et al. Energetics underlying hemin extraction from human hemoglobin by
Staphylococcus aureus. J. Biol. Chem. 293, 6942–6957 (2018).
[7] Villareal, V. A. et al. Transient Weak Protein À Protein Complexes Transfer Heme Across the
Cell Wall of Staphylococcus aureus. J. Am. Chem. Soc. 14176–14179 (2011).

P3-02

Effect of binning size of XFEL Diffraction Patterns on the
resolution of reconstructed 3D-molecular structure
Miki Nakano 1
miki.nakano@riken.jp

Osamu Miyashita 1
osamu.miyashita@riken.jp

Florence Tama 1,2,3
florence.tama@riken.jp

1

2

3

RIKEN Center for Computational Science, 6-7-1, Minatojima-minamimachi, Chuo-ku, Kobe,
650-0047, Japan
Department of Physics, Graduate School of Science, Nagoya University, Furo -cho, Chikusa-ku,
Nagoya, 464-8602, Japan
Institute of Transformative Bio-Molecules, Nagoya University, Furo-cho, Chikusa-ku, Nagoya,
Aichi 464-8602, Japan

Keywords: Single particle analysis, coherent X-ray diffraction imaging, phase retrieval
Single-particle analysis (SPA) by X-ray free electron laser (XFEL) is a novel method that canobserve biomolecules and living tissue difficult to crystallize in a state close to nature. To
reconstruct three-dimensional (3D) molecular structure from two -dimensional (2D) XFEL
diffraction patterns, we have to retrieve phase information of the 3D-diffraction intensity
distribution assembled from 2D-diffraction patterns. Usually, experimental diffraction patterns arebinned before analysis to increase signal and noise ratio. However, binning process would makethe loss of detailed information within the binned pixels. In this study, we examined the optimalbinning size to keep the quality of restored structures. Interpolation parameters used to assemble
2D-diffraction patterns into 3D-diffraction intensity distribution also should be adjusted. We foundthat resolution of restored molecular structure is sensitive to the interpolation parameters but
insensitive to binning size. Using the optimal parameter set, a linear oversampling ratio of aroundfour is found to be sufficient.

P3-03Mail-in X-ray Diffraction Data Collection for
High-throughput Crystal Structure Determination
Hideaki Niwa 1
hideaki.niwa@riken.jp

1

Shin Sato 1

Kentaro Ihara 1

shin.sato@riken.jp

kentaro.ihara@riken.jp

Takashi Umehara 1

Mikako Shirouzu 1

takashi.umehara@riken.jp

mikako.shirouzu@riken.jp

RIKEN Center for Biosystems Dynamics Research, 1 -7-22 Suehiro-cho, Tsurumi, Yokohama
230-0045, Japan

Keywords: X-ray crystallography, Drug discovery, Protein-ligand complex, Inhibitors
Structure-based drug design needs high-throughput structure determination of the target protein
complexed with a variety of drug candidate molecules. Since 2010, RIKEN has been running a
program of Drug Discovery and Medical Technology Platforms (DMP) in order to bridge
academia-based researches and pharmaceutical industry for drug development. As the Drug
Discovery Structural Biology Platform Unit of RIKEN DMP based in Yokohama, we are equippedwith a pipeline for protein expression, crystallization and structure analysis. For crystal structure
determination, we have been collecting X-ray diffraction data from dozens of crystals at a time,
mainly using BL26B2 of SPring-8 in a mail-in system with the help of the beamline staff at RIKEN
SPring-8 center. In this system, we remotely examine X-ray diffraction images from diffraction
tests and input data collection conditions before starting automated overnight data collection. Overthe last eight years, we have sent about 7,800 crystals and collected about 2,300 data sets. In thispresentation, we will introduce the mail-in data-collection system, as well as our protein
production and crystallization pipeline. Furthermore, we report representative studies ofstructure-based drug design targeting epigenetics-related proteins.

P3-04Structural basis for the binding of histo-blood group antigens
to the norovirus capsid protein
Tomomi Kimura-Someya 1

Yuichi Someya 2

tomomi.someya@riken.jp

someya@niid.go.jp

Kazushige Katsura 1

Kazutaka Murayama 1,3

Miyuki Kato-Murayama 1
miyuki.kato@riken.jp

Mikako Shirouzu 1

kazushige.katsura@riken.jp kazutaka.murayama.d4@tohoku.ac.jp mikako.shirouzu@riken.jp
1

RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan2
Department of Virology II, National Institute of Infectious Diseases, 4-7-1 Gakuen,
Musashi-Murayama, Tokyo 208-0011, Japan
3
Tohoku Univ. School of Biomedical Engineering, 6-6-12 Aramaki Aza Aoba, Aoba-ku, Sendai,
Miyagi 980-8579, Japan

Keywords: norovirus, capsid, X-ray crystallography, histo-blood group antigen
Noroviruses are major causative agents of acute non-bacterial gastroenteritis in humans
worldwide. There are neither antiviral therapeutic agents nor vaccines for the disease. Several
genogroups (GI to GVII) are currently identified, among which GI and GII noroviruses are major
pathogens for humans. The Vancouver isolate is the representative strain of GI.9, which is the
genotype with the shortest history within GI. It is well known that histo-blood group antigens(HBGAs) play a critical role in norovirus infection and that the HBGA recognition profile varies
by genotype. It was found by ELISA-based assay using the synthetic HBGAs and virus-likeparticles (VLPs) that the GI.9 norovirus preferentially bound to Lewis antigens, but not to any of
ABH antigens.
The X-ray crystal structure of the protruding (P) domain dimers derived from the Vancouverstrain was determined in the presence or absence of Lewis b tetrasaccharide or Lewis x
trisaccharide. As suggested by the difference in amino acid sequences, it was apparent that the P
domain structure was different from the corresponding domains from other GI strains, indicatingthat the P2 subdomain in the P domain is likely responsible for antigenicity of each norovirus
strain. Nevertheless, the HBGA binding site was located at a position equivalent to those for otherGI strains, which was formed on the P2 subdomain. The crystal structures clearly explained why
the Vancouver strain favored Lewis antigens. Accumulating the information on the molecular
structure of VLPs [1,2] and/or P domains [3,4] from various strains will help us design antiviral
agents and vaccines which are hopefully effective against a wide variety of norovirus genotypes.
[1] Prasad BV, Hardy ME, Dokland T, Bella J, Rossmann MG, Estes MK. X-ray crystallographic
structure of the Norwalk virus capsid. Science 286:287–290, 1999.
[2] Jung J, Grant T, Thomas DR, Diehnelt CW, Grigorieff N, Joshua-Tor L. 2019. High-resolution
cryo-EM structures of outbreak strain human norovirus shells reveal size variations. Proc Natl
Acad Sci USA 116:12828-12832, 2019.
[3] Choi JM, Hutson AM, Estes MK, Prasad BV. Atomic resolution structural characterization ofrecognition of histo-blood group antigens by Norwalk virus. Proc Natl Acad Sci USA
105:9175-9180, 2008.
[4] Kubota T, Kumagai A, Ito H, Furukawa S, Someya Y, Takeda N, Ishii K, Wakita T, Narimatsu H,
Shirato H. Structural Basis for the Recognition of Lewis Antigens by Genogroup I Norovirus. J
Virol 86:11138–11150, 2012.

P3-05

Crystal structures of GAS41 YEATS domain in complex
with acylated histone peptides
Masaki Kikuchi 1
masaki.kikuchi@riken.jp

1

Takashi Umehara 1
Takashi.Umehara@riken.jp

RIKEN Center for Biosystems Dynamics Research,
1-7-22 Suehiro-cho
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: YEATS, acylated lysine, GAS41
The YEATS domain, named after five proteins (Yaf9, ENL, AF9, Taf14 and Sas5) where it was
originally found, is evolutionarily conserved from yeast to human. The human YEATS
domain-containing proteins (GAS41, AF9, ENL and YEATS2) are found in chromatin-modifying
and transcription regulatory complexes, this domain is proposed to function in histone andchromatin interactions. GAS41 is frequently amplified in various types of cancer and is requiredfor cancer cell proliferation, survival, and transformation. Recently, the YEATS domains of
GAS41 were reported to recognize acyl-lysine, including Kac, Kcr and Ksuc, modifications inhistone H3. The GAS41 in complex with Tip60 or SRCAP colocalizes with acetylated histone H3on the promoters of actively transcribed genes. It was suggested that GAS41 recruits chromatin
remodeling factor to gene promoters and promotes histone H2A.Z deposition through binding
acylated lysines. Moreover, GAS41 functions as an oncogene also plays a role in repressing thep53 tumor suppressor pathway and activating Wnt/β-catenin signaling pathways in the nucleus
through binding acylated lysines. However, the acyl-lysine substrate preference of GAS41 and thestructural information of substrate-recognition sites are yet to fully understood. Here, we report thebinding of GAS41-YEATS to modified histone peptides with different acetyl groups using amodified histone peptide array, as well as the crystal structure of GAS41 YEATS domain incomplex with actylated histone peptides.

P3-06

Crystal structure of the Hepatitis B virus core protein
complexed with a novel drug candidate
Wakana Iwasaki 1,2

Kazushige Katsura 1,2

Kenji Ogawa 3

Soichi Kojima 2,4

wiwasaki@riken.jp

kazushige.katsura@riken.jp

kkogawa@riken.jp

skojima@riken.jp

Hideyuki Matsunami 2

Tomomi Uchikubo 1,2

Takeshi Yokoyama 1,2

hideyuki.matsunami@riken.jp

tomomi.kamo@riken.jp

takeshi.yokoyama@riken.jp

Hideki Shigematsu 2,5
hideki.shigematsu@riken.jp
1

2

3

4

5

Yuri Tomabechi 1,2
yuri.tomabechi@riken.jp

Mikako Shirouzu 1,2
mikako.shirouzu@riken.jp

RIKEN Center for Biosystems Dynamics Research, Suehiro -cho, Tsurumi-ku, Yokohama
230-0045, JapanRIKEN Center for Life Science Technologies, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045,
Japan
RIKEN Center for Sustainable Resource Science , 2-1, Hirosawa, Wako, Saitama 351-0198,
Japan
RIKEN Center for Integrative Medical Sciences, 2-1, Hirosawa, Wako, Saitama 351-0198,
Japan
RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148 Japan

Keywords: X-ray crystallography, electron microscopy, virus, capsid
Core protein of the hepatitis B virus (HBc) forms a dimer, and 120 HBc homodimers assemble
into an icosahedral capsid that packages the viral pregenomic RNA. HBc not only constitutes
capsids but plays multiple essential roles in HBV lifecycle. Therefore, HBc is a promising drug
target. HBc can be divided into the N-terminal assembly domain (residues 1-149) and the highly
basic C-terminal domain (residues 150-183) that is responsible for interaction with RNA. The
mutant consisting of the N-terminal assembly domain only (Cp149) is known to be sufficient for
self-assembly into capsid particles. In recent years, antiviral small molecules targeting HBc, called
core protein allosteric modulators (CpAMs) have been developed. CpAMs enhance HBc (orCp149) assembly, forming empty capsids or large aberrant structures.
Ogawa et al established a novel high-throughput method for screening drug candidates which
affect Cp149-Cp149 interactions (unpublished). Some of the hit compounds of the method
significantly inhibited HBV DNA replication. To elucidate the antiviral mechanism of thesecompounds, we examined the effect of these compounds on Cp149-Cp149 interaction using
electron microscopy and dynamic light scattering. After incubating Cp149 in proper salt
concentration, capsids were observed by negative staining electron microscopy. On the other hand,
Cp149 dimer in buffer of low salt concentration did not assemble into structured polymer. However,after incubating Cp149 dimer with the hit compounds in low -salt buffer, morphologically “normal”
capsids appeared. The compound strongly promotes assembly of Cp149, and it can even induce
polymerization of the capsid assembling deficient mutant Cp149 (F23A).
Furthermore, we determined the crystal structure of Cp149 in complex with the compound. The
compound binds in the hydrophobic pocket of interdimer interface of Cp149. This binding site andthe above-mentioned effects on Cp149 assembly indicate that the hit compounds satisfy thefeatures of the CpAMs that promote forming empty capsids and prevents encapsidation ofpregenomic RNA.

P3-07Structural insights into cyclic peptides in complex withtarget proteins
Satoshi Sogabe

satoshi.sogabe@axcelead.com

Axcelead Drug Discovery Partners, Inc., Molecular Screening,
26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa, 251-0012, Japan

Keywords: cyclic peptide, X-ray crystallography, crystal structure
Recently, cyclic peptides have emerged as one of new therapeutic modalities for the modulationof tough targets in drug discovery. Some attractive drug targets for therapeutic intervention havebeen considered undruggable because protein-protein interaction (PPI) interfaces are typicallybroad and/or flat binding pockets for enzyme or receptor targets. As a result, drugging these
intractable targets has been deemed to be challenging because of a limited understanding ofstructural insights of protein-ligand interactions. In general, cyclic peptides adopt flexible
conformations, resulting that it is difficult to predict active conformations by computational
approach. X-ray crystallography facilitates the identification of cyclic peptide conformation andleads to motivate medicinal chemists for the structural-based design of small molecule inhibitors.
We determined crystal structures of several drug targets to contribute to lead generation andoptimization.
The phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4) is an enzyme that reduces
lipid hydroperoxides in lipid membranes and has been investigated as a target molecule thatinduces iron-dependent cell death (ferroptosis) selectively in cancer cells. We successfully
identified one cyclic peptide that binds to near its catalytic site and two cyclic peptides that bind toanother site on GPX4. 1 This is the first example reporting GPX4 inhibitory peptides and structural
information of the protein surface.
The Ras proteins play important roles in cell differentiation, proliferation, and survival. We
performed structural determination of K-Ras(G12D) in complex with GDP and a cyclic peptide,
KRpep-2d. 2 The structure revealed that the peptide binds near Switch II and allosterically blocks
protein–protein interactions with the guanine nucleotide exchange factor. This structural analysis
identified a unique allosteric binding site for the future design of small-molecule inhibitors.
These structures revealed that cyclic peptides exhibit diverse binding conformations and are
adjusted for a variety of molecular surfaces of targets, taking advantage of their flexibility. Cyclic
peptides are considered to be a potential modality, although cell permeability and bioavailability
are key limitations. Our results provide valuable structural insight to understand molecularproperties of cyclic peptides and could expand the knowledge for small molecule design principles
with innovative strategies.
[1] Sakamoto, K., Sogabe, S., Kamada, Y., Matsumoto, S., Kadotani, A., Sakamoto, J., and Tani, A.
Discovery of GPX4 inhibitory peptides from random peptide T7 phage display and subsequent
structural analysis. Biochem. Biophys. Res. Commun. 482:195−201, 2017.
[2] Sogabe, S., Kamada, Y., Miwa, M., Niida, A., Sameshima, T., Kamaura, M., Yonemori, K.,
Sasaki, S., Sakamoto, J., and Sakamoto, K. Crystal structure of a human K‑Ras G12D mutant incomplex with GDP and the cyclic inhibitory peptide KRpep-2d. ACS Med. Chem. Lett.
8:732−736, 2017.

P3-08Prospects for applying in-silico crystal structure prediction
to drug development
Okimasa Okada 1 , Hiroomi Nagata 2 , Aaron J. Nessler 3 , Michael J. Schnieders 3
12
3

Mitsubishi Tanabe Pharma Corporation, Production Technology & Supply Chain Management
Division,
Mitsubishi Tanabe Pharma Corporation, Discovery Technology Laboratories
University of Iowa, Biomedical Engineering

Keywords: Crystal structure prediction, Molecular simulation, Polarizable force field, Polymorphs
An organic compound forms different crystals (polymorphs), which influences physicalproperties such as solubility and stability. It is known that 80 % of drug compounds havepolymorphs. So, it is meaningful to study polymorphism in drug development and quality controlof medicine.
Elaborate polymorph screening experiments are performed to select the most suitable crystal
form for each drug. However, screening with all of crystallization conditions is difficult. For thisreason, pharmaceutical companies sometimes encounter a sudde n emergence of a new stable form
to cause serious damage in business.
Recently, in-silico crystal structure prediction (CSP) is getting attention to support theexperiments. Due to enhancement of computational power, interatomic interactions can beevaluated accurately, which made CSP promising in practical use [1-3]. In the methods for CSP,
stable packing structures with low lattice energy and high density are extracted from a large
number of candidates produced by static methods (ex. Monte Carlo). How ever, free energy
including an entropy term would be preferable for the evaluation of stability.
In this study, we predicted packing structures of a drug compound by molecular dynamics
simulations with polarizable force field [4]. Experimental and obtained crystal structures arecompared using lattice energy, density, and packing structures. Sublimation free energy is also
evaluated. Based on the results, we would like to discuss applicability of the in -silico CSP andissues to be solved to support experimental polymorph screening.
[1] https://www.ccdc.cam.ac.uk/Community/initiatives/CSPBlindTests/[2] M. Reilly et al. Acta Cryst. B72, 439-459, 2016[3] Elizabeth Gibney, Nature, 527, 20-21, 2015[4] https://ffx.biochem.uiowa.edu/

P3-09Influence Analysis of Amino Acid Residues on
Protein Functions using Attention-based Neural Networks
Takato Kameyama 1
takato.kameyama@akane.waseda.jp

Shigeharu Mukai 1
shigeharu.mukai@moegi.waseda.jp

Risa Igarashi 1

Masaki Aota 1

risa.igarashi@toki.waseda.jp

masaki.aota@ruri.waseda.jp

Hideaki Kanehara 1
hideaki.kanehara@toki.waseda.jp

Daisuke Kiga 1
kiga@waseda.jp

Noboru Murata 1
noboru.murata@eb.waseda.ac.jp

1

Waseda University, Shinjuku-ku, Tokyo, 169-8555, Japan.

Keywords: neural network, attention mechanism, green fluorescent protein
Analyzing the influence of amino acid residues on the protein function is often a repeated processof trial and error, heavily relying on experimenters’ knowledge. However, those experiments
require a considerable amount of time and cost, therefore it is expected to make good use of
relevant experimental data to solve the target task. In this study, we propose a method to analyze
the influence of amino acid residues on the function using attention-based neural networks
utilizing an accumulated dataset.
We assume that the function of the protein is determined by a set of combinations between
residues of amino acid sequences of the protein, particularly some combinations have a strong
influence. To distill important relations, we first extract the features of the residues withconvolutional neural networks, which can take into account the short-range relations and canhandle sequences in different length [1, 2]. Then, we utilize attention-based regression analysis on
pair-wise correlations of the features to compute the long-range relations among residues.
We analyze the fluorescence intensity of green fluorescent protein (GFP) based on a dataset by
Sarkisyan et al [3]. The dataset contains a lot of different amino acid sequences of GFP variants
with their individual fluorescence intensity. Our proposed model suggests the change offluorescence intensity highly depends on the number of amino acid replacements, and thefluorescence intensity of GFP is strongly affected by its chromophore and the combinations ofneighboring residues.
[1] Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu. Accurate de novo
prediction of protein contact map by ultra-deep learning model. PLoS computational
biology, Vol. 13, No. 1, p. e1005324, 2017.
[2] Zhen Li and Yizhou Yu. Protein secondary structure prediction using cascaded
convolutional and recurrent neural networks. arXiv preprint arXiv:1604.07176, 2016.
[3] Karen S Sarkisyan, Dmitry A Bolotin, Margarita V Meer, Dinara R Usmanova, Alexander
S Mishin, George V Sharonov, Dmitry N Ivankov, Nina G Bozhanova, Mikhail S Baranov,Onuralp Soylemez, et al. Local fitness landscape of the green fluorescent protein. Nature,
Vol. 533, No. 7603, p. 397, 2016.

P3-10A Novel Inhibitor Stabilizes the Inactive Conformation of
MAPK-interacting Kinase 1
Yumi Matsui 1

Isao Yasumatsu 1

matsui.yumi.gk@rdn.daiichisankyo.co.jp

yasumatsu.isao.bw@rdn.daiichisankyo.co.jp

Ken-ichi Yoshida 2

Shin Iimura 2

Yutaka Ikeno 1

Takako Nawano 1

Hajime Fukano 1

Osamu Ubukata 1

Hiroyuki Hanzawa 1

Fumie Tanzawa 2

Takeshi Isoyama 2
isoyama.takeshi.y5@daiichisankyo.co.jp

12

Daiichi Sankyo RD Novare Co., Ltd., 1-16-13 Kitakasai, Edogawa-ku, Tokyo 134-8630, Japan
Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710, Japan

Keywords: X-ray crystal structure analysis, Structure-based drug design (SBDD), Protein Data
Bank, Protein kinase, Enzyme inhibitor, Drug discovery, Tumor therapy
Mitogen-activated protein kinase (MAPK)-interacting kinases 1 (Mnk1) and 2 (Mnk2) are protein
serine/threonine kinases that are activated by extracellular signal-regulated kinase (Erk) or p38
MAPK [1, 2]. Either activated Mnk1 or Mnk2 phosphorylates Ser209 of eukaryotic translation
initiation factor 4E (eIF4E) [3], which promotes tumorigenesis [4]. However, Mnk1 and Mnk2 aredispensable in normal cells [5], suggesting that the inhibition of Mnk1 and Mnk2 could be effective
in cancer therapy.
To provide a structural basis for Mnk1 inhibition, a novel Mnk1 inhibitor was discovered and the
crystal structure of Mnk1 in complex with this inhibitor was determined [6]. The crystal structure
revealed that the inhibitor binds to the autoinhibited state of Mnk1, stabilizing the Mnk-specific
DFD motif in the DFD-out conformation, thus preventing Mnk1 from switching to the active
conformation and thereby inhibiting the kinase activity. These results provide a valuable platform
for the structure-guided design of Mnk1 inhibitors.
[1] Waskiewicz A.J. et al., Mitogen-activated protein kinases activate the serine/threonine kinases
Mnk1 and Mnk2, EMBO J., 16(8):1909-1920, 1997.
[2] Fukunaga R. and Hunter T., MNK1, a new MAP kinase-activated protein kinase, isolated by anovel expression screening method for identifying protein kinase substrates , EMBO J.,
16(8):1921-1933, 1997.
[3] Waskiewicz A.J. et al., Phosphorylation of the cap-binding protein eukaryotic translation
initiation factor 4E by protein kinase Mnk1 in vivo , Mol. Cell. Biol., 19(3):1871-1880, 1999.
[4] Scheper G.C. and Proud C.G., Does phosphorylation of the cap-binding protein eIF4E play arole in translation initiation? Eur. J. Biochem., 269(22):5350-5359, 2002.
[5] Ueda T. et al., Mnk2 and Mnk1 are essential for constitutive and inducible phosphorylation of
eukaryotic initiation factor 4E but not for cell growth or development , Mol. Cell. Biol.,24(15):6539-6549, 2004.
[6] Matsui Y. et al., A novel inhibitor stabilizes the inactive conformation of MAPK -interacting
kinase 1, Acta Cryst., F74(Pt 3):156-160, 2018.

P3-11

Structural changes in Cl - pump rhodopsin by time-resolved
serial femtosecond crystallography
Toshiaki Hosaka 1

Eriko Nango 2

toshiaki.hosaka@riken.jp

Takashi Nomura 3
takashi.nomura@riken.jp

e.nango@mfour.med.kyoto-u.ac.jp

Minoru Kubo 3
minoru@sci.u-hyogo.ac.jp

Takanori Nakane 4

Tomomi Kimura-Someya 1

tnakane@mrc-lmb.cam.ac.uk

tomomi.someya@riken.jp

So Iwata 2
s.iwata@mfour.med.kyoto-u.ac.jp

Mikako Shirouzu 1
mikako.shirouzu@riken.jp

1

RIKEN Center for Biosystems Dynamics Research, 1 -7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan2
Department of Cell Biology, Graduate School of Medicine, Kyoto University,
Yoshidakonoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
3
Graduate School of Life Science, University of Hyogo, 3-2-1 Koto, Kamigori-cho, Ako-gun, Hyogo
678-1297, Japan
4
Structural Studies Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical
Campus, Cambridge, UK

Keywords: chloride ion pump rhodopsin, time resolved serial femtosecond crystallography
The X-ray free electron laser (XFEL) pulse is characterized by its high brightness and its shortpulse duration[1-3]. We have applied time-resolved serial femtosecond crystallography (TR-SFX)
using XFEL to observe structural change of proteins at room temperature without radiation damage .
These TR-SFX experiments have revealed the dynamic structural changes of various proteins. In
particular, the study of bacteriorhodopsin (BR) was successful in tracking photochemical reactions
within femtosecond to millisecond after photoexcitation, and interesting knowledge on thecorrelation between structural changes and proton transport was obtained [4].
Nonlabens marinus rhodopsin-3 (NM-R3) is a retinal containing protein which functions as alight-driven chloride ion-pump. NM-R3 and halorhodopsin (HR), a well known light-driven
chloride ion-pumping rhodopsin, have distinct motif sequences that are important for chloride ion
binding and transport. We have previously determined the high-resolution crystal structures of
NM-R3 and clarified the difference with HR [5], although detailed chloride ion transporting
mechanisms remain unknown. In this study, we observed the conformational changes during ion
pumping in NM-R3, from the initial changes to 1 msec following photoactivation by TR-SFX.
These results provide important insights for understanding the photochemical reaction of otherretinal proteins in comparison with BR.
[1] Amann, J., Berg, W., Blank, V., and Decker, F.J., Demonstration of self-seeding in a
Hard-X-ray free-electron laser. Nature Photonics, 6:693–698, 2012[2] Ishikawa, T., Aoyagi, H., Asaka, T., and Asano, Y.A., Compact X-ray free-electron laser
emitting in the sub-angstrom region. Nature Photonics 6:540-544, 2012[3] Emma, P., Akre, R., Arthur, J., and Bionta, R., First lasing and operation of an angstrom
wavelength free-electron laser. Nature Photonics 4:641-647, 2010[4] Nango, E., et al., A three-dimensional movie of structural changes in bacteriorhodopsin.
Science, 354:1552-1557, 2016[5] Hosaka, T., et al., Structural Mechanism for Light-driven Transport by a New Type of Chloride
Ion Pump, Nonlabens marinus Rhodopsin-3. J. Biol. Chem. 291:17488-17495, 2016

P3-12Crystal Structure of EGFR T790M/C797S in complex with
Brigatinib
Mutsuko Kukimoto-Niino 1

Yuri Tomabechi 1

kukimoto@riken.jp

yuri.tomabechi@riken.jp

Ryohei Katayama 2ryohei.katayama@jfcr.or.jp

1

2

Naoki Sakai 1
naoki.sakai@riken.jp

Mikako Shirouzu 1
mikako.shirouzu@riken.jp

RIKEN Center for Biosystems Dynamics Research , 1-7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan
Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake,
Koto-ku, Tokyo 135-8550, Japan

Keywords: X-ray crystallography, Tyrosine kinase inhibitor, Drug resistance
Activating mutations in the epidermal growth factor (EGFR) kinase domain are a common causeof non-small cell lung cancer (NSCLC). Osimertinib is a tyrosine kinase inhibitor (TKI) that
selectively inhibits the EGFR activating mutation including T790M acquired mutation displaying
resistance to first generation EGFR TKIs such as gefitinib. However, the additional C797S
mutation of EGFR confers resistance to osimertinib, by impairing covalent linking of the drug with
Cys797.
We have recently shown that brigatinib, an ALK tyrosine kinase inhibitor, is effective against the
EGFR-T790M/C797S/activating triple mutation, and that its effect has been markedly enhanced by
the combination of anti-EGFR antibody [1]. Our original computational simulation demonstrated
that brigatinib fits into the ATP-binding pocket of the EGFR-T790M/C797S/activating triple
mutant.
Here, we report the crystal structure of the EGFR-T790M/C797S double mutant in complex withbrigatinib. The structure reveals why brigatinib prefers to bind the T790M mutant without relying
on Cys797. The structure will be very powerful information to develop new TKIs to overcome drug
resistance due to the C797S mutation.
[1] Uchibori, K., Inase, N., Araki, M. Kamada, M., Sato, S., Okuno, Y., Fujita, N., Katayama, R.,
Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in
EGFR-mutated non-small-cell lung cancer, Nat Commun., 8:14768. doi: 10.1038/ncomms14768,
2017.

P3-135(3@:,:)(:,+65:;(;0:;0*(3;/,946+@5(40*:-693(9.,
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P3-14
Analysis by an Accelerated Quantum Chemical Molecular
Dynamics Method for the 8-oxoG added DNA Structure
Ai Suzuki 1

Akira Sassa 2

ai@aki.niche.tohoku.ac.jp

a-sassa@chiba-u.jp

Manabu Yasui 3

Masamitsu Homma 3

m-yasui@nihs.go.jp

honma@nihs.go.jp

1

3

New Industry Creation Hatchery Center, Tohoku University, 6-6-10 Aoba Aramaki, Aoba-ku,
Sendai, Miyagi 980-8579, Japan2
Lab o r ato r y o f Chr o mat i n Metab o l is m a nd Ep ig e neti cs, Grad uate sc ho o l o f Sci enc e,
Ch ib a U ni ver s it y, 1 -3 3 , Ya yo i -c ho , Ina g e - ku, C h ib a, 2 6 3 -8 5 2 2 , J ap an
Div isio n o f Ge ne tics a nd Mut age ne si s, T he Na tio nal I n stit ute o f Heal th S cience s , 3-25-26,
Tono-machi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan

Keywords: 8-oxoguanine, DNA, water
The oxidative DNA lesion 7, 8-dihydro -8-oxoguanine(8oxoG) is particularly removed by the human repair
enzyme 8 -oxoguanine glycosylase (hOGG1). The hOGG1
has catalytic role for the rotation or cleavage ofN-glycos ydic linkage between the 8oxoG and ribose [1].
Besides carrying numbers, clustered or isolated of 8oxoG
somehow changed the degree of the reactivity of hOGG1 [2],the mechanism of recognition has not been elucidated
especially for the several 8oxoG adducted cases. The
hOGG1 recogni zes the single 8oxoG legion, but does notrecognize for the two base pair remote tandem 8oxoG
likewise the healthy DNA [3]. Hence, we comprehensively
comparedtheelectronic
configurationofwater
organization around the 8oxoG of the DNA and its
influence on the backbone conformation by an accelerated
quantum chemical molecular dynamics method whichguarantees computational accuracy. The binding energies for
8oxoG, Guanine, Adenine, Thymine, and Cytosine obtained byan accelerated quantum chemical molecular dynamic method
agree well with those of the density functional methods asshown in Table 1. Backbone dihedral angles and waterorientation to the bases were analyzed for healthy, single
and tandem 8oxoG damaged DNA.

Water molecules outside of
the primary hydration layer

Water molecules ofprimary hydration layer

theFig.1 Stereo views of adouble-stranded
nativeDNA with water molecules.

Table 1. Comparison of the binding energies of 8oxoG and bases
Binding Energy
8oxoG Guanine Adenine Thymine Cytosine
DFT (Dmol3-P91)-2064-1925.1-1797-1768-1508.2
DFT (Dmol3-BLYP) -1990
-1860
-1741
-1715
-1464
Accelerated QC
-2046
-1863
-1688
-1700
-1401
[1] David, S. S., Nature, Structural biology: DNA search and rescue. 434:569–570, 2005.
[2] H. Terato, R. Tanaka, Y. Nakaarai, T. Nohara, Y. Doi , S. Iwai, R. Hirayama, Y. Fur usawa ,H. Ide, J. Radiat. Res, Quantitative Analysis of Isolated and Clustered DNA Damage Induced
by Gamma-rays, Carbon Ion Beams, and Iron Ion Beams, 49:133–46, 2008.
[3] Sassa, A., Kamoshita, N., Kanemaru, Y., Honma, M., Yasui, M., PLOS ONE, Xeroderma
Pigmentosum Group A Supresses Mutagenesis Caused by Clustered Oxidative DNA Adducts in
the Human Genome, 1/15-15/15, 2015.

P3-15
Validation of protein structure from low resolution density
map using Deep Learning
Miwa Sato 1
sato-miwa@mki.co.jp

Hiroyuki Nakagawa 1

Yuichi Kokabu 1

nakagawa-hiroyuki@mki.co.jp

kokabu-yuichi@mki.co.jp

Ikuko Miyaguchi 2
miyaguchi.ikuko@mv.mt-pharma.co.jp

Sigeyuki Matsumoto 4
sigeyuki.matsumoto@riken.jp

1
2
3

4
5
6

Akiko Kashima 2
kashiima.akiko@mt-pharma.co.jp

Atsushi Tokuhisa 4
tokuhisa@riken.jp

Masateru Ohta 5masateru.ota@riken.jp

Biao Ma 3
baio.ma@riken.jp

Mitsunori Ikeguchi 5,6
ike@tsurumi.yokohama-cu.ac.jp

Mitsui Knowledge Industry Co., LTD, 2-5-1 Atago, Minato-ku, Tokyo 105-6215, Japan
Mitsubishi Tanabe Pharma Co., LTD, 1000 Kamoshida, Aoba-ku, Yokohama, Kanagawa, JapanFoundation for Biomedical Research and Innovation at Kobe, 2-2 Minatojima-minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan
Riken Kobe Campus, 6-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 6500047, Japan
Riken Yokohama Campus,1-7-22 Suehirocyo, Tsurumi-ku, Yokohama, Kanagawa, Japan
Yokohama City University, 1-7-29 Suehirocyo, Tsurumi-ku, Yokohama, Kanagawa 230-0034,
Japan

Keywords: Protein structure prediction, Deep learning, X-ray
Protein functions are regulated by the interaction between the protein and its specific substrate.
The shape complementary of the protein-substrate complex can help us to elucidate the regulation
mechanism of protein function and to design medicinal candidates. In drug discovery, it is required
the detailed analysis of the interaction between target protein and its ligands with atomic
resolution when designing medical candidates based on the information of protein structures.
Therefore, the structural resolution of target protein is desired within 2 Å. At present, X-ray crystal
structure analysis, one of the methods to determine protein structures, is dominantly used, anddetermined 90% of protein structures in the Protein Data Bank (PDB). However, about more thanhalf of the protein structures which have been registered in the PDB show less than 2 Å resolution.
Moreover, at the scene of drug discovery, low resolution of protein structures negatively
may affect all over the structure-based analysis.
On the other hand, rapid progress in deep learning has accelerated its application to predict
protein structure and function. [1][2] It became possible to take as input the comprehensive 3D
representation of protein - ligand interaction. [3][4] In LINC (Life Intelligence Consortium), so as toconvert low resolution X-ray crystallographic data into high resolution data, we are developing avalidation system of protein structure using 3D convolutional neural network that take protein
structure and electron density map as input. This system will evaluate coordinates of model
structure with low resolution.
*Part of this research was carried out as activities of Life Intelligence Consortium (LINC).
[1] Maxwell L.B., et al., Using Deep Learning to annotate the protein universe., bioRxiv,
May. 3, 2019[2] Wang S., Sun S., Li Z., Zhang R., Xu J., Accurate de novo prediction of protein contact map by Ultra-Deep Learning model., PLoS Comput. Biol., 13(1),2017[3] Jimez J., Skalic M., Martinez-Rosell G., De Fabritiis G., KDEEP:Protein-liagand absolut
e binding affinity predicton via 3D-Convolutional Neural Network., J. Chem. Inf. Model.
2018, 58, 287-296[4]Jimenez J., Doerr S., Martinez-Rosell G., Rose AS., DeFabritiis G., DeepSite:Protein-bin
ding site predictor using 3D-convolutional neural networks., Bioinformatics, 2017 Oct 1;33(19):3036-3042

P3-16

Automated X-ray crystallographical inhibitor screening
against an insect ecdysteroidogenic enzyme, Noppera-bo
Kotaro Koiwai 1

Kazue Inaba 2

koiwaiko@post.kek.jp

kze1221@gmail.com

Fumiaki Yumoto 1
fumiaki.yumoto1@gmail.com

Yusuke Yamada 1

Ryusuke Niwa1,3

Toshiya Senda1,4

yusuke.yamada@kek.jp

ryusuke-niwa@tara.tsukuba.ac.jp

toshiya.senda@kek.jp

1Structural Biology Research Center, Photon Factory, Institute of Materials Structure Science,High Energy Accelerator Research Organization, 1-1 Oho, Tsukuba, Ibaraki 305-0801, Japan
2
Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai,
Tsukuba, Ibaraki 305-8572, Japan
3
Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA),
University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8577, Japan
4
School of High Energy Accelerator Science, SOKENDAI University, Tsukuba, Ibaraki 305-0801,
Japan

Keywords: X-ray protein crystallography, protein-ligand complex
Compounds which inhibit protein function are called as “inhibitors”. Inhibitors can be utilized asprobes to reveal in vivo function of target proteins. Structures of protein-inhibitor complex provide
beneficial information not only for molecular mechanism of interaction between a target protein
and an inhibitor, but also for that of biological function of the protein. In addition, theexperimentally determined complex structures also provide higher reliability and precision for insilico calculation or in vitro assays than free-form structures. For these application, complex
structures determined at atomic resolution are necessary, and X-ray crystallography has been
acknowledged as the most powerful method to determine such structures.
X-ray crystallography has required a reliable and high-performance system due to huge efforts
and costs at steps of crystallization, data collection, and structure determination. Currently,
high-throughput X-ray crystallography for inhibitor screening has become to be realized bydevelopment of highly automated systems at the steps. Toward the high-throughput X-ray
crystallography, we have proceeded automation of structure determination followed by datacollection, and developed a comprehensive MR-native SAD pipeline for X-ray crystallography,
COMPASS. In COMPASS, an MR and electron-density map visualization module, PEINTS, is
implemented for structure analysis of protein and small compound inhibitor complex, whichcollectively processes X-ray diffraction data and results in high-performance inhibitor screening.
Here, we report structural analysis of an insect-specific development and differentiation regulator,
Noppera-bo (Nobo), and small compound inhibitor complexes as one of the case studies ofapplication of PEINTS. Nobo has been shown to be involved in biosynthesis of an insect
steroidogenic compound, ecdysteroid. However, its endogenous substrate and regulatory
mechanism of ecdysteroid biosynthesis by Nobo have remained to be elucidated. To reveal them,structural analysis of Nobo and 13 small compound inhibitor complexes was performed. Amongthem, D. melanogaster Nobo (DmNobo)-17b-estradiol (EST) complex demonstrated EST was
captured in a ligand-recognition pocket of DmNobo. Therefore, we utilized EST as a probe toreveal biological function of Nobo. Structural analysis revealed that Asp113 of DmNobo interactedwith EST and the Asp113 was crucial for in vivo function of Nobo. This study showed
effectiveness of COMPASS toward realization of high-throughput X-ray crystallography.

P3-17Development of the screening system tocreate GPCR mutants stabilized in active states
Ryosuke Nakano 1
nryosu1995@chiba-u.jp

Nanao Suzuki 1

nanao@chiba-u.jp

Simon Hikiri 2simon.hkr3i@iae.kyoto-u.ac.jp

Satoshi Yasuda 1,2

Mitsunori Shiroishi 3

Masahiro Kinoshita 2s.yasud@chiba-u.jp

shiroishi@rs.tus.ac.jp

kinoshit@iae.kyoto-u.ac.jp

Takeshi Murata 1
t.murata@faculty.chiba-u.jp
12
3

Graduate school of Science, Chiba University, 1-33 Yayoi-cho, Inage, Chiba 263-8522, Japan
Institute of Advanced Energy, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
Graduate school of Engineering Science, Tokyo Univer sity of Science, 6-3-1 Niijuku,
Katsushika-ku, Tokyo 125-8585, Japan

Keywords: GPCR, active state, theoretical prediction method, S. cerevisiae
G protein-coupled receptors(GPCRs) are the largest family of membrane proteins in the huma ngenome, and these proteins exist in equilibrium between inactive and active states. Upon binding
extracellular ligands, GPCRs undergo conformational changes that activate heterotrimeric
G proteins and transmit the signals to the cell interior. Thus, GPCRs are located upstream of signaltransduction and involved in various diseases. Structure determination of GPCRs in active states isimportant to elucidate the precise mechanism of signal transduction and to facilitate optimal drug
design. However, their instability is an obstacle for structural studies and stabilization of GPCRs is
essential.
Here, we developed the screening system to create GPCR mutants stabilized in active states.
We applied our theoretical prediction method[1] to adenosine A 2 a receptor in the agonist-boundstate and selected mutants from the prediction result. The mutants were expressed in
S. cerevisiae strain YB14, which have been engineered so that heterologously expressed GPCRs
activate the yeast pheromone signaling pathway and enable growth on selective media. We
evaluated the mutants by using agonist-dependent yeast growth assay[2].
In conclusion, we succeed in screening a mutant which has higher affinity for agonists than thewild type. The screening system using the theoretical prediction method and the yeast growth
assay will be useful for creating mutants of other GPCRs stabilized in active states.
[1] Satoshi Yasuda, Yuta Kajiwara, Yuuki Takamuku, Nanao Suzuki, Takeshi Murata, Masahiro
Kinoshita, Identification of thermostabilizing mutations for membrane proteins : rapid method
based on statistical thermodynamics., The Journal of Physical Chemistry B, 120, 3833-3843,
2016.
[2] Dong S., Rogan SC., Roth BL., Directed molecular evolution of DREADDs: a generic approachto creating next-generation RASSLs., Nature Protocols, 5:561-573, 2010.

P3-18

Identification of a conserved allosteric sitein Heme-copper oxygen reductase
Yuya Nishida 1,2
nishida.yuya@ncvc.go.jp

Seiji Takashima 2

takasima@cardiology.med.osaka -u.ac.jp

Hitomi Yuki 3

Teruki Honma 3

hitomi.yuki@riken.jp

honma.teruki@riken.jp

Yasunori Shintani 1,2
shintani.yasunori@ncvc.go.jp
12

3

Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center
Research Institute,
6-1 Kishibeshinmachi, Suita, Osaka 564-8565, Japan
Medical Biochemistry, Osaka University Graduate School of Medicine,
2-2 Yamadaoka, Suita-shi, Osaka, 565-0871, JapanRIKEN Center for Life Science Technologies,
1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan

Keywords: Heme-copper oxygen reductase, cytochrome c oxidase, ubiquinol oxidase
allosteric regulation, ligand-based virtual screening
Heme-copper oxygen reductase (HCO) works as a terminal component of the respiratory electrontransport chain. HCO couples the oxygen reducing reaction to the proton pumping. This reaction
forms a proton gradient across the mitochondrial inner membrane, which is used by ATP synthase
to produce ATP. Here we describe two HCOs: mitochondrial cytochrome c oxidase (mtCcO) andbacterial ubiquinol oxidase (UqO). Recently we identified novel allosteric inhibitors of
mitochondrial mtCcO and determined mtCcO structure in complex with some of them. These
structures showed two inhibitors bound to the same site of mtCcO, which is distant from theligand-binding sites. There is similarity between the inhibitor binding site of mtCcO and thehomologous position in bacterial UqO, although mtCcO has additional subunits that are found only
in mammals. These led us to hypothesize that the novel allosteric site is conserved and has apotential to be a novel antibiotic target.
To this end, we established a custom library by using the ligand-based virtual screening
system, LAILAPS, with the mtCcO inhibitors as queries from commercially available compounds.
As results of enzyme assay with mtCcO, the most improved compound had IC50 of 100 nM, which
is one-order better than that of the query inhibitors. In addition, we identified multiple inhibitors
targeting UqO, and importantly some of them were specific to UqO . Next, to confirm theinhibitor-binding site in UqO, we performed the enzyme assay with mutant bacterial UqOs which
had an amino acid substitution in the corresponding site with the inhibitor-binding site of mtCcO.
As a result, the effect of the inhibitors was significantly changed in several mutants. These results
strongly suggest that the inhibitors-binding site is conserved between mtCcO and bacterial UqO.
Finally, to verify the target specificity of the novel UqO inhibitor in vivo, antibacterial activity ofthese inhibitors was analyzed. The UqO inhibitor prevented the growth of UqO -dependent E. coli
strain, whereas the effect was abrogated in UqO-independent strain.
From these results, we conclude that the allosteric site is conserved among HCOs. Basedon structural difference between mammalian and bacterial oxidase, it is possible to develop novel
antibiotics with specificity.

P3-19The influence of cosolvent on thermal stability of membraneproteins

Kazuki kazama 1

Satoshi Yasuda 1,2,3

kazkaz10nogodc@chiba-u.jp

Tomoki Akiyama 1
tomotomoaki01310131@yahoo.co.jp

s.yasud@chiba-u.jp

Masahiro Kinoshita 3
kinoshit@iae.kyoto-u.ac.jp

Takeshi Murata 1,2t.murata@faculty.chiba-u.jp

1

Graduate School of Science, Chiba University, 1-33 Yayoi-cho, Inage, Chiba 263-8522, Japan
Molecular Chirality Research Center, Chiba University, 1-33 Yayoi-cho, Inage, Chiba 263-8522,
Japan
3
Institute of Advanced Energy, Kyoto University, Uji, Kyoto 611-0011, Japan
2

Keywords: Membrane proteins, Solvation entropy, Protein stabilization, Clear-native PAGE
Membrane proteins play an important role in basic processes such as signal transduction throughbiological membranes to maintain homeostasis, substance transport, and energy production. More than
50% of currently marketed pharmaceutical products target membranes proteins. However, many
membrane proteins have low thermal stability in detergents and are easily denatured in the purification
process. Therefore, it is difficult to elucidate the structures and functions of these unstable membraneproteins. In general, it is known that the addition of a cosolvent such as sugar and alcohol affects thethermal stability of water-soluble proteins. Recently, we have proposed that the solvent entropy is the
key factor of the cosolvent effect. Upon protein folding, the excluded volume (EV) (i.e., the volume ofthe space which the centers of water molecules cannot enter) decreases to a large extent, which isfollowed by a corresponding increase in the total volume available to the translational displacement ofwater molecules in the system and a reduction in the water crowding. The folding thus leads to a large
gain of the water entropy. When the water crowding is made more serious by the cosolvent addition,
the solvent-entropy gain upon protein folding is magnified, leading to the enhanced thermal stability.
In this study, we focused on membrane proteins (MPs). We have shown that the entropic effect ofhydrocarbon groups constituting the lipid bilayer is essential for stabilization of the MPs. The lipid
bilayer is immersed in water, and it is expected that if water crowding becomes more serious, crowdingof hydrocarbon groups also become higher. We can regard hydrocarbon groups and water as one
solvent. This interpretation is also true for the MP inside the micelle formed by detergents. Based on
this unique view, the effects of cosolvent addition for the two MPs, adenosine A2a receptor (A 2a R) andthermophilic rhodopsin (TR), are experimentally examined. Six cosolvents (the order of thestabilization effect reported previously is sucrose > glucose ~ mannitol > erythritol > glycerol >2-propanol) are tested. Cosolvent addition can stabilize both of the MPs, and the order of stabilizationis completely consistent with our previous analytical results for the water-soluble protein. By sucrose
addition, T m increases by 2.6℃ and 5.5℃ for TR and A2aR, respectively. Thus, the cosolvent addition
according to the thermotical prediction is a useful method to stabilize purified MPs.

[1] Shota Murakami, and Masahiro Kinoshita. The Journal of Chemical Physics 144, 125105(2016)[2] Hiraku Oshima, and Masahiro Kinoshita. The Journal of Chemical Physics 138, 245101(2013)[3] Nanao Suzuki et al. Analytical Biochemistry 548 (2018) 7-14

P4-01

Identification of a biomarker for disease progression
in heart failure using single-cell RNA sequencing data
Momoko Hamano1

Seitaro Nomura 2,3

haman929@bio.kyutech.ac.jp

senomura-cib@umin.ac.jp

Hiroyuki Aburatani 3

Issei Komuro 2

Yoshihiro Yam anishi 1haburata-tky@umin.ac.jp

komuro-tky@umin.ac.jp

yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan,
2
Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo,
Tokyo 113-8655, Japan.
3
Genome Science Division, Research Center for Advanced Science and Technologies, The
University of Tokyo, Tokyo 153-0041, Japan.

Keywords: Heart failure, Single-cell RNA sequencing, Biomarker
Heart failure is one of the most serious problems in cardiovascular diseases, as reflected by anincrease in the number of patients. Heart failure has multiple risk factors and various pathologicalconditions, which makes it difficult to understand the molecular pathological mechanisms. We
previously reported that metabolic and shape remodeling was induced by activating p53 in
hypertrophic cardiomyocytes, which was critical for progression of heart failure [1]. In this study,
we analyzed gene expression patterns of individual cardiomyocytes using single-cell RNA
sequencing data in order to explore biomarkers characteristic of each cardiomyocyte and clarify
the molecular pathological mechanism of heart failure deterioration. First, we found that theexpression of natriuretic peptide A (Nppa) was significantly increased after stress loading bytransverse aortic constriction (TAC), and then, characterized each cardiomyocyte by the expression
level of Nppa. The tSNE analysis of gene expression data showed that Nppa high-expressing
cardiomyocytes were located farthest away from the TAC non-loading cardiomyocytes. Also, theclustering analysis showed different gene expression patterns between Nppa high-expressing andNppa low-expressing cardiomyocytes. Furthermore, multiple NADH ubiquinone complex family
proteins associated with the mitochondrial electron transport system were negatively correlatedwith Nppa expression at the early stage of cardiac hypertrophy, implying that mitochondrial
dysfunction was induced in Nppa high-expressing cardiomyocytes. These results suggest that theexpression level of Nppa is a useful marker for understanding the molecular pathogenesis of heart
failure.
[1] Nomura S., Satoh M., Fujita T., Higo T., Sumida T., Ko T., Yamaguchi T., Tobita T., Naito AT.,Ito M., Fujita K., Harada M., Toko H., Kobayashi Y., Ito K., Takimoto E., Akazawa H., Morita
H., Aburatani H., and Komuro I.. Cardiomyocyte gene programs encoding morphological andfunctional signatures in cardiac hypertrophy and failure, Nat Commun. (2018) 9(1):4435.

P4-02

An importance of polyamine metabolism regulated bycancer stem cells highlighted by our trans-omics method
Jun Koseki 1
jkoseki@cfs.med.osaka-u.ac.jp

Masamitsu Konno 2mkonno@cfs.med.osaka-u.ac.jp

Ayumu Asai 1,2

Hideshi Ishii 1aasai@cfs.med.osaka-u.ac.jp

aasai@cfs.med.osaka-u.ac.jp

12

Department of Medical Data Science, Graduate School of Medicine, Osaka University, 2-2
Yamadaoka, Suita-shi, Osaka 565-0871, Japan
Department of Frontier Science for Cancer and Chemotherapy, Gradu ate School of Medicine,
Osaka University, 2-2 Yamadaoka, Suita-shi, Osaka 565-0871, Japan

Keywords: Trans-omics analyses, reaction flow modeling, polyamine metabolism
In cancer treatment, generally, chemotherapy and radiation therapy are performed after surgery. These
treatments might result in recurrence of cancer, even though it appears to be eradicated. Previous
studies have shown that the subpopulations of tumor-initiating cells, or cancer stem cells (CSCs), areresponsible for the heterogeneity of tumors. It has been thought that this heterogeneity is involved intheir refractoriness to chemotherapy and radiation therapy as well as their subsequent relapse.
Therefore, it is very important to understand the properties of CSCs for the cancer therapy. In order tounderstand the differences of biological characteristic between CSCs and non-CSCs, gene expression,amount of metabolite, etc., have been widely analyzed. It is very important to observe their behavior
changes over time, not snapshots at each such observation for understanding the response of cells toexternal stimuli such as antitumor drug. However, there were no method for analyzing to observe
biological changes over time at that point. Therefore, we have developed a novel trans-omics method tocomprehend temporal changes in metabolism [1].
Using our trans-omics method, the difference of metabolic reaction flow changes between CSCs andnon-CSCs were analyzed. In this analyses, we have focused on the ornithine metabolic pathway. The
reason is that, it is well known that the proteasome activity in CSCs is lower than in non -CSCs, inaddition, it was reported that the relationship between proteasome activity and ornithine decarboxylasein esophageal, cervical, colorectal and bone cancer was investig ated [2, 3]. Our analyses show that
CSCs exposed to antitumor drugs rapidly promoted the reverse reaction to ornithine. On the other hand,in non-CSCs, the reaction from ornithine to putrescine was promoted and the after the reaction tended
to be stopped. This result means that in non-CSCs, the change of reaction flow make the total amountof polyamine increasing. By the way, previous reports have suggested that polyamine catabolic
enzymes such as SAT1, SAT2, and PAO share high similarity in overall structu re with the histone
demethylase LSD1. In particular, the amino acid sequence of PAO shares over 60% similarity with thatof LSD1. So, these results suggest that high level polyamines inhibit LSD1 enzyme activity in
non-CSCs and also changes gene expression levels to influence cell survival. It may result in the deathof cancer cells.

[1] Koseki, J., et al., A Trans-omics Mathematical Analysis Reveals Novel Functions of theOrnithine Metabolic Pathway in Cancer Stem Cells . Scientific Reports, 6:20726, 2016.
[2] Tamari, K., et al., Identification of chemoradiation-resistant osteosarcoma stem cells using an
imaging system for proteasome activity. Int. J. Oncol., 45:2349, 2014.
[3] Hayashi, K. et al., Visualization and characterization of cancer stem-like cells in cervicalcancer. Int. J. Oncol., 45:2468, 2014.

P4-03

Interpreting Japanese GWAS Results on Multi-Omics
Drug Target Validation Platform
Wirawit Chaochaisit
wirawit@genesis-healthcare.jp

Youssef Darzi
youssef_darzi@genesis-healthcare.jp

He Huanghuang@genesis-healthcare.jp

Yosvany Lopez
lopez@genesis-healthcare.jp

Kosuke Negishi
kosuke_negishi@genesis-healthcare.jp

Genesis Healthcare Co., Yebisu Garden Place Tower 15F/26F, 4-20-3
Ebisu, Shibuya-ku, Tokyo 150-6015, Japan

Keywords: GWAS, Multi-Omics Data Integration, Drug Target Validation, Drug Discovery
The interpretation of GWAS results for drug target validation requires an exhaustive evaluation
based on a multitude of research evidences from heterogeneous data sources. Open Targets
Platform (OTP) [1] has integrated over 20 multi-omics data sources to provide a comprehensivecompendium of more than 7 million evidences. It offers wide array of analysis and visualization
pipelines, allowing a more thorough validation, comparison, and prioritization of drug targets fordiseases of interest. We processed our GWAS results for gout, hyperlipidemia, myopia, and asthma
through OTP’s data processing pipelines to map significant SNPs (P-value £ 10 -5 ) to genes using
Ensembl’s Variant Effect Predictor [2] or by considering their distance (within 500kb) to the 5’ endof the closest gene for intergenic variant. Then, the target-disease association score is calculatedusing p-value, sample size, and variant’s functional consequence coefficient [3][4]. As a result, wewere able to cross-validate and confirm 10 target-disease associations, five of which having our
GWAS as the only primary genetic association evidence. We also discovered five potential novel
SNP’s loci for associated targets. Integrating with the platform, we were able to conduct
exploratory evaluation of drug targets identified by our GWASs in comparison with other omics
evidences, giving an invaluable insight into further research on drug target selection.
[1] Carvalho-Silva, D., Pierleoni, A., Pignatelli, M., Ong, C., Fumis, L., Karamanis, N., ... &Miranda, A. (2018). Open Targets Platform: new developments and updates two years on.
Nucleic acids research, 47(D1), D1056-D1065.
[2] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R., Thormann, A., ... & Cunningham,
F. (2016). The ensembl variant effect predictor. Genome biology, 17(1), 122.
[3] Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., ... &Pierleoni, A. (2016). Open Targets: a platform for therapeutic target identification and validation.
Nucleic acids research, 45(D1), D985-D994.
[4] Association score. (n.d.). Retrieved July 31, 2019, fromhttps://docs.targetvalidation.org/getting-started/scoringP4-04Development of Integrated Database “dbTMM” forstratification of cohort participant toward drug
development
Satoshi Nagaie 1
nagaie@megabank.tohoku.ac.jp

Satoshi Mizuno 1
samizuno@med.tohoku.ac.jp

1

2

Yukie Konno 1

Naoko Kasahara 1

y.konno@megabank.tohoku.ac.jp

n.kasahara@megabank.tohoku.ac.jp

Hiroshi Tanaka 2

Soichi Ogishima 1

tanaka@megabank.tohoku.ac.jp ogishima@megabank.tohoku.ac.jp

Department of Informatics for Genomic Medicine,
Tohoku Medical Megabank Organization, Tohoku University,
Seiryo 2-1, Aoba-ku, Sendai Miyagi 980-8573 Japan
Department of Bioclinical Informatics,
Tohoku Medical Megabank Organization, Tohoku University,
Seiryo 2-1, Aoba-ku, Sendai Miyagi 980-8573 Japan

Keywords: Biobank, Cohort, Stratification, Drug Discovery
Tohoku Medical Megabank project was started to reconstruct from the Great East Japan
Earthquake. We started cohort studies in 2013, and successfully recruited more than 150,000
participants for baseline assessment by 2017. Though these cohorts, biospecimens including serum,plasma, mononuclear cells, urine, and the answers to the questionnaires of their lifestyles werecollected. A part of participants took detailed health survey in assessment centers and satellites.
We developed the integrated database of Tohoku Medical Megabank project (dbTMM). The
dbTMM stores large-scale data of 84,000 cohort participants including whole genome (n=3,500)
and SNP genotyping data (n=23,000) (genetic factors), questionnaire data (environmental factors),
metabolome and proteome data (molecular phenotypic factors), and clinical data (phenotypicfactors). By using dbTMM, researchers can stratify cohort participants having a certain phenotype
by genetic and environmental factors. For example, researchers can search dbTMM under thefollowing conditions: chromosome 8, position 41519462 (rs515071) = “TT” & health status(laboratory test data): HbA1c > “6.2” & lifestyle (questionnaire data): alcohol drinking = “Yes” &disease history (questionnaire data): Type II diabetes = “Yes”. As for phenotypic data, we havedeveloped the pipeline for retrieval and structuring clinical data from medical information
SS-MIX2 storage. By using structured clinical data, we will conduct deep phenotyping, and willstore phenotypic data into dbTMM. We believe our integrated database dbTMM will contribute tostratification of cohort participant toward drug development for common disease which is causedby complex interplay between genetic and environmental factors.
[1] Kuriyama, S. et al. The Tohoku Medical Megabank Project: design and mission. J. Epidemiol.
26, 493–511 (2016).

P4-05Development of phenotyping algorithms for hypertensive
disorders of pregnancy (HDP) for precise stratification
toward drug development
Satoshi Mizuno 1

Maiko Wagata 2

samizuno@med.tohoku.ac.jp

Gen Tamiya 3
gtamiya@genetix-h.com

Nobuo Yaegashi 6
nobuo.yaegashi@gmail.com

1

2

3

4

5

6

wagata@med.tohoku.ac.jp

Shinichi Kuriyama 4

Satoshi Nagaie 1
satoshi.nagaie@gmail.com

Hiroshi Tanaka 5kuriyama@med.tohoku.ac.jp

tanaka@cim.tmd.ac.jp

Junichi Sugawara 2,6

Soichi Ogishima 1

jsugawara@med.tohoku.ac.jp

ogishima@megabank.tohoku.ac.jp

Department of Informatics for Genomic Medicine, Group of Integrated Database Systems,
Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Feto-Maternal Medical Science, Group of Community Medical Supports,
Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Statistical Genetics and Genomics, Group of Disease Risk Prediction, Tohoku
Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Molecular Epidemiology, Group of the Birth and Three-Generation Cohort Study,
Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
Department of Bioclinical Informatics, Group of Integrated Database Systems, Tohoku Medical
Megabank Organization, Tohoku University, Miyagi, Japan
Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine,
Tohoku University, Miyagi, Japan

Keywords: Phenotyping, hypertensive disorders of pregnancy, Stratification
In recent years, many phenotyping algorithms have been developed for scalable andhigh-throughput cohort identification from electronic health records (EHRs) [1]. Phenotyping is
expected to stratify patients into subgroups that reflect clinical diversity of disease. Precise
phenotyping is key for stratified medicine and drug development, because stratification ofpatients expected to improve lack of efficacy of drugs caused by heterogeneity of patients.
In this study, we developed rule-based phenotyping algorithms of hypertensive disordersof pregnancy (HDP) according to international guideline. The developed algorithm includes
blood pressure, proteinuria and unstructured medical data. The developed phenotyping
algorithms were applied to phenotype 22,256 pregnancy women in the BirThree cohort
study [2]. We also compared the phenotyped subclasses with diagnoses to evaluate theperformance of our algorithms against the diagnosis as the gold standard.
Of the 22,256 research subjects, 2,038 (9.16%) were phenotyped as having HDP by thephenotyping algorithm. The performance of our phenotyping algorithms shows high PPV(0.94) and modest NPV (0.65). Our phenotyping algorithm enables us to conduct precise
stratification which reflect clinical heterogeneity of the subgroups of HDP toward stratified
medicine and drug development.
[1] Wei WQ and Denny JC, Extracting research-quality phenotypes from electronic health records
to support precision medicine, Genome Med, 30; 7(1):41, 2015.
[2] S Kuriyama et al, The Tohoku Medical Megabank Project: Design and Misson, J Epidemiol,
26(9):493-511, 2016.

P5-01Development of a Liver Toxicity Informatics System(AMED project)
Hiroshi Yamada 1

Yoshinobu Igarashi 1

Noriyuki Nakatsu 1

h-yamada@nibiohn.go.jp

y-igarashi@nibiohn.go.jp

nnakatsu@nibiohn.go.jp

Yuki Yamagata 1

Hiroshi Akimaru 2

Erika Akimaru 2

y-yamagata@nibiohn.go.jp

h-akimaru8@nibiohn.go.jp

eakimaru@nibiohn.go.jp

Katsuhisa Horimoto 3

Kazuhiko Fukui 3

Takumi Era 4k.horimoto@aist.go.jp

k-fukui@aist.go.jp

tera@kumamoto-u.ac.jp

Tadahiro Numakawa 4

Shirou Matsumoto 5

numakawa@kumamoto-u.ac.jp s-pediat@gpo.kumamoto-u.ac.jp

Junko Nagai 6

Tamotsu Noguchi 7

nagai-j@my-pharm.ac.jp

noguchit@my-pharm.ac.jp

1

2

3

4

5

6

7

Yoshihiro Uesawa 6
uesawa@my-pharm.ac.jp

Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health andNutrition(NIBIOHN), 7-6-8 Asagi Saito Ibaraki-City Osaka 567-0085, Japan
Center for Rare Disease Research, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Asagi Saito Ibaraki-City Osaka 567-0085, Japan
Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology(AIST), 2-4-7 Aomi,Koto-ku,Tokyo 135-0064, Japan
Department of Cell Modulation, Institute of Molecular Embryology and Genetics, Kumamoto
University, 2-2-1 Honjo,Chuo-ku,Kumamoto 860-0811, Japan
Department of Pediatrics, Graduate School of Medical Sciences, Kumamoto University, 1-1-1
Honjo,Chuo-ku,Kumamoto 860-8556, Japan
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1
Noshio, Kiyose, Tokyo 204-8588, Japan
Laboratory of Computational Biology, Meiji Pharmaceutical University, 2-522-1 Noshio,
Kiyose, Tokyo 204-8588, Japan

Keywords: Drug induced liver injury (DILI), Liver toxicity, Database, Prediction system
In order to contribute to the improvement of the ability related to safety evaluation in an academic
drug discovery, the five-yearly program for “Development of a Drug Discovery Informatics
System” supported by Japan Agency for Medical Research and Development (AMED) was started
in 2015. In this program, a collaborative project of NIBIOHN, AIST, Kumamoto university andMeiji pharmaceutical university for “Liver Toxicity Informatics System Development” isorganized. The project has established 3 databases related to liver toxicity (DILI-cSEARCH,TOXPILOT, toxBridge) and 4 toxicity prediction systems (DILI-PANEL, toxRANK,LIVER/MIE-QSAR, LUNG/MIE-QSAR). Some databases and prediction systems are already open
and
can
be
accessedvia
portalsite
oftheproject(DILI-TOOLBOX,
https://dili-toolbox.nibiohn.go.jp).
In this poster, we will introduce an overview of our databases and prediction systems.
Acknowledgment
This research is supported by Japan Agency for Medical Research and Development, AMED underGrant Number 19nk0101103h0005.

P5-02

DILI-cSEARCH: a DILI database for drug safety assessment
Yoshinobu Igarashi 1

Noriyuki Nakatsu1

Yuki Yamagata 1

y-igarashi@nibiohn.go.jp

nnakatsu@nibiohn.go.jp

y-yamagata@nibiohn.go.jp

Hiroshi Akimaru 2

Erika Akimaru 2

Shirou Matsumoto 3

h-akimaru8@nibiohn.go.jp

eakimaru@nibiohn.go.jp

s-pediat@gpo.kumamoto-u.ac.jp

Tadahiro Numakawa 4

Takumi Era 4

Hiroshi Yamada 1

numakawa@kumamoto-u.ac.jp

tera@kumamoto-u.ac.jph-yamada@nibiohn.go.jp

1

2

3

4

Toxicogenomics Informatics Project, National Institutes of Biomedical Innovation, Health andNutrition (NIBIOHN), 7-6-8 Asagi Saito Ibaraki-City Osaka 567-0085, Japan
Center for Rare Disease Research, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Asagi Saito Ibaraki-City Osaka 567-0085, Japan
Department of Pediatrics, Graduate School of Medical Sciences, Kumamoto University, 1-1-1
Honjo, Chuo-ku, Kumamoto 860-8556, Japan
Department of Cell Modulation, Institute of Molecular Embryology and Genetics, Kumamoto
University, 2-2-1 Honjo, Chuo-ku, Kumamoto 860-0811, Japan

Keywords: DILI, Transcriptomics, Human primary hepatocyte, Database
Among drug-induced toxicity, liver toxicity is one of the leading causes of discontinuation of drug
development and market withdrawal after-sales. For this reason, it is desired to create a new
method which is capable of finding and evaluating potential hepatotoxic compounds in the earlystage of drug development using in vitro or in silico approaches, which are faster and lessexpensive than animal experiments. In this study, we constructed the DILI (drug-induced liver
injury) database as an information infrastructure in "Development of an informatics system forpredicting drug-induced liver injury," which plays a part of AMED's "the Drug Discovery Support
Promotion Project." We assembled and organized data on hepatotoxicity from the four independent
databases (FDA / LTKB, NIH / LINCS, NTP / Drug Matrix, NIBIOHN / Open TG-GATEs) to a new
integrated database. This new database contains more than 800 compound information (mainly on
pharmaceutical drugs), gene expression information, biochemistry, and hematology information.
We also implemented a flexible search interface that enables to explore in complex data structures.
This
database called "DILI information cross-searchsystem"is available athttps://dili-csearch.nibiohn.go.jp.
Acknowledgment
This research is supported by Japan Agency for Medical Research and Development, AMED underGrant Number 19nk0101103h0005.

P5-03

Ontology-based Toxic Process Interpretable Knowledge
System for Drug-Induced Liver Injury
Yuki Yamagata 1

y-yamagata@nibiohn.go.jp

Yoshinobu Igarashi 1

y-igarashi@nibiohn.go.jp

Noriyuki Nakatsu 1

nnakatsu@nibiohn.go.jp

Katsuhisa Horimoto 2

k.horimoto@aist.go.jp

Yoshihiro Uesawa 3

uesawa@my-pharm.ac.jp

12
3

Kazuhiko Fukui 2

k-fukui@aist.go.jp

Hiroshi Yamada 1h-yamada@nibiohn.go.jp

Toxicogenomics, Informatics Project, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Asagi Saito Ibaraki-City Osaka 567-0085, Japan
Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Department of Clinical Pharmaceutics, Meiji Pharmaceutical University, 2-522-1 Noshio,
Kiyose, Tokyo 204-8588, Japan

Keywords: Ontology, Drug induced liver injury, Hepatotoxicity, Toxic course, Toxic process
Hepatotoxic mechanisms are complex and require multidisciplinary expertise. Therefore, newtechnology is necessary for the understanding of mechanisms, knowledge sharing, and knowledge
reusing across domains. We have constructed an ontology, TXPO, related to the hepatotoxicity
process [1]. In this research, we report on the toxic process interpretable support knowledge
system (TOXPILOT) as application work. TOXPILOT is a web application system that aims toprovide necessary information for interpretation of toxicity mechanism. By utilizing Semantic Web
technology, it has functions such as visualization of hepatotoxic course, display of functioning
processes in the organism, and provides various related information based on ontology.
Furthermore, it is also possible to search for routes that focus on the specific process. This system
is expected to be a knowledge sharing tool for users involved in safety assessment because it cangrasp toxic mechanisms cross-disciplinary. The prototype of TOXPILOT is open at the following
site: https://toxpilot.nibiohn.go.jp. Users can also access this knowledge system via our portal site(https://dili-toolbox.nibiohn.go.jp).
Acknowledgment
This research is supported by Japan Agency for Medical Research and Development, AMED underGrant Number 19nk0101103h0005.
[1] Yamagata Y., Igarashi Y., Nakatsu N., Horimoto K., Fukui K., Uesawa Y., and Yamada H.,
Development of Liver Toxicity Ontology for Drug Safety Evaluation and its Application,
Transactions of the Japanese Society for Artificial Intelligence 34(2), D-I81_1-18, 2019 [inJapanese].

P5-04Development of an informatics system for predicting
cardiotoxicity: 6. Update of the AMED cardiotoxicity
database and the hERG prediction model with additional
assays for the compounds selected by active learning.
Tomohiro Satotomohiro.sato@riken.jp

Hitomi Yuki

Teruki Honma

hitomi.yuki@riken.jp honma.teruki@riken.jp

Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho,
Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan

Keywords: hERG, QT prolongation, database
The inhibition of hERG potassium channel is closely related to the prolonged QT interval [1], andto assess the risk could greatly contribute to the development of safer therapeutic compounds. In
the optimization stage of drug development, quantitative prediction of hERG inhibitory activity is
crucial to design drug candidates without cardiotoxicity risk.
We previously reported the construction of AMED cardiotoxicity database which integrated theinformation of small molecule inhibitors of hERG, Nav1.5, Kv1.5, and Cav1.2 from various publicdata sources, and the prediction models based on the integrated data. [2, 3] Despite the successful
development of the largest dataset for the cardiac ion channels, the applicability of the prediction
models was still greatly affected by the chemical space coverage of the dataset. In this study, we
performed additional hERG inhibition assays employing both patch clamp and binding assays toimprove the hERG prediction model. Table 1 shows the number of hERG activity information in
the public databases and newly measured data in this study. The compound selection was
performed based on 1. known hERG inhibitors to obtain IC 50 values determined by a single assay
protocol, and 2. active learning to add novel compounds for which the prediction model based on
public data cannot predict the potency. The effect by the additional compounds to the prediction
performance and the applicability domain of the hERG prediction model is to be discussed.
Table 1. The number of hERG inhibitors and inactive compounds in each databasehERG inhibitors Inactive compounds Compounds
Database(IC50>10uM)(total)(IC50≦10uM)
ChEMBL (v24)
5,092
5,660
10,752GOSTAR
3,043
3,124
6,167
PubChem (AID588834)
382
2,964
3,346
hERG Central
4,321
274,536
278,857
public databases
10,269282,611292,880
RIKEN hERG assay
AMED cardiotoxicity database924
10,9401,725
283,8652,649
294,805[1] S. Picard, S. Goineau, P. Guillaume, J. Henry, J.L. Hanouz, R. Rouet, Supplemental studies for
cardiovascular risk assessment in safety pharmacology: a critical overview, Cardiovasc. Toxicol.11,
285–307, 2011[2] T. Sato, H. Yuki, T. Honma, Construction of an integrated database for hERG blocking small
molecules. PLOS ONE 13(7), e0199348.
[3] K. Ogura, T. Sato, H. Yuki, T. Honma, Support Vector Machine model for hERG inhibitory
activities based on the integrated hERG database using descriptor selection by NSGA -II. Scientific
Reports (in press)

P5-05Development of an informatics system for predicting
cardiotoxicity: 7. hERG prediction model based on
docking simulation and interaction descriptors with hERG
residues
Hitomi Yuki

Tomohiro Satohitomi.yuki@riken.jp

tomohiro.sato@riken.jp

Teruki Honma
honma.teruki@riken.jp

RIKEN Center for Biosystems Dynamics Research, 1 -7-22, Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: hERG, Ligand docking, Machine learning, Interaction fingerprinthERG (human ether-à-go-go-related potassium channel) is a member of potassium ion channels
and involved in cardiac repolarization. Drug-induced hERG blocking causes long QT syndrome
and it is a major cause of cardiotoxicity. Drug design for avoiding the hERG inhibition is quite
important through the all stages of drug discovery. Although 3D models of hERG would provide
worthy compound design ideas for reducing hERG inhibition, there was no experiment ally
validated 3D model for several decades. Using fingerprints or 2D descriptors of compounds ha vebeen a common way to predict their hERG inhibitory. Finally the molecular structure of hERG wasdetermined by cryo-electron microscopy in 2017. 1 However, the drug-binding site seemed to be too
narrow for known inhibitors such as dofelitide to bind.
To utilize the hERG 3D structures and design compounds for avoiding the hERG inhibition, wedeveloped prediction models based on docking simulation and interaction descriptors with hERG
residues. We performed MD simulation of the hERG cryo -electron microscopy structure andsubsequent docking simulations of known representative hERG inhibitors and similar compounds
measured their hERG binding affinity at RIKEN. Then we made and validated classification
models for hERG binding inhibitory based on interaction descriptors between a compound andeach residue/atom of hERG. We would like to show and discuss the analysis of these results andhow to use hERG 3D structures.
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED). The computing resources were
provided by the HOKUSAI-GreatWave system at RIKEN.
[1] Wang, W., and MacKinnon, R. Cryo-EM Structure of the Open Human Ether-à-go-go-Related
K+ Channel hERG. Cell 169:422–430.e10, 2017

P5-06Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
13. Development of DruMAP, Drug Metabolism andpharmacokinetics Analysis Platform
Hitoshi Kawashima 1

Reiko Watanabe 1

Tsuyoshi Esaki 1,2

hkawashi@nibiohn.go.jp

reiko-watanabe@nibiohn.go.jp

tsuyoshi-esaki@biwako.shiga-u.ac.jp

1

2

3

4

5

Chioko Nagao 3

Yayoi Natsume-Kitatani 1

chio@nibiohn.go.jp

natsume@nibiohn.go.jp

Rikiya Ohashi 1,4
rikiya-ohashi@nibiohn.go.jp

Hiroshi Komura 5

Kenji Mizuguchi 1,3

h-komura@ado.osaka-cu.ac.jp

kenji@nibiohn.go.jp

Laboratory of Bioinformatics, AI Center for Health and Biomedical Research, National
Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito Asagi, Ibaraki, Osaka
567-0085, JapanThe Center for Data Science Education and Research, Shiga University, 1-1-1, Banba, Hikone,
Shiga 522-8522, Japan
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7-6-8, Saito Asagi, Ibaraki, Osaka 567-0085,
Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50,
Kawagishi, Toda, Saitama 335-8505, Japan
Research Center for Industry Innovation and Osaka City University Incubator, Osaka City
University, 1-2-7, Asahimachi, Abeno, Osaka, 545-0051, Japan

Keywords: Pharmacokinetics, Drug metabolism, Database, Prediction models, Web application
We started the initiative “Development of a Drug Discovery Informatics System” four years ago
with support from the Japan Agency for Medical Research and Development (AMED). The main
aim of this initiative is to develop accurate prediction systems for DMPK (Drug Metabolism andPharmacoKinetics), primarily targeting academic scientists.
We collected pharmacokinetic and physicochemical parameters from ChEMBL or other public
sources. However, since ChEMBL compiles data obtained in different experimental conditions, weselected the data measured in compatible conditions and reformatted the results as appropriate forour prediction system. In addition to the public data, we have acquired in vitro and in vivo
experimental data under unified conditions.
We stored these data to a database named DruMAP (Drug Metabolism and pharmacokinetics
Analysis Platform). We have constructed prediction models for several DMPK parameters using
these data, and additional model construction is currently ongoing. DruMAP contains experimental
and predictive values for those parameters for about 18,000 compounds and the users can search
the database by key words, compound IDs, structural descriptions such as InChI, InChI Key or
SMILES, and by parameter values. DruMAP also provides the function to predict DMPKparameters for the user input compounds using our prediction models. DruMAP can be used forearly DMPK studies and for candidate compound selection to accelerate novel drug development.
DruMAP can be accessed at: https://drumap.nibiohn.go.jp/.
This work was conducted as a part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P5-07Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
14. In silico three-class predictor of human intestinal
absorption with Caco-2 permeability and dried-DMSO
solubility
Tsuyoshi Esaki 1,2

Reiko Watanabe 2

tsuyoshi-esaki@biwako.shiga-u.ac.jp

reiko-watanabe@nibiohn.go.jp

Yayoi Natsume-Kitatani 2natsume@nibiohn.go.jp

Hitoshi Kawashima 2

Chioko Nagao 4

hkawashima@nibiohn.go.jp

chio@nibiohn.go.jp

Hiroshi Komura 5
h-komura@ado.osaka-cu.ac.jp
1

2

3

4

5

Rikiya Ohashi 2,3
rikiya-ohashi@nibiohn.go.jp

Kenji Mizuguchi 2,4kenji@nibiohn.go.jp

The Center for Data Science Education and Research, Shiga University, 1-1-1, Banba, Hikone,
Shiga 522-8522, Japan
Laboratory of Bioinformatics, AI Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7-6-8, Saito Asagi, Ibaraki, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi,
Toda, Saitama 335-8505, Japan
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7 -6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
University Research Administrator Center, Osaka City University, 1 -2-7 Asahi, Abeno-ku, Osaka,545-0051, Japan

Keywords: Human intestinal absorption, Membrane permeability, Solubility, Machine learning
Oral drug administration is one of the most preferred delivery routes and is currently the goal of new
drug development owing to the ease of administration and patient compliance. In the drug discovery
stage, lead compounds with high intestinal absorption have been found to be very importan t because
intestinal absorption is the first step after dosing. Orally administered drugs reaching the intestine passthrough the intestinal epithelial cell membrane to the circulating blood and interact with targets such as
receptors, channels, and enzymes in several tissues. The administration of compounds with poor humanintestinal absorption (HIA) may cause substantial individual variation in pharmacokinetics and may
affect the desired pharmacological effects.
Therefore, we developed a freely available model classifying compounds using a three-level prediction
capacity for the fraction of absorption in human (Fa) with membrane permeability measured using Caco2 cell line (Papp) and solubility measured using dried -DMSO method (D-Sol); only the chemical
structure of compounds was employed [1]. To support Fa prediction, binary classification models ofPapp and D-Sol were constructed. To construct the Fa prediction model with high performance, we
compared several machine learning methods and descriptors such as physicochemical, substructure, andpharmacokinetics (Papp and D-Sol) parameters and found that adding both predicted Papp and D-Sol
was effective in classifying Fa (Accuracy: 0.836 and Kappa: 0.560).
In this presentation, we would like to present the results and to introduce an open web application(https://drumap.nibiohn.go.jp/fa) for three parameters (Fa, Papp and D-Sol) prediction.
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).
Reference: [1] Esaki T., et al., J. Pharma. Sci., 2019, DOI: 10.1016/j.xphs.2019.07.014

P5-08Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
15. Development of an in silico prediction system of
human renal excretion and clearance from chemical
structure information
Reiko Watanabe 1

Rikiya Ohashi 1,2

Tsuyoshi Esaki 1,3

reiko-watanabe@nibiohn.go.jp rikiya-ohashi@nibiohn.go.jp tsuyoshi-esaki@biwako.shiga-u.ac.jp

Hitoshi Kawashima 1

Yayoi Natsume-Kitatani 1

hkawashi@nibiohn.go.jp

natsume@nibiohn.go.jp

Chioko Nagao 1,4

Kenji Mizuguchi 1,4

chio@nibiohn.go.jp

kenji@nibiohn.go.jp

1

Laboratory of Bioinformatics, AI Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7 -6-8, Saito Asagi, Ibaraki, Osaka 567-0085, Japan
2
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi, Toda,
Saitama 335-8505, Japan
3
The Center for Data Science Education and Research, Shiga University, 1 -1-1, Banba, Hikone, Shiga
522-8522, Japan
4
Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes of Biomedical
Innovation, Health and Nutrition, 7-6-8, Saito Asagi, Ibaraki, Osaka 567-0085, Japan

Keywords: Renal clearance, protein unbound in plasma, machine learning
In the early stages of drug development, predicti ng the pharmacokinetic profiles of new
chemical entities is essential to minimize the risks of potential withdrawals. The excretion o funchanged compounds by the kidney constitutes a major route in drug elimination and plays animportant role in pharmacokinetics. Excretion in the urine is the consequence of a complex
mechanism that involves three main processes: glomerular filtration, tubular secretion, andreabsorption. We had previously developed in silico models to predict the values of fraction
unbound in plasma (f u,p ), which is an important determinant of drug efficacy in pharmacokinetic
and pharmacodynamic studies. In this study, we have extended our approach and built predictivemodels for two important pharmacological indicators of drugs in renal excretion : the fraction of
drug excreted unchanged in urine (f e ) and renal clearance (CL r ). Experimental values of f e and CL r
were extracted from the literature and the ChEMBL database. The binary classification model for f edemonstrated a balanced accuracy of 0.74. The two-step prediction system for CL r was generated
by using the combination of a classification model to predict excretion -type compounds andregression models to predict the value of CL r in each excretion type; moreover, the accuracies of
the regression models were increased by adding observed and predicted f u,p values as a descriptor.
Our predictive models can be integrated into other pharmacokinetic modeling systems , which
would be highly useful in academic drug discovery.
[1] Bohnert T, Gan L. S, Plasma protein binding: from discovery to development. J Pharm Sci 2013,
102 (9), 2953-94.
[2] Varma M, Obach Bo R, Troutman M, Physicochemical determinants of human renal clearance.
J Med Chem 2009, 52, 4844-52.
[3] Ito S, Ando H, Ose A, Kitamura Y, Ando T, Kusuhara H, Sugiyama Y, Relationship between theurinary excretion mechanisms of drugs and their physicochemical properties. J Pharm Sci 2013,
102(9), 3294-301
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P5-09Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
16. Prediction of sites of metabolism of drug by CYP2C9
by molecular simulation
Hiroaki Saito,

Takao Otsuka,

hiroaki.saito@riken.jp, totsuka@riken.jp,

Noriaki Okimoto,

Makoto Taiji

okimoto@riken.jp,

taiji@riken.jp

Laboratory of computational molecular design computational biology, RIKEN Center for
Biosystems Dynamics Research (BDR)

Keywords: Molecular dynamics, Molecular docking, Site of metabolism, Cytochrome P450 (CYP)
Cytochrome P450s (CYPs), a superfamily of hem-containing enzymes, are the major enzymes
involved in drug metabolism. In humans, it has been estimated that CYPs metabolize
approximately 75% of all marketed drugs, 95% of which are metabolized by CYP3A4, 2D6, 2C9
and 1A2 [1]. In the drug metabolism by the CYPs, identification of sites of metabolism (SOMs) on
molecules and the structure of their metabolites can be decisive for the design of molecules withfavorable metabolic properties. However, experimental techniques to determine SOMs andstructures of metabolites are still highly resource-demanding and challenging [2]. Thus,
developing fast and accurate computational methods to predict the SOMs/products of compounds
metabolized by the CYPs is one of the important tasks for the optimization of ADME and toxicity
properties.
In this study, we present a new computational method (score function) to predict SOMs ofcompounds metabolized by CYP2D6. The new score function is composed of accessibility andreactivity scores. The accessibility scores for the sites (atoms) of compounds are estimated byensemble docking simulation, while the activation energies of atoms estimated from the
SMARTCyp software[3] are used as the reactivity scores in the method. We carried out a molecular
dynamics (MD) simulation of apo type of CYP2D6 for 10 micro second to sample the receptor(pocket) structures for the ensemble docking. For the molecular docking, myPresto program withgrid score, which are optimized for CYP system [4], was used. We prepared 24 test set compounds
metabolized by the CYP2D5 and 100,000 receptor snapshots (which correspond to the 10 micro
second MD simulation) for the ensemble docking simulation. We found the success rate of thepredicted SOMs was 88 %, showing better result than those by the ensemble docking alone.
[1] Wienkers LC, heath TG., Nature Rev. Drug. Discov. 4 (2005), 825-833.
[2] Johannes Kirchmair et al., J. Chem. Inf. Model. 52 (2012), 617−648.
[3] P. Rydberg et al., ACS Med. Chem. Lett., 1 (2010), 96-100[4] Kiumars Shahrokh, Anita Orendt, Garold S. Yost, Thomas E. Cheatham, III, J. Comp. Chem.,
33 (2012), 119-33.
This work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P5-10Development of a pharmacokinetics prediction system using
multiscale integrated modeling:
17. Accuracy and performance of the MDGRAPE-4A system
Gentaro Morimoto

Yousuke Ohno

Teruhisa Komatsu

gentaro.morimoto@riken.jp

ohno@riken.jp

teruhisa.komatsu@riken.jp

Hao Zhang

Itta Ohmura

Keigo Nishida

Yohei M. Koyama

hao.zhang@riken.jp ohmura@riken.jp keigo.nishida.jg@riken.jp ym.koyama@riken.jp

Hiroshi Koyama

hkoyama@riken.jp

Makoto Taiji

taiji@riken.jp

Laboratory for Computational Molecular Design, RIKEN Center for Biosystems Dynamics
Research (BDR), 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan

Keywords: Molecular dynamics simulations, Ligand binding, Site of metabolism, Drug design
We have been developing a series of special purpose computers for molecular dynamics
simulations. The target performance is to make it possible to simulate typical protein -ligand
complex surrounded by water for microseconds per day, which is two orders of magnitude fasterthan commercially available systems. One of the applications of long -term MD simulations ofproteins is to predict the site of metabolism. In 2019 we have completed the MDGRAPE-4A, an
improved version of MDGRAPE-4[1]. The MDGRAPE-4 hardware development was completed in2014, and the stabilization of operation and software development process succeeded for two years.
However, the final performance did not reach the expected one. In the process of the development,
we realized that not only accelerating the computationally demanding part but also more specialized
hardware for the type of computation is required to improve the performance.
The MDGRAPE-4 and 4A systems both consist of 512 custom System on Chip (SoC) LSI chips in
3D torus network. The biomolecular systems are spatially divided into small cells and each LSI
communicate with each other to update the allocated cells. To improve the performance, we revised
the SoC LSI to enhance the functionalities as follows:
1) support of management and migration of atoms by the memory hardware
2) more sophisticated treatment of exclusion and reduced non -bonded interaction pairs in thespecial purpose pipelines3) implementation SIMD functions and special instructions required in the MD simulations in thegeneral-purpose cores4) reduction of the latency and more flexible routing patterns in the network interface5) support of a long-range electrostatic interaction calculation method that is suitable for theparallel systems with torus network topology
Currently the hardware installation has been completed. Software is still under development toimprove the accuracy and the performance and to implement more functionalities. In thispresentation, we would like to show some results of the evaluation of the accuracy and theperformance of the MD simulations by using the MDGRAPE-4A system.
[1] Ohmura, I., et al., MDGRAPE-4: a special-purpose computer system for molecular dynamics
simulations, Phil. Trans. R. Soc. A, 372:20130387, 2014.
Some of this work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P5-11QSAR model to predict Kp,uu,brain with small-scale dataset-incorporating predicted values of related parametersYuki Umemori 1
y.umemori@teijin.co.jp

1

Koichi Handa 1
ko.handa@teijin.co.jp

Seishiro Sakamoto 1

Shin Umeda 1

se.sakamoto@teijin.co.jp

s.umeda@teijin.co.jp

DMPK Research Department, Teijin Pharma Limited. 4-3-2, Asahigaoka, Hino, Tokyo,
191-8512, Japan

Keywords: K p,uu,brain, CNS, QSAR model[Objective] Unbound brain-to-blood ratio (K p,uu,brain ) is widely used as an indicator of brain
penetration (BP). However, it is hard to obtain the experimental value of K p,uu,brain for many
compounds in drug discovery stage, because it requires many experiments. So, somepharmaceutical companies targeting central nervous system (CNS) are actively performing insilico prediction of K p,uu,brain using their in-house QSAR model[1]. Their models were constructedusing not only small-scale published dataset but also large scale in-house dataset. For otherpharmaceutical companies which does not have enough in -house dataset, it is difficult to develop
such QSAR model[2]. Recently, we can use some software which provide each predicted value formany PK parameters. Although they cannot predict K p,uu,brain itself, they can predict someBP-related parameters ( BPrPs) . In this study, with the aim of constructing an accurate K p,uu,brain
prediction model with only a small-scale published dataset, we examined an effect of combinationof structure descriptors and the predicted values of some BPrPs as explanatory variables.
[Method] We used 88 compounds which experimental K p,uu,brain were available as our dataset.
Structure descriptors were calculated using ADMET Predictor® 9.0 (AP) and Schrödinger 2016.
The predicted values of several BPrPs were also calculated using AP. Random Forest [software:R3.4.4 (caret)] was used as a machine learning model. RMSE and R 2 calculated by 5-fold crossvalidation were used as the indices of prediction accuracy. Firstly, we developed prediction models
using structure descriptors. Then, we verified a predictive accuracy of each model which wereincorporated predicted value(s) of BPrPs in various combinations as explanatory variables. In
order to confirm the practicability of our model, K p,uu,brain of in-house compounds were predicted,
and compared with the experimental values.
[Result and Discussion] Compared to basic model, most models which were incorporated thepredicted value(s) of BPrPs showed much higher prediction accuracy. Among BPrPs, brain-to-blood
ratio (LogBB) contributed to much improve predictability. Our final model constructed by only
small-scale published dataset showed good predictability for in-house compounds. In conclusion,
we could build a highly accurate K p,uu,brain prediction model by incorporating BPrP for asmall-scale dataset. Based on this finding, it was shown that it is possible to build a highly
accurate prediction model by using relevant information even with a small-scale dataset.
[1] Liu, H. et al. (2018) Prediction of brain: Blood unbound concentration ratios in CNS drug
discovery employing in silico and in vitro model systems. Drug. Discov. Today. (7): 13 57-1372[2] Zhang, YY et al. (2016) Integrate in silico and in vitro approaches to predict drug accessibility
to the central nervous system. Mol. Pharm. 13, 1540 –1550

P5-12Cluster Gauss-Newton method for efficiently estimating
multiple sets of parameters: Application to
Physiologically-Based PharmacoKinetic models
Ken Hayami 1,2

Yasunori Aoki 1,3

hayami@nii.ac.jp

yaoki@uwaterloo.ca

Kota Toshimoto 3
kota.toshimoto@riken.jp
1
2

3

Yuichi Sugiyama 3
yu-one.sugiyama@nifty.com

National Institute of Informatics
Department of Informatics, School of Multidisciplinary Sciences,
SOKENDAI (The Graduate University for Advanced Studies)
Sugiyama Laboratory, RIKEN Baton Zone Program, RIKEN

Keywords: parameter estimation, nonlinear least squares problem, multiple global minimizers
PKPK model, pharmacokinetics
Parameter estimation problems of mathematical models can often be formulated as nonlinear leastsquares problems. Typically, these problems are solved numerically using iterative methods.
The local minimizer obtained using these iterative methods usually depends on the choice of theinitial iterate. Especially, when there is no unique global minimum to the nonlinear least squares
problem, the algorithm finds one of the solutions near the initial iterate. Hence, the estimatedparameter and subsequent analyses using the estimated parameter depends on the choice of theinitial iterate. One way to reduce the analysis bias due to the choice of the initial iterate is torepeat the algorithm from multiple initial iterates.
However, the procedure can becomputationally intensive and is not often implemented in practice. In pharmaceutical science,
there are many mathematical models where the parameters are not uniquely identifiable from theobservation, and this issue of non-unique global minimizers appears often. For example, themodel parameters of the physiologically based pharmacokinetics models are usually not uniquely
identifiable from the plasma concentration.
To overcome this problem, we propose the Cluster Gauss-Newton (CGN) method [1], an
efficient algorithm for finding multiple global minimizers of nonlinear least squares problems.
CGN simultaneously solves the nonlinear least squares problem from multiple initial iterates.
Then, CGN iteratively improves the solutions from these initial iterates similarly to theGauss-Newton method. However, it uses a global linear approximation instead of the gradient.
The global linear approximations are computed collectively among all the initial iterates tominimize the computational cost and increase the robustness against convergence to local minima.
We use three PBPK model parameter analysis examples to demonstrate the use of CGN.
We show that the proposed algorithm is computationally more efficient and robust against local
minima compared to many of the conventional algorithms. Besides the algorithm development andthe numerical experiments, we have implemented a Graphical User Interface based software todisseminate our algorithm to pharmaceutical scientists.
[1] http://arxiv.org/abs/1801.06714v2

P5-13

Prediction of Health Effects of Food Peptides and Elucidationof The Mode-of-action Using Multi-task Graph Convolutional
Neural Networks
Itsuki Fukunaga 1

q238056i@mail.kyutech.jp

Kazuma Kaitoh 1

kaito168@bio.kyutech.ac.jp

Ryusuke Sawada 1

sawad330@bio.kyutech.ac.jp

Yukie Sakai 1

sakai541@bio.kyutech.ac.

Tomokazu Shibata 1

shiba535@bio.kyutech.ac.jp

Yoshihiro Yamanishi 1

yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka,
Fukuoka 820-8502, Japan

Keywords: foods, peptides, health effects, peptide-protein interaction, graph convolutional neuralnetwork, multi-task learning, deep learning
Food proteins work not only as nutrients but also modulators for physiological functions of human
body, and they are at the forefront of scientific and industrial attentions. These physiological functions
are basically regulated by some peptides encrypted in the food protein sequences, which we call “foodpeptides”. These bioactive food peptides can have beneficial health effects and can be a lead compound
for the development of functional and nutraceutical foods. In fact, there are a number of bioactive food
peptides that are beneficial for human health, but the mode-of-action of bioactive food peptides has notbeen well understood, and their potential health effects remain to be identified. In this study, we
propose a novel deep learning-based method to elucidate the mode-of-action of functional food
peptides and to predict new health effects of the food peptides, which is made possible by an extensionof the concept of drug repositioning from drugs to food peptides. In the algorithm, we represent each
peptide structure by a chemical graph, estimate its potential target proteins by multi-task graphconvolutional neural networks (GCNNs), and predict new its health effects using the information on
therapeutic targets for diseases [1]. We construct predictive models based on 23,933 peptide-protein
interactions involving 10,951 peptides and 2533 proteins, and apply the models to analyzing available
food peptides including foods for specified health use (referred to TOKUHO in Japan). We also
compare the performance of the GCNN model with that of the recurrent neural network (RNN) modelin which each peptide structure is represented by a string of amino acids. We show some examples ofthe mode-of-action analysis and newly predicted health effects of food peptides. The proposed methods
are expected to be useful for practical applications in the development of functional foods, drugs, andcosmetics.
[1] Sawada, R., Iwata, H., Mizutani, S., and Yamanishi, Y., Target-based drug repositioning usinglarge-scale chemical-protein interactome data, Journal of Chemical Information and Modeling, 55(12),2717–2730, 2015.

P5-14

Case study of machine learning for drug metabolism andpharmacokinetics properties
Kazuyoshi Yoshii 1

Yusuke Hoshino 1

kazuyoshi-yoshii@zeria.co.jp

Ryoko Toda 1
1

Yoshihiro Kawabata 1

Toxicology&Pharmacokinetics Research, Central Research Laboratories,
Zeria Pharmaceutical Co., Ltd, 2512-1, Numagami, Oshikiri, Kumagaya-shi, Japan

Keywords: drug metabolism, cytochrome P450, machine learning
Machine learning is used in natural language processing, analysis of stock market, customer
consumption etc. As data in these fields are complex and diverse, a gold standard method hasn’t
been built for machine learning in these fields. Machine learning in drug metabolism andpharmacokinetics (DMPK) is basically based on compound structure information, so a common
strategy in feature engineering and classifier selection might be developed. To explore the common
strategy for machine learning in DMPK, the effects of feature engineering and classifier selection
on prediction were evaluated using open source data of cytochrome P450 (CYP) inhibition.
Features of compounds were generated using open source software. Number of features,conversion to log-scale and deletion of noise were evaluated. One to eight classifiers were used.
The effects of voting and stacking were evaluated.
Accuracy of prediction using untransformed features depended on classifier in the range of 0.8 to
0.85. Deletion of noise in features highly improved accuracy of prediction to approximately 0.9.
The classifier using gradient boosting accurately predicted positive and negative about CYP
inhibition. The voting and stacking using some classifiers slightly improved accuracy of
prediction.
These results suggested that feature engineering like deletion of noise was most effective in thisstudy. If cases like this study were accumulated in machine learning of DMPK, common strategy
would be found in the near future.

P5-15

Free Energy Landscapes of Cyclic Hexapeptide
Diastereomers by Multicanonical Molecular Dynamics
Simulations
Satoshi Ono 1

Matthew R. Naylor 2

Chad E. Townsend 2

Ono.Satoshi@mg.mt-pharma.co.jp

Chieko Okumura 1
1

2

Okimasa Okada 1

R. Scott Lokey 2

Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000,
Kamosida-cho, Aoba-ku, Yokohama, Kanagawa 227-0033, Japan
Department of Chemistry and Biochemistry, University of California Santa Cruz, 1156 HighStreet, Santa Cruz, California 95064, United States

Keywords: Cell permeability, Cyclic peptide, Molecular dynamics simulations
Efficient conformational sampling of cyclic peptides is an emerging interest for assigningphysical properties, such as cell permeability. Conventionally, conformational sampling inchloroform has been used to mimic the membrane environment. Recently, experimentalhydrocarbon-water distribution coefficients, logD hc/w , showed excellent correlation with cellpermeability [1], suggesting that hydrocarbons, such as cyclohexane, can be alternative solventsfor mimicking membrane environments.
Multicanonical molecular dynamics (McMD) simulations were performed to obtain
conformational ensembles of eight cyclic hexapeptide diastereomers in explicit cyclohexane,
chloroform, and water. Free-energy landscapes (FELs) for each compound and solvent wereobtained from principal component analysis at T = 300 K. From the FELs, we introduced a new
flexibility index and an overlap between FELs.
Detailed analysis of ensembles at T = 300 K revealed that the average solvent-accessible surface
area (SASA) in cyclohexane showed excellent correlation with cell permeability (R 2 =0.872),
whereas this correlation was weaker in chloroform. We also found that single stereoisomericchange affects the conformational patterns, resulting in permeability differences. A possible
strategy for designing permeable cyclic peptides from FELs by McMD simulations is proposed.
[1] Naylor, M. R., et al., J. Med. Chem., 61:11169-11182, 2018.
[2] Ono, S., et al., J. Chem. Info. Model., 59:2952-2963, 2019.

P6-01
Interpretable Reaction Prediction using
Graph Convolutional Networks
Shoichi Ishida 1
ishida.shouichi.57a@st.kyoto-u.ac.jp

Kei Terayama 2,3,4

Ryosuke Kojima 4

kei.terayama@riken.jp

kojima.ryosuke.8e@kyoto-u.ac.jp

Kiyosei Takasu 1
takasu.kiyosei.6r@kyoto-u.ac.jp

1

2
3

4

5

Yasushi Okuno 3,4,5
okuno.yasushi.4c@kyoto-u.ac.jp

Graduate School of Pharmaceutical Sciences, Kyoto University, Yoshida, Sakyo-ku, Kyoto
606-8501, JapanRIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo 103-0027, Japan
Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology andInnovation Hub, Tsurumi-ku, Kanagawa 230-0045, Japan
Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, Sakyo-ku, Kyoto
606-8507, JapanFoundation for Biomedical Research and Innovation at Kobe, Center for Cluster Development
and Coordination, Chuo-ku, Kobe, Hyogo 650-0047, Japan

Keywords: Reaction prediction, Graph convolutional networks, Integrated gradients,
Visualization, Interpretability
Improving reaction prediction and its interpretability are essential issues for spreading datadriven retrosynthesis approaches to chemists. Various research groups have been addressing datadriven approaches for reaction prediction and retrosynthetic analysis, and the approaches haveshown excellent performances[1-3]. Although the performances have progressed due to recent
advances of machine learning and deep learning techniques, problems such as improving capabilityof reaction prediction and the black-box problem of neural networks persist for practical use bychemists. To spread data-driven approaches to chemists, we focused on two challenges:
improvement of reaction prediction and interpretability of the prediction. Here, we propose aninterpretable prediction framework using Graph Convolutional Networks (GCN)[4] for reaction
prediction and Integrated Gradients (IGs)[5] for visualization of contributions to the prediction toaddress these challenges[6]. As a result, from the viewpoint of average accuracies, our model
showed better performances than the existing model based on Extended-Connectivity Fingerprint(ECFP), which was used in Segler’s Alphachem[2]. Furthermore, IGs based visualization of the
GCN prediction successfully visualized reaction-related atoms.
[1] Segler, M. H. S., Waller, M. P., Neural-Symbolic Machine Learning for Retrosynthesis andReaction Prediction, Chemistry - A European Journal, 23:5966-5971, 2017[2] Segler, M. H. S., Preuss, M., and Waller, M. P., Planning chemical syntheses with deep neuralnetworks and symbolic AI, Nature, 555:604-610, 2018.
[3] Coley, C. W., Green, W. H., and Jensen, K. F., Machine Learning in Computer-Aided
Synthesis Planning, Accounts of Chemical Research, 51:1281-1289, 2018.
[4] Kipf, T. N., and Welling, M. S., Semi-Supervised Classification with Graph Convolutional
Networks, International Conference on Learning Representations (ICLR), 2017.
[5] Sundararajan, M., Taly, A., and Yan, Q., Axiomatic Attribution for Deep Networks,
International Conference on Machine Learning (ICML), 2017.
[6] Ishida, S., Terayama, K., Kojima, R., Takasu, K., and Okuno, Y., Prediction and Interpretable
Visualization of Synthetic Reactions using Graph Convolutional Networks, ChemRxiv,
10.26434/chemrxiv.8343995, 2019.

P6-02
Applicable Machine Learning Method to Predict Site ofMetabolism~Comparison of Various Methods Using In-house
Compounds ~Katsunori Sasahara
Sasahara.Katsunori@otsuka.jp

Hiroyuki Sasabe
Sasabe.Hiroyuki@otsuka.jp

Tomoki Suzuki
Suzuki.Tomoki@otsuka.jp

Masakazu Shibata
Shibata.Masakazu@otsuka.jp

Kenji Takeuchi
Takeuchi.Kenji@otsuka.jp

Takahiro Shimada
Shimada.Takahiro@otsuka.jp

Ken Umehara
Umehara.Ken@otsuka.jp

Nonclinical Research Center, Tokushima Research Institute, Otsuka Pharmaceutical Co., Ltd.
463-10, Kagasuno, Kawauchi-cho Tokushima 771-0192, Japan

Keywords: Machine Learning, ADME Prediction, Drug design, Artificial intelligence (AI)
Pharmaceutical companies have a large amount of high-quality data obtained by high throughput
screening (HTS) under the same experimental conditions, which are expected to utilize effectively as
part of a big data analysis. We previously focused on machine learning in many in silico methods
including molecular dynamics, quantum mechanics 1 and have succeeded in building a highly accurate
ADME prediction models for membrane permeability, protein binding, metabolic stability, CYP
inhibition, and some transporter recognitions using HTS data 2 .
The established ADME prediction models, which turn compound structures into each ADME profile,
are important to understand ADME profile of candidates. Moreover, considering that these predictions
are used for design of new compound, determination of structurally active sites (atoms or parts affected
to study results) is more important. If structurally active sites are shown by in silico methods, we canchange the corresponding structure before synthesis, which enables us to accelerate design process.
Machine learning methods are generally considered as black boxes which just only turn input into
output and inclined to believe that only prediction results can be obtained. However some methods cankeep track of the internal decision-making process. This means structurally active sites areunderstandable by analysis of the machine learning process.
In this study, we developed the some active atom visualization models which prioritized andhighlighted metabolically active sites (atoms or parts), using a data set of human liver microsomal
metabolic activity of approximately 30000 in-house compounds with various methods (Soft used:
PipelinePilot, Chainer Chemistry, MOE etc). Proposed sites of metabolism on the structure were
evaluated by a comparison with the actual site which are revealed in the microsomal metabolic
experiments of some in-house compounds using LC-MS/MS. The active atom visualization models,
which can show actual sites of metabolism, were successfully developed.
The procedure of active atom visualization models are considered to be applicable not only to theprediction of metabolic site but also to other studies related to efficacy, toxicology and other DMPK
issues. The molecular design at a discovery stage which is required simultaneous optimization ofvarious issues is expected to become more efficient by showing structurally active sites in many
experiments.
[1] Sasahara K, Mashima A, Yoshida T, Chuman H (2015) Molecular dynamics and density functional
studies on the metabolic selectivity of antipsychotic thioridazine by cytochrome P450 2D6:
Connection with crystallographic and metabolic results. Bioorg Med Chem 23:5459-5465[2] Sasahara K (2018) 薬物動態予測システムを用いた 0 次スクリーニング, 第 41 回薬物動態談
話会年会, invited lecture など

P6-03In Silico Modeling to Predict Drug-induced
Phospholipidosis Based on Machine Learning Approach
Yui Migura 1

Katsunori Sasahara 2

Migura.Yui@otsuka.jp

Chifumi Nagano 1

12

Yasuhiro Iwata 1

Kaori Abe 1

Department of Investigative Toxicology, Nonclinica l Research Center, Tokushima ResearchInstitute, Otsuka Pharmaceutical Co., Ltd., 463-10 Kagasuno, Kawauchi-cho Tokushima
771-0192, Japan
Department of Drug Metabolism and Pharmacokinetics, Nonclinical Research Center,
Tokushima Research Institute, Otsuka Pharmaceutical Co., Ltd., 463-10 Kagasuno,
Kawauchi-cho Tokushima 771-0192, Japan

Keywords: Machine Learning, QSAR, Phospholipidosis, Toxicity, Drug Design

The onset of drug-induced phospholipidosis leads to a delay in the development of new drugs,
thus early detection of it is desirable. We have previously developed an in vitro screening system
to detect the potential of the onset of phospholipidosis.
In this study, we created an in silico predictive model of phospholipidosis by machine learning
using the in vitro data and a public database. This model was applied to even earlier detection andde novo design of compounds.
We evaluated various predictive models using several data sets of descriptors for 717 in-housecandidate compounds and 793 known compounds in ChEMBL by accuracy and F-value. 2D
physicochemical descriptors were calculated by MOE, and structure descriptors such as ECFP were
calculated by Discovery Studio.
As a result, better predictions were obtained when using 2D physicochemical descriptors ofinternal and external compounds as training-data set. And, compared with known models(Ploemen[1], Pelletier[2], Tomizawa[3] and DEREK), our in silico model obtained better resultswith accuracy of 78%, F-value of 78% and area under an ROC curve of 0.86.
In addition, we created a system that proposes structures avoid ing phospholipidosis by evaluating
new compounds generated by substituent change automatically.

[1] Plomen JP., Use of physicochemical calculation of pKa and CLogP to predict
phospholipidosis-inducing potential: a case study with structurally related piperazines, Exp.
Toxicol. Pathol., 2004, 55, 347-55[2] Pelletier D., Evaluation of a Published in Silico Model and Construction of a Novel BayesianModel for Predicting Phospholipidosis Inducing Potential, J. Chem. Inf. Model., 2007, 47,
1196-1205.
[3] Tomizawa, K., Physicochemical and cell-based approach for early screening ofphospholipidosis-inducing potential, J. Toxicol. Sci., 2006, 31, 315– 324

P6-04In silico models for predicting hepatotoxicity andrenal toxicity based on HESS database
Tatsuya Ochibe

Kaori Ambe

C182711@ed.nagoya-cu.ac.jp

ambek@phar.nagoya-cu.ac.jp

Masahiro Tohkintohkin@phar.nagoya-cu.ac.jp

Graduate School of Pharmaceutical Sciences, Nagoya City University, 3-1
Tanabe-dori, Mizuho-ku, Nagoya 467-8603, Japan

Keywords: Machine learning method, Random forest, Hepatotoxicity, Renal toxicity
It is desirable to develop alternative methods for animal experiments to reduce the number ofexperimental animals and improve efficiency of the risk assessment of chemical substances. In
silico methods for predicting chemical toxicity are considered to be useful for alternative methods.
A high-quality toxicity testing database and appropriate prediction methods are necessary for thein silico toxicity evaluation. Hazard Evaluation Support System Integrated Platform (HESS) [1] byJapanese National Institute of Technology and Evaluation, which is open toxicity knowledge
database, contains information on repeated dose toxicity (RDT) of rat. In this study, we developedin silico models focused on the hepatotoxicity and renal toxicity which are often observed in RTD
studies by using HESS and machine learning methods, and tried to examine the toxicity endpoints
that could be predicted high performance.
At first, we investigated hepatic and renal toxicological findings reported in HESS. A wide
variety of toxicological findings related to hepatotoxicity or renal toxicity were reported in HESS.
For the predicting hepatotoxicity and renal toxicity, we developed 4 prediction models,
respectively. In hepatotoxicity, we predicted (1) the increase of liver weight, (2) hepatic
histopathological findings, (3) the combination of the increase of liver weight and hepatic
histopathological findings and (4) alanine aminotransferase (ALT) and aspartate aminotransferase(AST) level rise. In renal toxicity, we predicted (1) the increase of kidney weight, (2) renal
histopathological findings, (3) the combination of the increase of kidney weight and renal
histopathological findings and (4) creatinine and blood urea nitrogen (BUN) level rise. We
developed prediction models by the random forest using descriptors calculated by dragon 7 [2].
The performance of each model was evaluated in terms of matthews correlation coefficient (MCC)[3] using 5-fold cross-validation. All our prediction models achieved MCC of above 0.45. For
predicting both hepatotoxicity and renal toxicity, the model showed the best prediction
performance (MCC: hepatotoxicity 0.558, renal toxicity 0.631) when predicting the combination of
the increase of organ weight and the histopathological findings. These results suggested that our insilico models are robust and promising method for hepatotoxicity and renal toxicity prediction.
[1] http://www.nite.go.jp/en/chem/qsar/hess-e.html[2] https://www.affinity-science.com/dragon.html[3] Brown JB., et al., Classifiers and their Metrics Quantified, Molecular Informatics, 37(1-2):
1700127, 2018

P6-05Prediction of pharmacological activities from chemical
structures with graph convolutional neural network
Miyuki Sakai 1,2

Kazuki Nagayasu 1

sakai.miyuki.28w@st.kyoto-u.ac.jp

nagayasu@pharm.kyoto-u.ac.jp

Shuji Kaneko 1
skaneko@pharm.kyoto-u.ac.jp

12

Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto
University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan
Medical Database Ltd., 2-5-5 Sumitomoshibadaimon building, Shibadaimon, Minato-ku, Tokyo,
105-0012, Japan

Keywords: Graph convolutional neural network, ChEMBL
The first step of pharmacological activity is the binding of a chemical compound to its target
protein, whose efficacy depends on the affinity of ligand to the t arget. Accordingly, enormous
experimental efforts have successfully determined the affinity of molecule -target interactions.
Even though precise docking simulation may help estimate the receptor affinity w hen thethree-dimensional (3D) structure of the receptor is given, it is impracticable in the case of largetransmembrane proteins or ion channel complexes, since most of the structures still remains to be
uncovered, and many of them have allosteric binding sites. In this study, we collected theexperimentally-confirmed affinity data (for example, IC50 values) and SMILES data of the ligands
from ChEMBL25 database to build regression models by graph convolutional neural networks, andoptimized the prediction performance by tuning hyper parameters. A wide range of 18 targets are
selected from kinases with known 3D structures to transporters and ion channels with unknown
or limited structures. Overall, better prediction performance of the model was obtained in parallelwith the increase of the number of raw data. In a case of dopamine transporter with a large numberof 5908 records, the coefficient of determination was 0.74. Even in the case of TRPA1 channel
with a small number of 621 records, a good value of 0.72 was obtained. We will present more
examples and discuss the significance of this approach in the presentation.

P6-06
Design of Selective GPCR Antagonists with a desired
Pharmacophore using Deep Reinforcement Learning
Chisato Kanai 1
kanai@intage.com

Atsushi Yoshimori 2
yoshimori@itmol.com

Enzo Kawasaki 1
kawasaki@intage.com

Tomohiko Tasaka 3tasaka@affinity-science.com

1

INTAGE Healthcare, Inc.; 79 Kankoboko-cho, Shimogyo-ku, Kyoto, 600-8009, Japan
Institute for Theoretical Medicine, Inc., 26-1 Muraoka-Higashi 2-chome, Fujisawa, Kanagawa
251-0012, Japan.
3
Affinity Science Corporation, Aios Gotanda-ekimae, 1-11-1 Nishi-Gotanda, Shinagawa, Tokyo
141-0031, Japan.
2

Keywords: De novo design, Deep reinforcement learning, Pharmacophore model, Chemical
genomics-based virtual screening, Selective GPCR antagonist
The objective of drug design is to find chemical structures that fit a pocket of a target protein in
both shape and pharmacophore features. In recent years, the use of deep generative models using
reinforcement learning is getting a lot of attention as an effective method of de novo design [1].
We have previously developed a strategy to generate selective kinase inhibitors with a desired
pharmacophore using deep reinforcement learning and chemical genomics-based virtual screening(CGBVS) method [2].

G protein-coupled receptors (GPCRs) are the most intensively focused class of target proteins,because GPCRs are some of the most successful therapeutic targets for serious diseases.
Our strategy can be adopted into general use for generating selective GPCR antagonists oragonists with a desired pharmacophore. In this work, we have employed this strategy to generate
molecular structures of CXCR4 antagonists and evaluated their selectivity by predicting
interactions with 214 GPCRs using CGBVS. A detailed analysis and application will be presented
in the meeting.
[1] Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H., J. Molecular de-novo design throughdeep reinforcement learning, Journal of Cheminformatics, 9:1, 2017.
[2] Yoshimori, A., Kawasaki, E., Kanai, C., Tasaka, T., Strategies for design of molecular
structures with a desired pharmacophore using deep reinforcement learning, submitted for
publication.

P6-07High-performance predication model utilizing a novel deeplearning-based QSAR analysis using Deep Snap and the Tox
21 10k library
Yasunari Matsuzaka

Yoshihiro Uesawa

matsuzys@my-pharm.ac.jp

uesawa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo,

Keywords: Deep Snap, Tox21, Deep learning, QSAR, Tox21 10K library
The Toxicology in the 21 st Century (Tox21) project has profiled a library of more than 10,000
chemical compounds including commercial chemicals, pesticides, food additives, and drugs,
referred to as the Tox21 10K library [1]. To this data, most of the toxicological assessment
methods for novel chemical compounds displaying agonist or antagonist activities rely on animalmodels. Such approaches can, however, be expensive, time-consuming, and most importantly
difficult to translate for humans. Hence, alternative approaches to traditional animal models must
be investigated [2]. On the other hand, in silico approaches require the feature selection to avoid
the combinatorial optimization problems. Furthermore, finding the suitable combination andfeature learning to reduce overfitting by numerous molecular descriptors requires complex
technical skills and is time-consuming [3]. Recently, a novel technique for capturing molecularfeatures from molecular structure input data has been utilized [4]. In this study,
three-dimensionally (3D) optimized molecular structures were rotated at an arbitrary angle on thex-, y-, and z-axes and photographed. In the obtained images, the arrangement of each atomic
symbol was depicted as a ball-and-stick model, with different colors denoting different atoms.
Automatically inputting as much structural information as possible into the deep learning (DL)models was referred to as Deep Snap [4]. Previously, the performance of the DL prediction modelusing the image input data produced by Deep Snap showed that the receiver operatic characteristic(ROC-AUC) value in the Deep Snap-DL approach outperformed that of the previously reported
state-of –the -art methods using random forest (RF) and DL with 3D or extended-connectivity
fingerprint descriptors [4]. Furthermore, optimization of some parameters in the D eep Snap
technique has revealed the existence of optimal thresholds to attain the best performance of theprediction models [5]. In the current study, our aim was to assess the input datasets and parametersin the Deep Snap-DL method and investigate how they affect the prediction performance. Excellent
performance of the prediction model was observed and calculation cost was reduced using the
Deep Snap approach. The Deep Snap software is developed by grant in Long-Range Research
Initiative (LRI), Japan Chemical Industry Association (JCIA) and AI-Substances Hazardous
Integrated Prediction System (AI-SHIPS) project.
[1] Sipes, N.S. et al., An Intuitive Approach for Predicting Potential Human Health Risk with the
Tox21 10k Library, Environ Sci Technol, 51(18):10786 -10796, 2017.
[2] Freires, I.A., Sardi, J.C., de Castro, R.D., Rosalen, P.L. Alternative Animal and Non-Animal
Models for Drug Discovery and Development: Bonus or Burden ?, Pharm Res, 34(4), 681 -686,
2017.
[3] Ponzoni, I., et al., Hybridizing Feature Selection and Feature Learning Approaches in QSAR
Modeling for Drug Discovery, Sci Rep, 7(1):2403, 2017.
[4] Uesawa, Y. Quantitative structure-activity relationship analysis using deep learning based on anovel molecular image input technique, Bioorg Med Chem Lett, 28(20), 3400 -3403, 2018.
[5] Matsuzaka, Y., Uesawa, Y. Optimization of a Deep-Learning Method Based on theClassification of Images Generated by Parameterized Deep Snap a Novel
Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR)
Analysis, Front Bioeng Biotechnol, 7:65, 2019.

P6-08Development of AI-aided hit compound finding/profiling system
for imaging-based high content screening
Hiroki Terauchi 1
h-terauchi@hhc.eisai.co.jp

Kaoru Mitsuhashi 1
k2-mitsuhashi@hhc.eisai.co.jp

Makiyo Iida 1
m3-iida@hhc.eisai.co.jp

Hajime Shimizu 1

Sachie Taniguchi 1

h4-shimizu@hhc.eisai.co.jp

Shuichi Suzuki 1
s3-suzuki@hhc.eisai.co.jp
1

Naoko Tsukahara 1
n-tsukahara@hhc.eisai.co.jp

s3-taniguchi@hhc.eisai.co.jp

Katsuyuki Fukuda 1
k4-fukuda@hhc.eisai.co.jp

Tsukuba Research Laboratories, Eisai Co., Ltd. , Tokodai 5-1-3, Tsukuba-shi,
Ibaraki 300-2635, Japan

Keywords: Artificial Intelligence (AI), deep neural network, cell imaging, hit profiling, Deep
Metric Learning
Background:
Recently Artificial Intelligence (AI) especially deep neural network is attracting attention
in drug discovery.
We have been using high content cell imaging in early drug discovery. However, the image
analysis has high computer complexity, and requires a lot of time and effort to identify true hit
samples [1]. To address this issue, we tried to develop imaging analyzing system with AI.
Results:
We established an AI-aided imaging hit finding system using deep neural network. AI
learned the difference between negative and positive control images. AI identified compoundswith the similar phenotype as the positive controls with high accuracy. Furthermore, by applying
the reference compound library as a training set, we were able to predict the mode of action (MOA)of various compounds.
In addition, we recently found a brand-new learning method, Deep Metric Learning [2],also performed possible similar profiling without reference compound library. We would like todiscuss the usefulness of our methodology.
[1] Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O. & Golland,
P. (2006). CellProfiler: image analysis software for identifying and quantifying cell phenotypes.
Genome biology, 7(10), R100.
[2] Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss fordeep face recognition. In Proceedings of the IEEE Conference on Comp uter Vision and Pattern
Recognition (pp. 4690-4699).

P6-09Computational drug target prediction using PU learning
approach.
Kazuto Nakata 1
k-nakata@ay.jp.nec.com

Takeshi Fujiwara 3
takeshi.fujiwara@nibiohn.go.jp

1

2

3

Hiroaki Iwata 2
iwata.hiroaki.3r@kyoto-u.ac.jp

Mari Ito 3
mari@nibiohn.go.jp

1st Government and Public Solutions Division, NEC Corporation, 5-7-1, Shiba, Minato-Ku,
Tokyo, 108-8001, Japan
Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Syogoin, Sakyo-ku, Kyoto
606-8507, Japan
Bioinformatics project, National Institutes of Biomedical Innovation, Health and Nutrition
7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan

Keywords: Machine Learning, PU Learning, Drug Target Prediction
Drug target discovery is one of the most important processes in drug development. Computational
drug target prediction reduces times and costs to evaluate drug target. Recently, some drug target
prediction models were proposed by using machine learning approaches [1][2]. However, these
proposed models use several data sources which include few positive samples. To solve thisproblem, we employed positive-unlabeled learning (PU learning) approach [3] using publicly
available data sources. By compared with known models, PU learning model achieved
comparable results. These results indicated that our model using publicly available data sources isan effective tool for exhibiting proof of concept to predict drug targets with few positive data
sources. This research was carried out as one of the activities of Life Intelligence Consortium(LINC).
[1] Ferrero E, Dunham I, Sanseau P., In silico prediction of novel therapeutic targets using
gene-disease association data. J Transl Med. 2017 Aug 29;15(1):182.
[2] Yao J, Hurle MR, Nelson MR, Agarwal P., Predicting clinically promising therapeutic
hypotheses using tensor factorization. BMC Bioinformatics. 2019 Feb 8;20(1):69.
[3] Elkan C, Noto K., Learning Classifiers from Only Positive and Unlabeled Data, Proceedings ofthe 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Pages 213-220.

P6-10

Meta-modeling for Optimization in QSAR Modeling Processes andApplication to Estrogen Receptor Agonist Activity Prediction
Kota Kurosaki 1
d196955@std.my-pharm.ac.jp

1

Yosihiro Uesawa 1
ueswa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo

Keywords: Machine learning, Virtual screening, Tox21
Virtual screening is a powerful tool for the rapid and comprehensive screening of compoundproperties in safety assessment and drug discovery. Although many empirical studies havecompared the various algorithms involved in model construction, there are still no definite
conclusions about the cause of a specific algorithm’s ability or inability to work properly in a
specific domain. Therefore, it is difficult to artificially optimize selection decisions in themodeling process from specific domains for the changing environment of each project.
We aimed to build a model that could use machine learning for selecting the best option in eachstep of the modeling process. First, the optimization, which is known as the Bayesian optimization(BO) introducing the Gaussian process, of the hyperparameters of the decision -tree-based models(Random Forest (RF), Light GBM (LGB), and XG Boost (XGB)), was performed using machinelearning. This optimization method is a technique that is able to explore an optimal solution more
efficiently [1]. Second, under the assumption that the entire QSAR modeling process itself was afunction (meta-model), we extended the BO from determining the dataset fraction to setting theclassifier hyperparameters in a simple modeling process. These models were implemented using
Python for the prediction of ERlbd signaling pathway active compounds stored in the Tox 21 10 Kcompound library.
For the first approach, BO was superior to the conventional optimization method in terms of bothtime efficiency and trial efficiency. Also, the classifier built by BO yielded better ROC-AUC in thetest set as compared to the non-optimized model’s one. In addition, RF and LGB were able toautomatically construct a high-performance model comparable to the Tox 21 data challenge world
ranking. However, for the second approach, neither further improvement in ROC-AUC wasobserved nor was the dataset split rate’s convergence to a certain value confirmed.
Previous studies have shown that human optimization can build high -performance prediction
models for ERlbd active compounds [2,3] but is very time- and labor-intensive. On the other hand,the optimization with BO enabled us to construct a highly efficient and high -performance toxicity
prediction model. Also, no performance improvement, including the data set partitioning pro cess,was confirmed by meta-modeling, suggesting that hyperparameter regulation was the mostinfluential process on the model’s performance for the prediction model developed at this time.
Thus, we expect that constructing the meta-model would allow us to comprehensively andefficiently determine the process or compound representation that would be influential (orinsignificant) and appropriate for the final evaluation.
[1] J. Snoek, H. Larochelle, and R.P. Adams, IN NIPS, 2012[2] Y. Uesawa, Front. Environ. Sci., 4, 1–6, 2016[3] Y. Aasako, Y. Uesawa, Molecules, 22, 1–10, 2017

P6-11

Predicting drug-induced transcriptome responses
of a wide range of human cell lines
by a novel tensor-train decomposition algorithm
Michio Iwata 1

iwata121@bio.kyutech.ac.jp

Yasuo Tabei 3

Longhao Yuan 2,3longhao.yuan@riken.jp

Francois Berenger 1

Qibin Zhao 3,4

qibin.zhao@riken.jp

Ryusuke Sawada1

yasuo.tabei@riken.jp

beren314@bio.kyutech.ac.jp

sawad330@bio.kyutech.ac.jp

Sayaka Akiyoshi 5

Momoko Hamano 1

Yoshihiro Yamanishi 1

akiyoshi-s@bioreg.kyushu-u.ac.jp haman929@bio.kyutech.ac.jp

yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
2
Graduate School of Engineering, Saitama Institute of Technology, 1690 Fusaiji, Fukaya,
Saitama 369-0293, Japan
3
RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027,
Japan
4
School of Automation, Guangdong University of Technology, Panyu, Guangzhou, Guangdong,
China
5
Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka,
Fukuoka 812-8582, Japan

Keywords: imputation, tensor decomposition, gene expression, human cell line, drug discovery
Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments
is a challenging issue in medical and pharmaceutical research. However, drug-induced gene
expression profiles are largely unknown and unobserved for all combinations of drugs and humancell lines, which is a serious obstacle in practical applications. In this study [1], we developed anovel computational method to predict unknown parts of drug-induced gene expression profiles forvarious human cell lines and predict new drug therapeutic indications for a wide range of diseases.
We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential
values for unknown parts in tensor-structured gene expression data. Our results revealed that the
proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for arange of human cell lines in the Library of Integrated Network-based Cellular Signatures. The
results also revealed that in comparison with the use of original gene expression profiles, the useof imputed gene expression profiles improved the accuracy of drug repositioning. We also
performed a comprehensive prediction of drug indications for diseases with gene expression
profiles, which suggested many potential drug indications that were not predicted by previous
approaches.
[1] Iwata et al., Predicting drug-induced transcriptome responses of a wide range of human celllines by a novel tensor-train decomposition algorithm, Bioinformatics, 35 i191–i199, 2019.

P6-12

3D-RISM-AI: A machine learning approach to predict
protein-ligand binding affinity using 3D-RISM
Kazu Osaki 1
W195405a@yokohama-cu.ac.jp

Toru Ekimoto 1
ekimoto@yokohama-cu.ac.jp

Tsutomu Yamane 1
tyamane@yokohama-cu.ac.jp

Mitsunori Ikeguchi 1, 2
ike@yokohama-cu.ac.jp

1

Graduate School of Medical Life Science, Yokohama City University,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan
2
RIKEN Medical Science Innovation Hub Program,
1-7-29 Suehirocho, Tsurumi-ku, Yokohama City 230-0045, Japan

Keywords: Machine learning, 3D-RISM, Binding affinity
Accurately predicting protein-ligand binding affinities is important in drug discovery, because theprotein-ligand affinity is a primary criteria to evaluate stabilities for abundant protein-ligand
complexes in virtual screening and/or docking simulation. The protein-ligand affinity is given by
the (relative) binding free energy, and many computational methods have been developed using
empirical scores and molecular dynamics simulations etc. Recently, the prediction of the affinity
has been evaluated by machine learning in which intermolecular interactions between protein andligand, structural features, and amino acid sequences were taken into account as descriptors [1].
In the binding free energy calculation, interactions of protein/ligand with solvent molecules are
considered as an important factor to improve accuracy of the prediction. Therefore, we propose amachine learning approach taking into account the interactions with solvent molecules(3D-RISM-AI). The interactions with solvent molecules were evaluated by the hydration freeenergy (HFE), and the HFE was calculated by the three-dimensional reference interaction model(3D-RISM) [2, 3]. In 3D-RISM, a thermally averaged distribution of solvent molecules around thesolute is theoretically obtained, and the HFE can be calculated through an integral equation using
the distribution function. We calculated ~3800 of the HFE for protein-ligand complexes in the
PDBbind database (v.2018) and used the data in the learning process. Whereas the binding freeenergies solely evaluated using 3D-RISM are not correlated with the experimental data, thebinding free energies predicted using 3D-RISM-AI show a good correlation with the experimental
values.
[1] Ain, Q. U. et al. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2015, 5, 405−424[2] D. Beglov and B. Roux, J. Chem. Phys. 103, 360, 1995.
[3] D. Beglov and B. Roux, J. Chem. Phys. 104, 8678, 1996.

P6-13
Deep Learning-aided Label-free, Real-time and Time-lapse
Cell Visualization System that Enables Live/Dead Cell
Discrimination and Counting
Tamio Mizukami 1, 2 , Mie Tsuchida 1, 2 , Miho Hosoi 1, 2 , Makoto Hasegawa 1 , Ryuzo Sasaki 1, 2,
Shigeru Nemoto 3 , Katsumi Kishimoto 2
mizukami@nagahama-i-bio.ac.jp
1

2
3

Nagahama Institute of Bio-Science and Technology, 1266 Tamura, Nagahama, Shiga, 526-0829,
Japan
Frontier Pharma Inc., 1281-8 Tamura, Nagahama, Shiga, 526-0829, Japan
Broad Band Tower Inc., 2-1-6 Uchisaiwaicho, Chiyoda-ku, Tokyo 100-0011, Japan

Keywords: Deep learning, Imaging, Cytotoxicity
At present, as a method for analysis of cell characteristics such as life and death anddifferentiation state, a fluorescence observation method in which a specific molecu le of a cell islabeled with an antibody or a fluorescence reagent to observe fluorescence is generally used.
However, in this method, cells are invaded by fluorescence labeling and excitation light irradiation,and continuous observation as live cells can not be performed. Therefore, we constructed a modelin which the relationship between the fluorescently labeled teacher cell image and thecorresponding bright field cell image was finely learned by deep learning. By utilizing the model,
we developed a technology that enables prediction of cell characteristics from a normal
microscope bright field cell image and counting of cells and nuclei.
The technology is based on learning models of pseudo -fluorescent image generation andcell nuclear localization as follows.
(1) First step (visualization of cells): A model is constructed in which the relationship between afluorescence microscope image of a cell treated with a fluorescent reagent for staining live anddead cells and a corresponding bright field image i s learned by deep learning. The input of theunknown bright field cell image to the constructed model makes it possible to generate a
pseudo-fluorescent cell image showing the characteristics of the cell without experimental work
such as fluorescence labeling.
(2) Second step (cell counting): The position of the cell nucleus detected on the generated
pseudo-fluorescent cell image is determined. For that purpose, a second model (position
determination model) is constructed in which a teacher image manually h it at the center position of
the cell nucleus on the fluorescence cell image is created and the relationship with thecorresponding fluorescence cell image is learned by deep learning. The input of thepseudo-fluorescent cell image to the present position determination model can generate an image
in which the center position of the cell nucleus on the fluorescent cell image is hit. A separately
created program can automatically count the number of detected representative points and countthe number of cells.
In the validation test of this technology, HepG2 , a human hepatoma cell line widely used inhepatotoxicity evaluation and MG132, a proteasome inhibitor, were used. As a result, clear
discrimination and counting of live and dead cells were possible.
This technology is capable of noninvasive, real time and time -lapse observation, and we hope that
it will be widely used in the fields of life sciences, medicine and drug discovery in the future.

P6-14Deep-learning of cancer stem cell morphology
Tomoyasu Sugiyama 1

Saori Aida 2

tsugiyama@stf.teu.ac.jp

aidasor@stf.teu.ac.jp

Tomonari Kasai 1

Atsushi Sato 1

Hiroyuki Kameda 2

kasaitnr@stf.teu.ac.jp

atsato@stf.teu.ac.jp

kameda@stf.teu.ac.jp

1

School of Bioscience and Biotechnology, Tokyo University of Technology, 1404-1 Katakura,
Hachioji, Tokyo 192-0982, Japan
2
School of Computer Science, Tokyo University of Technology, 1404-1 Katakura, Hachioji,
Tokyo 192-0982, Japan

Keywords: Artificial intelligence, Cancer stem cell, miPS-LLCcm cells
Cancer stem cells (CSCs) are thought to have an important role in tumour development. In
cancer therapies it has been concerned that tumour might possess a capability of regrowth after
suppression of large amount of tumour tissue after the therapy. In fact, many clinical results havesuggested the presence of cells which have not affected by those therapies. It is believed that CSCs
in tumour tissue could serve cancer cells which lacked the stem cell characteristics.
An experimental model showed that CSCs developed malignant tumour tissues (PLoS ONE 2012,
7: e33544). It is conceivable that mouse iPS-LLCcm (miPS-LLCcm) cell, a model of CSC, maydifferentiate to cancer cell while the cells may maintain self-renewal in mouse. It is useful to knowwhether a drag affects the characteristics of stem cell such as marker gene expression and cellmorphology. Accordingly, in vitro culture of miPS-LLCcm cells makes many kinds of cell-based
assays available.
In recent years, image recognition technologies are making remarkable advances by using
artificial intelligence (AI). An image-to-image translation of any photographs can be processed bya single algorithm without any specific settings to each photograph (arXiv 2016, 1611.07004v1).
Those examples of image translation were significant in accuracy and creativity. It is interestingwhether the code of conditional Generative Adversarial Networks (CGAN) can recognize cellmorphology. Accordingly, we showed the application of CGAN for the identification of CSCs fromphase contrast miPS-LLCcm cell image at the last CBI annual meeting 2018.
Here, we will report the progress of the research.
Methods: miPS-LLCcm cell harboring Nanog-GFP reporter gene was used to monitor thepluripotency of CSC. To evaluate cell morphology, miPS-LLCcm cells were cultured in 96-wellplate. CSC images of phase contrast and fluorescence pair were obtained using fluorescencemicroscopy BZ-X810. Each image was processed to make small size images. Then pairs of images
were subjected to deep-leaning using CGAN. CGAN was trained to learn translation from phase
contrast image to fluorescence image.
Results and Discussion: In the previous study, we obtained cell images using manually-operated
conventional fluorescence microscopy equipped with a color CCD camera. Almost 10,000 pairs
were used for the deep-learning, however, the trained-AI was hard to output appropriate
fluorescence image from phase contrast image. In contrast, current system enables us to obtain
CSC images under same condition even if many images are required to take. In addition,
monochrome CCD has potential to take more detailed image. We will discuss the output of AI
trained under the system which is supposed to use in drug screening.

P6-15
Study on adverse outcome pathways related to drug-induced
rhabdomyolysis using machine learning
Shunichi Sasaki

Yoshihiro Uesawa

y161150@std.my-pharm.ac.jp

uesawa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan

Keywords: JADER, Tox21, machine learning, QSAR, Rhabdomyolysis
Introduction: Drug-induced rhabdomyolysis (DIR) caused by various medicines including
dyslipidemia drugs is a serious adverse effect in pharmacotherapies. Destruction of the cell
membrane due to reduction of cholesterol is considered as one of the factor for onset of DIR
mechanism [1]. However, information on the adverse outcome pathway (AOP) related to DIR islimited. Therefore, in this study, the relationship between DIR and molecular initiating events(MIE), such as activation of nuclear receptors and stress response pathways in the AOP wasinvestigated using the Tox21-MIE database and the Japanese Adverse Drug Event Report database(JADER) published by the Pharmaceuticals and Medical Devices Agency (PMDA). Furthermore,
we constructed a DIR discrimination model based on quantitative structure -activity relationship(QSAR) analysis.
Methods: Initially, the odds ratio and P-value in Fisher’s exact test for rhabdomyolysis werecomprehensively calculated from JADER, and drugs inducing rhabdomyolysis were classified
based on both statistics. After that, based on the Tox21-MIE database, QSAR discrimination
models for 59 types of MIE related activities were constructed using LightGBM [2]. The QSAR
model was used to predict the MIE related activities of medicines listed in JADER. In addition, theprediction results of the medicines were applied to the DIR discrimination model.
Results and Discussion: As a result, it was suggested that estrogen and thyroid -stimulating
hormones may be related to DIR. A previous study [3] indicated that rhabdomyolysis occurred as acomplication of hypothyroidism; however, the exact involvement of estrogen andthyroid-stimulating hormones to trigger rhabdomyolysis has not yet been investigated.
[1] Dirk Moßhammer, et al., Mechanisms and Assessment of Statin-Related Muscular Adverse
Effects, Br J Clin Pharmacol. 78(3): 454–466, Sep 2014.
[2] Guolin Ke, et al., LightGBM: A Highly Efficient Gradient Boosting Decision Tree, NIPS, 2017.
[3] Nooshin Salehi, et al., Rhabdomyolysis in a Patient with Severe Hypothyroidism, Am J Case
Rep. 18: 912–918, 2017.

P6-16

A New Scoring Function for Protein Structure Assessment
Based on the Hydration Structure Information

1

Yasuomi Kiyota 1

Mayuko Takeda-Shitaka 1

kiyotay@pharm.kitasato-u.ac.jp

shitakam@pharm.kitasato-u.ac.jp

School of Pharmacy, Kitasato University, 5-9-1 Shirokane Minato-ku, Tokyo 108-8641, Japan

Keywords: Structure bioinformatics, machine learning, 3D-RISM theory
In the field of protein complex study, to understand the hydration structure is becoming an
increasingly important issue. A famous international contest for the prediction of protein complex
structure, the critical assessment of predicted interactions (CAPRI), also considered theimportance of water prediction inside the protein interface (Round 34 in 2015, http://www.ebi.
ac.uk/msd-srv/capri/round34/). This challenge was two complexes of pyocin, which is one of thebacteriocin, and the inhibited immunity protein. For solving this session, we focused on theposition of conserved water molecules around the interface and re flected the information in thealignments of the amino acid sequence. We got good results in this session, however, we noticed
that predicted models which had been much higher accuracy would not be evaluated “good”. We
consider that this reason is caused by selection scores for protein complex and we aim to develop a
new structure evaluated score for the predicted structure. In the process of development, we
focused on the hydration structure again. Therefore, a new structure evaluated score is based on
the protein hydration structure information. We calculated the 3D-RISM theory[1], which is strict
statistical mechanics theory, and performed the machine learning on the solvation energy obtained
from this theory.
At first, we analyzed the structure dataset SCOP2 (Structural classification of proteins)[2], whichhave a detailed and comprehensive description of the structural and evolutionary relationships , andselected 670 chains which could be carried out the 3D -RISM theory. After the 3D-RISM
calculations, the obtained solvation energies are decomposed by each residue . These decomposedenergies are accumulated with some structural indices for the amino -acid residues (e.g. accessible
surface area, secondary structure, and surrounding residues). We built the da taset for the solvationenergy and structural index, and performed the machine learning approach based on Deep Learningwith Keras Python library and Theano backend. After the reproducing solvation energies bystructure indices, we tried to evaluate the pr edicted models which are submitted in CASP12. As aresults of this verification, we reached that developed score should be used with ProQ3[3], whichis also structure evaluated score for protein, for highly precision and stability. The developed
structure evaluated score is faster and more accurate than the 3D-RISM theory.

[1] Kovalenko, A., and Hirata, F., Self-consistent description of a metal–water interface by the
Kohn–Sham density functional theory and the three-dimensional reference interaction site model,
J. Chem. Phys., 110:10095−10112, 1999.
[2] Andreeva, A., Howorth, D., Chothia, C., Kulesha, E. and Murzin, A. G., SCOP2 prototype: a
new approach to protein structure mining, Nucleic Acid Res., 42:D310-D314, 2014.
[3] Uziela, K., Shu, N.m, Wallner, B. and Elofsson, A., ProQ3: Improved model quality
assessments using Rosetta energy terms. Sci. Rep., 6:35509, 2016.

P6-17Development of in silico prediction model
for skin sensitization using the alternative tests dataset
Masaharu Suzuki 1
c162518@ed.nagoya-cu.ac.jp

Kaori Ambe 1Masahiro Tohkin 1

ambek@phar.nagoya-cu.ac.jp

tohkin@phar.nagoya-cu.ac.jp

Takashi Yamada 2
t-yamada@nihs.go.jp

1

2

Takao Ashikaga 2
takao.ashikaga@nihs.go.jp

Graduate School of Pharmaceutical Sciences, Nagoya City University,
3-1 Tanabe-dori, Mizuho-ku, Nagoya 467-8603, Japan
National Institute of Health Sciences, 25-26, Tonomachi, Kawasaki-ku,
Kawasaki 210-9501, Japan

Keywords: Direct Peptide Reactivity Assay, human Cell Line Activation Test, KeratinoSens™,
Local Lymph Node Assay, skin sensitization, Support Vector Regression, prediction model
Skin sensitization is a key endpoint for safety assessment of chemicals especially in cosmetics.
Although the animal tests such as Local Lymph Node Assay (LLNA) had been widely used for theskin sensitization, the European Union (EU) has banned animal tests in safety testing of cosmetic
ingredients since 2013. Therefore, the hazard identification method of skin sensitization is now
widely used based on three in vitro tests listed in Organization for Economic Co-operation andDevelopment (OECD) test guideline [1]: Direct Peptide Reactivity Assay (DPRA) [2],KeratinoSens™ [3], and human Cell Line Activation Test (h-CLAT) [4]. However, this hazard
identification method is not quantitative. In this study, we tried to develop in silico quantitative
prediction model of LLNA EC3 (Estimated Concentration needed to produce a stimulation index of3) values which indicate skin sensitization potency [5] using data of those three in vitro tests. We
used a set of 120 substances from the Cosmetics Europe database [6] to construct the in silico
prediction model using Support Vector Regression. The data was randomly divided the training
dataset for the model construction (90 substances) and the test dataset for the evaluation (30substances) of constructed models. As a result of the evaluation of SVR model, the contribution
rates (r 2 values) and root mean squared error (RMSE) were 0.433 and 0.753, respectively, whichsuggested the satisfied performance prediction model. In conclusion, in silico approach using three
in vitro tests dataset is useful for prediction LLNA EC3 values.

[1] M. Hirota, T. Ashikaga, H. Kouzuki., Development of an artificial neural network model forrisk assessment of skin sensitization using human cell line activation test, direct peptide
reactivity assay, KeratinoSens™ and in silico structure alert parameter, J Appl Toxicol, 38 (4):1-13, 2017[2] https://ntp.niehs.nih.gov/iccvam/suppdocs/feddocs/oecd/oecd-tg442c-508.pdf[3] https://ntp.niehs.nih.gov/iccvam/suppdocs/feddocs/oecd/oecd-tg442d-508.pdf[4] http://www.asas.or.jp/jsaae/eng/pdf/info_20170208.pdf[5] https://ftp.cdc.gov/pub/Documents/OEL/06.%20Dotson/References/OECD_2002.pdf[6] Nicole C. Kleinstreuer et al., Non-animal methods to predict skin sensitization (II): anassessment of defined approaches, Crit Rev Toxicol, 48 (5): 359-374, 2018

P6-18Focused Library Generative Model for GPCR Family
Gen Li 1

Nobuaki Yasuo 2

li.g.af@m.titech.ac.jp

yasuo@cbi.c.titech.ac.jp

Masakazu Sekijima 1,2
sekijima@c.titech.ac.jp

12

Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho,
Midori-ku, Yokohama, Japan
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259 -J3-23,
Nagatsuta-cho, Midori-ku, Yokohama, Japan

Keywords: drug discovery, GPCR family, selectivity, generative model
Developing novel drug for protein target in GPCR family is a difficult task, where the drugcandidates are required for high off-target selectivity [1]. Some ligand based and structure based
computational approaches have been reported. However, scanning chemical space is atime-consuming work and candidate molecules are lack of novelty.
In this work, we report a machine learning based model to generate novel drug-like molecules forprotein targets in GPCR family with high selectivity. Model training consists of two steps.
Doc2vec [2] model, which is used in natural language processing, is used for vectorizing protein
sequence to fixed-length vector which is the input condition of generative model. Subsequently,
protein sequence vectors and SMILES representations of corresponding Molecules are applied toconditional variational autoencoder [3] for model training. Novel molecules generated by thismodel represent high distributional similarity to training molecules which have high selectivity
and shown that this model can potentially expand ligand library for any specific protein family.
[1] Alexander S. Hauser, Misty M. Attwood, Mathias Rask -Andersen, Helgi B. Schiöth & David E.
Gloriam, Trends in GPCR drug discovery: new agents, targets and indications, Nature Reviews
Drug Discovery, 16:829–842, 2017.
[2] Le, Q. V., and Mikolov, T., Distributed representations of sentences and documents, ICML,
14:1188–1196, 2014.
[3] K. Sohn, H. Lee, and X. Yan, Learning structured output rep resentation using deep conditional
generative models, Advances in Neural Information Processing Systems , 2015.

P6-19Prediction of G4MP2-level Molecular Properties from
DFT-level Structures Using Deep Tensor Neural Network
Wan Mingda 1

Nobuaki Yasuo 2

wan.m.aa@m.titech.ac.jp

yasuo@cbi.c.titech.ac.jp

Masakazu Sekijima 1,2
sekijima@c.titech.ac.jp

Department of Computer Science, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho,
Midori-ku, Yokohama 226-8501, Japan
2
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259
Nagatsutacho, Midori-ku, Yokohama 226-8501, Japan
1

Keywords: Property prediction, Deep learning, Computational chemistry, Machine learning
It is important to predict molecular properties with high throughput and accuracy for drug
discovery and materials science. ab initio quantum mechanical methods, such as density functional
theory (DFT) have widely been used for molecular property calculations [1]. When the required
accuracy is as high as experimental error (<1kcal/mol), higher level calculations such as G4MP2
are used [2]. However, it is difficult to screen large number of molecules by high-level method,
because the computational cost of these methods is much larger than that of DFT. Machine learning
is a potential method to predict the molecular property accurately without using high-levelcalculations. Recently, the Δ-machine learning approach, which learns the difference between
rough calculations and accurate calculations, is reported to reduce the calculation cost of property
prediction [3]. However, the total prediction accuracy of previous works could not be as high as
chemical experiments because the dataset was based on DFT, whose error is larger thanexperimental error. In this study, we 1) constructed a G4MP2-level dataset for molecular property
prediction using TSUBAME3 supercomputer, and 2) performed Δ-machine learning-based
prediction by using deep tensor neural network. Our database named QM9-G4MP2, which is theextension of QM9 database [4], contains 133,885 molecules with DFT-level input structure and
G4MP2-level properties. The prediction results showed that deep tensor neural network
successfully predicted G4MP2-level molecular properties from DFT-level structures.
[1] Hohenberg, P. and Walter K., Inhomogeneous electron gas. Physical review, 136.3B: B864,
1964.
[2] Curtiss, L. A., Paul C. R., Krishnan R., Gaussian-4 theory. The Journal of chemical physics,
126.8: 084108, 2007.
[3] Ramakrishnan, R., Dral, P. O., Rupp, M., Lilienfeld, O. A., Big data meets quantum chemistry
approximations: The Δ-machine learning approach. Journal of chemical theory and computation
11.5: 2087-2096, 2015.
[4] Ramakrishnan, R., Dral, P. O., Rupp, M., Lilienfeld, O. A., Quantum chemistry structures andproperties of 134 kilo molecules, Scientific data 1: 140022, 2014.

P7-01

Data set selection in deep learning based
CYP3A4 binding mode prediction
Atsuko Sato 1
sato.a.al@m.titech.ac.jp

1

2

3

4

Naoki Tanimura 2
naoki.tanimura@mizuho-ir.co.jp

Teruki Honma 1,3,4

Akihiko Konagaya 1

honma.teruki@riken.jp

kona@c.titech.ac.jp

School of Computing, Department of Computer Science, Tokyo Institute of Technology; 4259
Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan
Science Solutions Division, Mizuho Information & Research Institute, Inc.; 2 -3, KandaNishikicho, Chiyoda-ku, Tokyo, 101-8443, Japan
Center for Biosystems Dynamics Research, RIKEN; 1 -7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa, 230-0045, Japan
Medical Sciences Innovation Hub Progra m, RIKEN; 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa, 230-0045, Japan.

Keywords: Computational drug design, Convolutional neural network, CYP3A4, binding modeprediction, data selection
For rational drug design, it is essential to predict the binding mode of protein–ligand complexes.
Although various machine learning-based models have been reported that use convolutional neuralnetworks to predict binding modes from three-dimensional structures, there are few detailed reports
on how best to construct and use datasets. Here, we examined how different datasets affected theprediction of the binding mode of CYP3A4 by a three-dimensional neural network when the numberof crystal structures for the target protein was limited. We used four different training datasets: onelarge, general dataset containing various protein complexes and three smaller, more specific datasets
containing complexes with CYP3A4-like pockets, complexes with CYP3A4-binding ligands, andcomplexes with CYP protein family members. We then trained models with different combinationsof datasets with or without subsequent fine-tuning and evaluated the binding mode prediction
performance of each model. The ROC AUC model with respect to area under the receiver operating
characteristic curve was obtained by training with a combination of the general protein and CYP
family datasets. However, the ROC AUC – recall balanced model was obtained by training withthis combination of datasets followed by fine-tuning with the CYP3A4-binding ligands dataset. Our
results suggest that datasets that balance protein functionality and data size are important for
optimizing binding mode prediction performance. In addition, datasets with large median binding
pocket sizes may be important for the binding mode p rediction specifically of CYP3A4.
[1] Sato, A.; Tanimura, N.; Honma, T. and Konagaya, A. A role of data selection in deep learning
based CYP3A4 binding mode prediction. (submitted)

P7-02An Approach to Investigate Disease Mechanisms
by FAERS Data Mining
Yoshito Ohya 1
yoshito.ohya.yf@kyowakirin.com

Yoko Ono 1
yoko.ono.jg@kyowakirin.com

Katsuya Kobayashi 2
katsuya.kobayashi.xe@kyowakirin.com

Kensuke Kojima 1
kensuke.kojima.th@kyowakirin.com
1

2

Takashi Yoneya 1takashi.Yoneya.sk@kyowakirin.com

Biometrics Dept., R&D Division, Kyowa Kirin Co., Ltd., Otemachi Financial City Grand Cube ,1-9-2 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan
Immunology & Allergy lab., R&D Division, Kyo wa Kirin Co., Ltd., 3-6-6 Asahi-machi,
Machida-shi, Tokyo 194-8533, Japan

Keywords: Real World Data, RWD, FAERS, Drug discovery, Reporting Odds Ratio
Recently, decline of success rate and cost escalation become notable in drug discovery process [1],and more efficient approach is required. In this decade, the significant progress of technology allows
us to collect and analyze the clinical big data from real world not only for epidemiological study but
for drug discovery and development [2]. FDA Adverse Event Reporting System (FAERS) is one of thelargest real world data which consists of adverse drug events collected all over the world [3], and thenumber of researches which succeeded to show the possibility of drug r epositioning has been increased[4].
Here, we explored the FAERS database to identify effective drugs to lower the occurrence of sys temic
lupus erythematosus (SLE). We defined the set of adverse events similar to the clinical findings of SLE
by some PT codes of Medical Dictionary for Regulatory Activities (MedDRA) [5], followed by
calculation of Reporting Odds Ratio (ROR) with its 95% confidence interval for signal detection.
According to this ROR, we selected the drugs that could suppress the SLE-like adverse events andevaluated the effects of a confounding factor. After that, we listed common biological functions ofthese drugs and made assumptions about their molecular mechanisms.
We would like to discuss this scheme, e.g., selection of effective drugs, dealing with confounding
factors, identification of the common factors of the selected drugs and the construction of hypotheses.

[1] https://www.mhlw.go.jp/content/10801000/000398096.pdf[2] Alemayehu, D and Berger, M. L., Big Data: transforming drug development and health policy
decision making, Health Services and Outcomes Research Methodolog y, Health Services andOutcomes Research Methodology, 16:92-102, 2016[3]https://www.fda.gov/drugs/drug-approvals-and-databases/fda-adverse-event-reporting-system-faers[4] Nagashima,T., Shirakawa, H., Nakagawa, T., Kaneko, S., Prevention of antipsychotic-induced
hyperglycaemia by vitamin D: a data mining prediction followed by experimental exploration of
the molecular mechanism, Scientific Reports, 6: 26375, 2016[5] https://www.meddra.org/

P7-03

Predicting drug indications and therapeutic target modules
based on disease similarity by interpretable machinelearning models
Ryusuke Sawada 1
sawad330@bio.kyutech.ac.jp

Yasuo Tabei 2
yasuo.tabei@riken.jp

Yoshihiro Yamanishi 1
yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680 -4 Kawazu,
Iizuka, Fukuoka 820-8502, Japan.
2
RIKEN Center for Advanced Intelligence Project, 1 -4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027,
Japan.

Keywords: drug indication, drug repositioning, drug targets, disease similarity, protein modules,
machine learning, interpretability
The identification of drug indications and understanding of the mode-of-action are important
issues in drug discovery. Machine learning plays a key role in computational prediction, but
predictive models of most machine learning methods are black-box and unexplainable. In thisstudy, we propose a novel machine learning method to predict new indications of drugs fromchemical-protein interactome data and disease similarity. Disease-specific predictive models areconstructed based on their molecular features such as diagnostic markers, disease-related
genes/pathways, and environmental factors. We also estimate informative sets of proteins andbiological pathways that contribute to the drug indication prediction by exploring disease-protein
modules using a module search algorithm. In the results, we show the superior performance of the
proposed method over the other methods in terms of accuracy and interpretability. Herein, we
comprehensively predicted drug-disease association networks for 1,124 drugs and 365 humandiseases, and validated some of these predictions using independent resources. The proposed
method is expected to facilitate drug discovery and repositioning.

P7-04
A Method for Systematic Analog Searching Using theMega SAR Matrix Database
Atsushi Yoshimori 1
yoshimori@itmol.com

Toru Tanoue 2
tanoue.toru@infogram.co.jp

Yuichi Horita 2
horita.yuichi@infogram.co.jp

Jürgen Bajorath 3
bajorath@bit.uni-bonn.de

1

Institute for Theoretical Medicine, Inc., 26 -1 Muraoka-Higashi 2-chome, Fujisawa, Kanagawa
251-0012, Japan.
2
Infogram, Inc., 2-17-19 Hakataekimae, Hakata-ku, Fukuoka 812-0011, Japan3
Department of Life Science Informatics, B -IT, LIMES Program Unit Chemical Biology andMedicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c,
D-53115 Bonn, Germany

Keywords: Structure-activity relationships, Matched molecular pair, Matching molecular series,
SAR Matrix (SARM), SARM-based analog search
Analog searching is an important part of hit expansion, hit-to-lead, and lead optimization efforts
during the early stage of drug development. A new computational methodology is introduced tosearch for real and virtual structural analogs of active compounds in large chemical databases [1].
The approach is based on the SAR Matrix (SARM) data struc ture that was originally designed toelucidate SAR information associated with groups of structurally related active compounds [2].

The SARM-based analog search method extends the capacity of current substructure-based
methods by (i) considering real and virtual analogs of query compounds , (ii) permitting not only
R-group replacements but also well-defined chemical modifications in core structures , (iii)
automatically extracting all possible analogs form a large chemical database, and (iv) using an
index tables to perform a computationally efficient structure search.
In this study, we introduce several examples of the SARM-based analog search to get usefulknowledge from analog compounds linked to ChEMBL database.
[1] Yoshimori, A., Horita, Y., Tanoue, T., Bajorath, J. Method for Systematic Analog Search Using
the Mega SAR Matrix Database, submitted for publication.
[2] Wassermann, A.M., Haebel, P., Weskamp, N., and Bajorath, J., SAR matrices: automatedextraction of information-rich SAR tables from large compound data sets, Journal of Chemical
Information and Modeling, 52:1769-1776, 2012.

P7-05The Multiple Representation of Protein Sequence Motifs
Using Sequence Binary Decision Diagrams
Hiroaki Kato 1
kato @ hiroshima-cmt.ac.jp

Kohei Yamato 2
k143377 @ edu.tut.ac.jp

1

Department of Distribution and Information Engineering, National Institute of Technology,
Hiroshima College, 4272-1 Higashino, OsakiKamijima-cho, Hiroshima 725-0231 Japan2
Department of Computer Science and Engineering, Toyoh ashi University of Technology,
1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580 Japan

Keywords: sequence binary decision diagram, Aho-Corasick algorithm, sequence motif,
matrix metalloproteases, olfactory receptors
A protein is a biological macromolecule consisting of one or more chains of amino acid r esidues.
It is well known that there are characteristic patterns called sequence motifs among proteins whichhave a common function. Since sequence motifs closely related to its structure and function, they
have been applied to the functional analysis of proteins. Usually, it has employed sequence motif
representations (motif models) such as regular expressions and weight matrices. In the present
work, the authors have proposed a novel representation for a set of sequence motifs using
SeqBDDs (sequence binary decision diagrams).
A SeqBDD is a directed acyclic graph, which represents a set of character strings, and is capableof using various set operations [1]. A SeqBDD contain comprehensive patterns associated with aparticular function, and also several types of sequence motifs, i.e., it can represent a sequencemotif group. We have also developed a string search algorithm and a homology search algorithm
using SeqBDDs. The information of a sequence motif group is compressed and stored in a
SeqBDD. The string search algorithm is based on the Aho -Corasick algorithm [2], and thehomology search algorism based on local and global s equence alignments using dynamic
programming.
At first, the sequence motifs on matrix metalloproteases (MMPs) were searched from all aminoacid sequences in UniProtKB/Swiss-Prot using SeqBDDs and PROSITE patterns. The results
were compared with their precision, recall and f-measure. As a result, the proposed motif model
can properly search the sequences that were in false -negative when using regular expressions.
Then, we verified the time efficiency of the homology search algorithm using artificial data. The
homology search algorithm is faster than existing method. Finally, similar sequences wereobtained with the homology search on sequence motif group on human olfactory receptors (ORs),and used to update the motif model on human ORs.

References:[1] E. Loekito, J. Bailey and J. Pei, A binary decision diagram based approach for mining frequent
subsequences, Knowledge and Information Systems, 24, 235-268 (2010).
[2] A. V. Aho and M. J. Corasick, Efficient string matching: an aid to bibliographic search,
Communications of the ACM, 18, 333-340 (1975).

P7-06Development of preventive drugs against
oxaliplatin-induced peripheral neuropathy usinglarge-scale medical database
Takahiro Niimura 1

Yoshito Zamami 1, 2

c201756030@tokushima-u.ac.jp

Yutaro Naitoh 1

zamami@tokushima-u.ac.jp

c401831018@tokushima-u.ac.jp

Takehiro Kawashiri 3

Mitsuhiro Goda 2Kenshi Takechi 4

tkawa@med.kyushu-u.ac.jp

mgoda@tokushima-u.ac.jp

takechi.kenshi@tokushima-u.ac.jp

Masayuki Chuma 4

Yuya Horinouchi 5

chuma.masayuki@tokushima-u.ac.jp

Yuki Izawa-Ishizawa 6
ishizawa.yuki@tokushima-u.ac.jp

1

2

3

4

5

6

horinouchi@tokushima-u.ac.jp

Yasumasa Ikeda 5
yasuike@tokushima-u.ac.jp

Keisuke Ishizawa 1, 2
ishizawa@tokushima-u.ac.jp

Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate
School of Biomedical Sciences, 2 -50-1 Kuramoto-cho, Tokushima 770-8503, Japan.
Department of Pharmacy, Tokushima University Hospital, 2 -50-1 Kuramoto, Tokushima,
770-8503, Japan.
Department of Clinical Pharmacy and Pharmaceutical Care, Graduate School of Pharmaceutical
Sciences, Kyushu University, 3–1–1 Maidashi, Higashi-ku, Fukuoka 812–8582, Japan.
Clinical Trial Center for Developmental Therapeutics, Tokushima University Hospital , 2-50-1
Kuramoto-cho, Tokushima, 770-8503, Japan.
Department of Pharmacology, Institute of Biomedical Sciences, Graduate School, Tokushima
University, Tokushima, 770-8503, Japan.
AWA Support Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503,
Japan.

Keywords: oxaliplatin, peripheral neuropathy, adverse event, medical database
Oxaliplatin, a platinum-based anticancer drug, causes oxaliplatin-induced peripheral neuropathy(OIPN). Because OIPN not only reduces patients’ quality of life (QOL) but also leads to dose
reduction of oxaliplatin, the development of preventive agents against OIPN is desired. In thisstudy, we searched for preventive drugs against OIPN using large -scale medical database, andexperimentally validated the findings with neuron-like cells and OIPN rat models.
First, we searched for approved drugs that cancel the gene expression change caused byoxaliplatin, using the drug discovery tool LINCS, and found 23 approved drugs that met therequirements. Candidate drugs were then evaluated for their mitigating effects on OIPN, using the
FDA Adverse Event Reporting System (FAERS) database. Based on the FAERS analysis, Drug X
was found to significantly reduce the risk of patients developing OIPN. Using PC12 cells, weobserved significant improvement in oxaliplatin-associated axonal damage with Drug X treatment.
In addition, in vivo experiments showed that Drug X significantly reduced the expression ofoxaliplatin-induced neuropathy in OIPN rat models.
Present study suggested that Drug X could be used as a preventive agent for OIPN.

P7-07

Kampo Drug Repositioning and Compound Mixture
Analyses using Multi-task Graph Convolutional Neural
Networks
Akihiro Douke 1
akihiro.douke605@mail.kyutech.jp

Yukie Sakai 1
sakai541@bio.kyutech.ac.jp

1

2

Ryusuke Sawada 1

Michio Iwata 1

sawad330@bio.kyutech.ac.jp

iwata121@bio.kyutech.ac.jp

Makoto Kadowaki 2
makotok@inm.u-toyama.ac.jp

Yoshihiro Yamanishi 1
yamani@bio.kyutec h.ac.jp

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Division of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of
Toyama, Toyama 930-0194, Japan.

Keywords: Kampo medicines, mode-of-action, target prediction,
convolutional neural network, multi-task learning, deep learning

repositioning,graph

Kampo medicine is useful to treat multifactorial and chronic diseases. Today in Japan, more than
80% of medical doctors prescribe Kampo medicines in addition to western medicine. The
mechanisms of Kampo medicines are different from those of western medicines. The efficacies ofKampo medicines stem from multiple compound–multiple target interactions. Therefore, it isindispensable to establish fundamental technologies to comprehensively analyze the underlying
mechanisms of every pharmacological action of multi-component Kampo medicines in the humanbody as a complex system. In this study, we propose a novel computational method to elucidate themode-of-action of Kampo medicines and to predict new indications of Kampo medicines, which
we call Kampo drug repositioning. This is an extension of our previous work [1]. In the algorithm,
we estimate potential target proteins of each of constituent compound by multi-task graphconvolutional neural networks (GCNNs), and predict its new indications by a mixture analysis ofindications at the compound level and those at the crude drug level taking into account the ratio ofcrude drugs in each of Kampo medicines. We also estimate target pathways of Kampo medicines
by an enrichment analysis of target proteins of constituent compounds. We construct predictivemodels based on 1,588,885 compound-protein interactions involving 1,111,415 compounds and4,393 proteins, and predict new indications of 194 Kampo medicines involving 99 crude drugs and2791 constituent compounds. The proposed methods are expected to be useful for health scienceand for clinical applications.
[1] Sawada, R., Iwata, M., Umezaki, M., Usui, Y., Kobayashi, T., Kubono, T., Hayashi, S.,
Kadowaki, M. & Yamanishi, Y., KampoDB, database of predicted targets and functionalannotations of natural medicines, Scientific Reports, 8:11216, 2018.

P7-08Prediction of Compound-Protein Interactions andVisualization Based on Graph Convolutional Networks
Marie Ikeguchi 1
ikeguchi.marie.44n@st.kyoto-u.ac.jp

Shoichi Ishida 2

Ryosuke Kojima 1

ishida.shoichi.57a@st.kyoto-u.ac.jp

kojima.ryosuke.8e@kyoto-u.ac.jp

Hiroaki Iwata 1
iwata.hiroaki.3r@kyoto-u.ac.jp
1

2

3

4

Yasushi Okuno 1,3,4
okuno.yasushi.4c@kyoto-u.ac.jp

Graduate School of Medicine, Kyoto University, Shogoin-Kawaharacho, Sakyo-ku, Kyoto
606-8507, Japan
Graduate School of Pharmaceutical Science, Kyoto University, Yoshida, Sakyo-ku, Kyoto
606-8501, Japan
Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology andInnovation Hub, Tsurumi-ku, Kanagawa 230-0045, JapanFoundation for Biomedical Research and Inn ovation at Kobe, Center for Cluster Development
and Coordination, Chuo-ku, Kobe, Hyogo 650-0047, Japan

Keywords: Compound-protein interactions,
Visualization, Integrated gradientsGraphconvolutional

networks,

Multimodal,

Predicting compound-protein interactions (CPIs) has played an important role in drug discovery[1], CPIs prediction methods using deep learning have achi eved excellent performances [2,3].
However, effective methods for visualizing the interactions between each atom of a compound andeach residue of a protein sequence have not been established. In this study, we developed a new
prediction method together with the visualization of the predicted CPIs. Our method uses amultimodal neural network that combining graph convolutional networks [4] for chemical structure
and convolutional neural networks for a protein sequence. Also, our method can visualize thecontribution of the prediction results by using integrated gradients [5]. To evaluate the prediction
performance of our methods, we applied this method to CPI datasets collected from the ChEMBL
database. As a result, we indicated that a prediction performance of our model is higher than 0.9
AUC score. Furthermore, using our visualization method, we compared the visualized interactions
with crystal structures. We want to give details of the results in this presentation.
[1] Michael, J. K., Vincent, S., Predicting new molecular targets for known drugs, Nature,
462:175-181, 2009.
[2] Tsubaki, M., Tomii, K., Sese, J., Compound -Protein interaction prediction with end-to-end
learning of neural networks for graphs and sequences, Bioinformatics, 35:309-318, 2018.
[3] Hamanaka, M., Taneishi, K., Iwata, H., Ye, J., Pei, J., Hou, J., and Okuno, Y., CGBVS-DNN:
Prediction of Compound‐protein Interactions Based on Deep Learning, Molecular Informatics,
2016.
[4] Kipf, T. N., and Welling, M. S., Semi -Supervised Classification with Graph Convolutional
Networks, International Conference on Learning Representations (ICLR), 2017.
[5] Sundararajan, M., Taly, A., and Yan, Q., Axiomatic Attribution for Deep Networks,
International Conference on Machine Learning (ICML) , 2017.

P7-09Development of data curation and integration protocol for chemical
library in early drug discovery
Yugo Shimizu
shimizu-yg@pha.keio.ac.jp

Masanori Osawa
osawa-ms@pha.keio.ac.jp

Kazuyoshi Ikeda
ikeda-kz@pha.keio.ac.jp
Keio University, Faculty of Pharmacy, Division of Physics for Life Functions, 1-5-30
Shibakoen, Minato-ku, Tokyo 105-8512, Japan
Keywords: Chemical data integration, Curation, Library design
It is known that there are a huge number of chemical structures that can be synthesized in
drug development. Therefore, it is important to develop data science technology in chemical
information, especially chemical data curation and integration protocols related to existing
pharmaceutical compounds.
We developed a new library-design method of data curation and integration for high-quality
compound-library using open drug discovery data. We aim to design a core-library consistingof drug-like compounds for satisfying diversity in physical properties and chemical
characteristics. Furthermore, we considered the bioactivity diversity for known and unknown
drug targets. We created ligand-based target models by using several machine learning(ML) methods. We extracted compounds and analysis their bioactivity information frompublic databases such as ChEMBL and PubChem to train and test the models. By using our
method, we mapped and annotated large-scale chemical data extracted from commercial
libraries. In this presentation, we will show how to develop our chemical data science
technologies for analyzing known and unknown drug developing compounds, and also
discuss how to apply it for a core library in an early stage of drug discovery.
This work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P7-10Clustering therapeutic drugs based on similarities ofindications and side effects reported in public database
Ryutaro Shinkawa 1 ,Yuya Suzumra 1 ,Taichi Yamada 1 ,Takafumi Moriguchi 1 ,319057@m.mie-u.ac.jp

Yuhei Nishimura 1
yuhei@doc.medic.mie-u.ac.jp

1

Department of Integrative Pharmacology, Mie University School of Medicine,
2-174 Edobashi, Tsu, Mie 514-8507, Japan

Keywords: Real world data, Public database, Cosine similarity, Multidimensional clustering
Therapeutic drugs have been classified based on pharmacodynamics and disease indications.
However, it has gradually been revealed that profiling of side effects can be used to classify
therapeutic drugs and to find novel disease indications of drugs. In this study, we generated
multidimensional vectors for each therapeutic drug based on the cosine similarity of indications orside effects in the US FDA Adverse Event Reporting System (FAERS) published for 2018. Using
the spatial density, the multidimensional vectors were clustered into 54 and 52 groups based on theindications and side effects, respectively, reported in FAERS. By comparing the 54 and 52 groups,
we were able to identify 45 sets of therapeutic drugs that were common in the two clusterings.
Several sets were comprising of therapeutic drugs with different pharmacodynamics and different
disease indications. These findings suggest that clustering therapeutic drugs based on similarities
of indications and side effects reported in public database can be useful to find novel similarity
between therapeutic drugs and to reveal novel disease indications for these drugs.

P7-11Drug Discovery Raid Battle 2018: an open challenge todiscover PD-1/PD-L1 small-molecule inhibitors
Kazuki Z Yamamoto 1, 2

kayamamoto-tky@umin.net

Masahiro Mochizuki 3

f09l001a@alumni.niigata-u.ac.jp12
3

Hiroyuki Yamasaki 3

yamasaki.phone@gmail.com

Kentaro Rikimaru 3

kristall19@gmail.com

Isotope Science Center, University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032,
Japan
SHaLX Inc., 5-23-11 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Drug Discovery Raid Battle 2018 Contributive Committee, 5-23-11 Hongo, Bunkyo-ku, Tokyo
113-0033, Japan

Keywords: Social drug discovery, Virtual screening, Screening assay
PD-1/PD-L1 immune checkpoint inhibitors, which have recently been established to be effectivein the treatment of cancer, have been put to practical use as antibody drugs. However, they havedisadvantages such as cost and inability to be administered orally. Therefore, if substitution of
those biologics by small molecules is possible, the resulting practical merits are great [1]. Here, inorder to discover small molecules that inhibit PD-1/PD-L1 immune checkpoint signaling, we helda drug discovery event in which anyone could participate. We provided a digital library ofscreening compounds [2] and the participants individually screened the candidate compounds andsubmitted the candidates to us. And we assay them and confirm the inhibitory activity [3], whose
results are supposed to be available by the fall this year. If promising hit compounds are obtained,
we aim for development of a medicine with the compound proposer.
[1] Shaabani, S., Huizinga, H. P., Butera, R., Kouchi, A., Guzik, K., Magiera-Mularz, K., ... &Dömling, A. A patent review on PD-1/PD-L1 antagonists: small molecules, peptides, andmacrocycles (2015-2018). Expert opinion on therapeutic patents, 28(9), 665-678, 2018.
[2] Enamine Screening Collection
https://enamine.net/hit-finding/compound-collections/screening-collection[3] Cisbio, Human PD1/PD-L1 biochemical binding assay
https://www.cisbio.jp/human-pd1-pd-l1-biochemical-binding-assay-40578

P7-12

Automatic Reading of Tables and Figuresin Scientific Papers
Hiroyuki Shindo 1
shindo@is.naist.jp
1

Division of Information Science, Nara Institute of Science and Technology, 8916-5
Takayama-cho, Ikoma, Nara 630-0192, Japan

Keywords: Natural Language Processing, Machine Learning, Computer Vision

Automatic reading of tables and figures is a critical component for data mining and information
extraction from scientific papers since many experimental results in scientific research arepresented in the form of tables and figures. In computer science, previous work [1, 2, 3] has shownthat tables and figures can be successfully detected and parsed with the help of machine learningtechniques.
In this work, I propose an automated end-to-end pipeline for information extraction fromscientific papers. Given a PDF of scientific papers, the body texts, tables and figures are firstly
detected with deep neural networks, then the extracted objects are parsed and structured withnatural language processing and image processing. For figure parsing, this work focuses on data
extraction from line charts and scatter plots. Experimental results show that my proposed approachaccurately extracts data from tables and figures.
[1] Mathieu Cliche, David Rosenberg, Dhruv Madeka and Connie Yee, Scatteract: Automatedextraction of data from scatter plots, Proc. of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases , 2017.
[2] Noah Siegel, Nicholas Lourie, Russell Power and Waleed Ammar, Extracting Scientific Figureswith Distantly Supervised Neural Networks, Proc. of Joint Conference on Digital Library, 2018.
[3] Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zh ou, Zhoujun Li, TableBank: Table
Benchmark for Image-based Table Detection and Recognition, arXiv, 2019.

P8-01

Functionalization of liposomes using artificially evolved
peptides against lipid membranes
Takuya Terai 1

Shota Kobayashi 1

Yuki Yoshikawa 1

tterai@mail.saitama-u.ac.jp

Yasuhito Utsugi 1

Naoto Nemoto 1
nemoto@mail.saitama-u.ac.jp

1

Graduate School of Science and Engineering, Saitama University, 255 Shimo-okubo
Sakura-ku, Saitama, 338-8570, Japan

Keywords: Molecular Robotics, Liposome, In vitro selection, Peptide
A molecular robot is a bottom-up artificial molecular system comprised of sensors, actuators, andcomputing machineries, using organic (bio)molecules such as (modified) nucleic acids, proteins,and lipids. They are potentially useful in a number of applications such as drug delivery,
information storage, in vivo diagnosis, and so on [1]. In most cases, the outer layer, or scaffold, ofmolecular robots is giant unilamellar vesicles (GUVs), or liposomes. Hence, to promote molecularrobotics, it is important to develop a facile methodology to stably attach molecules of interest on
the surface of lipid membranes.
In the last two decades, our group has established a methodology of in vitro evolution ofpolypeptides using a puromycin-modified DNA as a linker to covalently attach a polypeptide to its
corresponding cDNA (= cDNA display) [2][3]. In this study, we used cDNA display to discovernovel peptide sequences that had affinity to lipid membranes but did not disrupt liposome
structures. We prepared an arginine-rich peptide library over 10 12 members, and performed sevenrounds of in vitro selection against DOPC GUVs. Liposome fractions were collected and attachedcDNA were amplified. After cloning of selected cDNA display molecules, a peptide called LB-1
was identified as a novel peptide that bound to lipid membranes [4]. LB-1 consisted of amphiphilic
region and cationic region, and both parts played important roles for membrane anchoring. Notably,fusion of nucleic acids and proteins (including an antibody) at the C-terminus of LB-1 did nothamper its specific localization on membrane. It is expected that the peptide will be a usefultoolkit to attach sensors and targeting molecules to liposome-based molecular robots.
Furthermore, when we investigated several LB-1 derivatives, it was found that one of the peptides
showed permeability to liposome membrane. Although further study is necessary, it seems that thecomposition of lipid membrane is important for this phenomenon. More details will be presented at
the conference venue.
[1] Murata, S., Konagaya, A., Kobayashi, S., Saito, H., Hagiya, M., Molecular Robotics: A New
Paradigm for Artifacts, New Generation Computing, 31:27-45, 2013.
[2] Yamaguchi, J., Naimuddin, M., Biyani, M., Sasaki, T., Machida, M., Kubo, T., Funatsu, T.,
Husimi, Y., Nemoto, N., cDNA display: a novel screening method for functional disulfide-rich
peptides by solid-phase synthesis and stabilization of mRNA–protein fusions, Nucleic Acids
Research, 37:e108, 2009.
[3] Terai, T., Anzai, H., Nemoto, N., Selection of Peptides that Associate with Dye-ConjugatedSolid Surfaces in a pH-Dependent Manner Using cDNA Display, ACS Omega, 4:7378-7384,
2019.
[4] Kobayashi, S., Terai, T., Yoshikawa, Y., Ohkawa, R., Ebihara, M., Hayashi, M., Takiguchi, K.,
Nemoto, N., In vitro selection of random peptides against artificial lipid bilayers: a potential
tool to immobilize molecules on membranes, Chemical Communications, 53:3458-3461, 2017.

P8-02

Experimental Investigation of DNA Generation Circuits
toward Molecular Robot Control
Ken Komiya 1

Chizuru Noda 1

komiya@c.titech.ac.jp

Teruya Enomoto 1

Masayuki Yamamura 1
my@c.titech.ac.jp

1

School of Computing, Tokyo Institute of Technology, 4259, Nagatsuta -cho, Midori-ku,
Yokohama, Kanagawa, 226-8503, Japan

Keywords: Single-stranded DNA, DNA amplification, Physiological temperature, Molecular robot
We had developed a modular reaction cascade that generates pre-designed, single-stranded DNA(ssDNA) molecules as signal transducers in a programmable way [1]. With the use of tandemlyaligned template sequences of signal DNA [2], we later applied our reaction cascade to exponential
DNA amplification at a physiological temperature [3]. This isothermal ssDNA amplification
reaction, termed low-temperature amplification (L-TEAM) reaction, allows a high amplification
rate and concurrent hybridization of the amplified ssDNA under low -temperature conditions during
amplification. Therefore, the L-TEAM reaction seems promising for versatile applications
including automated nucleic acid test and a cell-like molecular robot that requires generation of alarge amount of ssDNA signals to control a swarm of DNA-directed nanodevices in response to atiny amount of nucleic acid stimuli [4, 5]. In the present study, we experimentally investigated theperformance of DNA generation cascades as elementary molecular circuits. Leak-free DNA
amplification and temporal modulation of DNA generation would be discussed from the viewpointof molecular robots, where our reaction cascade is integrated as the controller.
[1] Komiya, K. and Yamamura, M., Cascading DNA generation reaction for sequential control of
DNA nanomachines at a physiological temperature, New Generation Computing, 33:213-229,
2015.
[2] Van Ness, J., Van Ness, L. K. and Galas, D. J., Isothermal reactions for the amplification ofoligonucleotides, Proc. Natl. Acad. Sci. USA, 100(8):4504-4509, 2003.
[3] Komiya, K., Komori, M., Noda, C., Kobayashi, S., Yoshimura, T. and Yamamura, M.,
Leak-free million-fold DNA amplification with locked nucleic acid and targeted hybridizationin one pot, Organic and Biomolecular Chemistry, 17(23):5708-5713, 2019.
[4] Komori, M., Komiya, K., Shirakawa, T., Morikawa, T. J. and Yoshimura, T., Measurement of
microRNA with isothermal DNA amplification on fully automated immunoassay analyzers ,Analytical and Bioanalytical Chemistry, 411(17):3789-3800, 2019.
[5] Sato, Y., Komiya, K., Kawamata, I, Murata, S., Nomura, S. M., Isothermal amplification of
specific DNA molecules inside giant unilamellar vesicles , Chemical Communications, in press,
2019, DOI: 10.1039/c9cc03277k

P8-03A database to store design information of DNA
nanostructure
Ibuki Kawamata 1
kawamata@molbot.mech.tohoku.ac.jp

Sho Aradachi 1
aradachi@molbot.mech.tohoku.ac.jp

Satoshi Murata 1
murata@molbot.mech.tohoku.ac.jp

1

Department of Robotics, School of Engineering, Tohoku University, Sendai, Miyagi, Japan

Keywords: Molecular Robotics, DNA origami, DNA nanotechnology, database, caDNAno fileIn the field of structural DNA nanotechnology, DNA duplex is u tilized as build blocks to assemble
nano-scale structures with a complex geometry [1]. In the past few decades, a number of DNA
nanostructures have been demonstrated with a potential application in molecular -scale robotics,
smart drug delivery systems, and so on [2]. To rationally design such DNA nanostruc tures, several
softwares have been developed such as caDNAno [3]. Although those softwares can be
downloaded, raw design information (e.g. caDNAno file) is not usually provided together with apaper publication. The situation causes an underlying problem t hat one has to pay a great effort toreuse/recycle the DNA nanostructures of the others. Getting rid of the obstacle enables us toengineer novel DNA systems on top of well-qualified DNA nanostructures.
Here, we propose a web-based database that stores the design information of DNA nanostructures,
especially targeting the caDNAno format (Fig. 1). In the currently developed trial version, theinput to the database is a caDNAno file, which is then converted to several useful formats such as
PDB file, rendered images, DNA sequence table etc. It is also possible to store additional
information such as digital object identifier (DOI) to identify the publication about the structure.
Moreover, we prepare some standard objects such as rectangles and cuboids, which are available
from the database. From the web-based interface, users can upload a caDNAno file and download
the all of the data for the purpose of lab experiment and/or computer simulations. At thepresentation, we would like to discuss further requirement s and get some feedbacks of the databaseto improve the user experience of the service. In the future, the database may serve as a catalog of
DNA objects, which will accelerate the engineering process of complex DNA nanostructures.

Figure 1: The schematic figure of the DNA nanostructure database. When the caDNAno file isuploaded, it is converted to several file formats, which will be listed in the database.
[1] N. C. Seeman, Molecular Biotechnology. 2007, 37, 246 —257.
[2] N. C. Seeman and H, F. Sleiman, Nature Reviews Materials. 2017, 3, 17068.
[3] S. M. Douglas, A. H. Marblestone, S. Teerapittayanon, A. Vazquez, G. M. Church, and W. M.
Shih, Nucleic Acids Research. 2009, 37, 5001–5006.

P8-04

Haptic Interactive Virtual Reality Simulation on
Biomolecules
Arif Pramudwiatmoko 1,2
Pramudwiatmoko.a.aa@m.titech.ac.jp

Satoru Tsutoh 3
tsutoh.satoru@fujixerox.co.jp

Gregory Gutmann 1

Yutaka Ueno 4

gutmann.g.aa@m.titech.ac.jp

yutaka.ueno@aist.go.jp

Akihiko Konagaya 1
kona@c.titech.ac.jp

1

2

3
4

Tokyo Institute of Technology, J3-18, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa
226-8503, Japan
Universitas Teknologi Yogyakarta, Jl Siliwangi, Jombor, Sleman, D.I. Yogyakarta 55285,
Indonesia
Fuji Xerox Co., Ltd., 430 Sakai, Nakai-machi, Ashigarakami-gun, Kanagawa 259-0157, Japan
National Institute of Advanced Industrial Science and Technology, 2-4-7, Aomi, Koto-ku,
Tokyo 135-0064, Japan

Keywords: Haptic rendering, hand tracking, molecular modelling
In order to provide an intuitive way to observe molecular objects, virtual reality (VR) system forbiomolecules have been implemented with haptic technology and hand tracking module. An
immersive VR simulation requires a specific framerate to perform and so does the interaction ofhands with the biomolecules. Anchors with spring forces are implemented to propagate handinteraction force to biomolecule objects.
[1] Pramudwiatmoko, A., Tsutoh, S., Gutmann, G., Ueno, Y., Konagaya, A., Haptic rendering
applied to hand tracking 3D user interface for a molecular modeling environment, 24th
International Symposium on Artificial Life and Robotics, International Society of Artificial Lifeand Robotics, 109–114, 2019.
[2] Gutmann, G., Inoue, D., Kakugo, A., Konagaya, A. , Parallel Interaction Detection
Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding
Simulation System Accelerated by GPGPU , New Gener Comput, 35:157–180, 2017.
[3] Löwe, J., Li, H., Downing, K.H., Nogales, E., Refined structure of αβ-tubulin at 3.5 Å
resolution, J Mol Biol, 313:1045–1057, 2001.

P8-05

Molecular dynamics study on breakage of photoresponsive
DNA
Ryuzo Azuma
azuma.r.ac@m.titech.ac.jp

Greg Gutmann

gutmann.g.aa@m.titech.ac.jp

Akihiko Konagaya
kona@c.titech.ac.jp

Tokyo Institute of Technology, J3-25, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Keywords: Molecular robotics, azobenzene attached photoresponsive DNA, molecular dynamics
Mechanical stretching of DNA has been one of hot topics in nanotechnology and nano-science research
fields for nearly 30 decades. Whereas conventional studies have mostly focused on characterization of
mechanisms underlying force-stretching curves obtained from experiments, engineering novel functional
devices by utilizing these properties has also become popular since the development of technologies such as
DNA linkers, switches, etc. In particular, photoresponsive DNA (p-DNA) has been incorporated into
tubulines, the protein dimer subunit of microtubles, as conjugate and lead to successful observation ofswitching on and off the collective motion of microbutules (referred to as ‘swarms’) in response to visible
and UV light irradiation [1].
Here we present our molecular dynamics studies on breakage of p-DNA under a pulling force applied to the
5’ ends. The p-DNA consists of an 8 repeat of the same sequence, CAA, each of which (except for the lastone) is followed by one azobenzene. Azobenzene is known to take two different forms depending on wave
lengths of irradiation: under visible or UV light they may take trans- or cis-forms, respectively [2]. Our
strategy is to use atomic structures of p-DNA prepared with those 2 different forms of azobenzenes and carryout MD simulations so that we can distinctively analyze the dynamics of breakage. In addition, as a control,
we carried out same MD analysis on native duplex with a sequence (CAAT) 11CAA. The pulling force is due
to ideal elastic strings attached on both ends of the p-DNA, and terminals of those strings are moving apartat a constant speed. In our measurements, the force constants of the elastic string are ranged within 0.14414.4 [10-2pN/Å], while the pulling speeds are ranged within 5-100 [10 -3Å/ps]. The system initially undergoes
an NVT MD run up to 36ps for equilibration, then goes through a production NPT run under a pulling force
for 8-120ns. The simulation results exhibit common behavior wherein breakage involves structuraltransitions. The latter are shown to proceed as follows: the B→ S structural change continues until thepulling force reaches the maximum (as found experimentally [3]), which is followed by gradual sliding andrapid decrease in the pulling force, and then separation of the strands involving rapid shrinkage takes place
after breakage of all the h-bonds. The insertion of azobenzenes is shown to decrease the time to break off,
whereas this effect is significantly lower for the trans-ones. We further analyzed these results morequantitatively and obtained dependence of force-extention curves on the pulling force constants and speeds.
Those results will be discussed in detail in our presentation.
The authors thank Y Ueno (AIST), A Kakugo (Hokkaido Univ.), and A Kuzuya (Kansai Univ.) for useful
discussions. Financial supports: Strategic Advancement of Multi-Purpose Ultra-Human Robot and Artificial
Intelligence Technologies (NEDO, P15009), MEXT/JSPS KAKENHI (JP17H00769), and Platform Projectfor Supporting Drug Discovery and Life Science Research (BINDS-AMED, JP18am0101112).
[1] Keya, J.J., et al., DNA-assisted swarm control in a biomolecular motor system, Nature Communications,453(9), 1-8, 2019.
[2] Asanuma H., Ito T., Komiyama M. Photoregulation of the formation and dissociation of a DNA duplex
by using the cis-trans isomerization of azobenzene. Angew. Chem. Int. Ed. Engl., 38, 2393–2395, 1999.
[3] Cluzel, P., et al., DNA: An Extensible Molecule, Science, 271(9), 792-794, 1996.

P8-06

Formulating R&D Guidelines for Molecular Robotics
Naoto Kawahara 1

Ryuma Shineha 2n-kawaha@med.kyushu-u.ac.jp

r_shineha@seijyo.ac.jp

Akihiko Konagaya 3
kona@dis.titech.ac.jp

1
2
3

Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
Seijyo University, Seijo 6-1-20, Setagaya-ku, Tokyo 157-8511, JapanTokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa
226-8503 Japan

Keywords: R&D, Molecular Robotics, Genetically Modified Organism, Cartagena Protocol on
Biosafety, Containment Measures, Biological Control, Physical Control, Chemical Management
Thus far, we have established the ethical principles of Molecular Robotics with our colleagues.
Based on the principles, we will progress to formulate practical R&D guidelines for proper use of
Molecular Robotics. For example, its composition and/or ingredient, physical and/or chemical
properties, and environmental influence might be important factors in the R&D guidelines.
Otherwise, its containment measure involving a certain chemical management might be discussedwith reference to the Cartagena Protocol on Biosafety. What then are such the R&D criteria of
Molecular Robotics? - We discuss practical frameworks or viewpoints for proper use of Molecular
Robotics focusing on its technological characteristics concerning senses, motions and intelligence.
[1] U.S. Library of Congress, “R estrictions on Genetically Modified Organisms: Japan”, https://www.loc.gov/law/help/restrictions-on-gmos/japan.php, 2015[2] Ministry of Economy, Trade and Industry, Japanese Government, “Chemical Management”,
https://www.meti.go.jp/policy/chemical_management/english/index.htm l, 2014
[3] PMDA, “Quality Evaluation of Antibody Drug”, https://www.pmda.go.jp/files/000206143.pdf,
2012[4] UN, “The Globally Harmonized System of Classification and Labelling of Chemicals (Fourth Revised Edition)”, https://www.unece.org/fileadmin/DAM/trans/danger/publi/ghs/ghs_rev0
4/English/ST-SG-AC10-30-Rev4e.pdf, 2011.
[5] Comité Europeo de Normalización, “Laboratory Biorisk Management Standard (CWA 15793)”, ftp://ftp.cenorm.be/PUBLIC/CWAs/wokrshop31/CWA15793 , 2008
[6] WHO, “Laboratory Biosafety Manual (Third Edition)”, https://www.who.int/csr/resources/
publications/biosafety/Biosafety3_j.pdf, 2004
[7] UN Secretariat of the Convention on Biological Diversity, “The Cartagena Protocol on
Biosafety”, http://bch.cbd.int/protocol, 2003.

P8-07

Programmable DNA reaction-diffusion system for a
Voronoi pattern formation in hydrogel
Keita Abe 1
abe@molbot.mech.tohoku.ac.jp

Shin-ichiro M. Nomura 1
nomura@molbot.mech.tohoku.ac.jp
1

Ibuki Kawamata 1
kawamata@molbot.mech.tohoku.ac.jp

Satoshi Murata 1
murata@molbot.mech.tohoku.ac.jp

Department of Robotics, Graduate School of Engineering, Tohoku University, Aramaki -azaAboba 6-6-01, Abobaku, Sendai, Miyagi 980-8579, Japan.

Keywords: Pattern formation, Reaction diffusion system, DNA logic gate
In the early stages of biological development, ordered patterns form as a process of morphogenesis.
Some of ordered pattern such as skin pattern of fish and far pattern of mammals are reproduced bynumerical simulation based on reaction-diffusion system [1]. Such biological phenomena and theory
inspire engineers to build artificial self-organizing system. To realize desired reactions in solution,
synthetic DNA which interacts each other based on its base sequence is studied as a programmablematerial. Some artificial pattern formation by synthetic DNA shows the capability to program reaction[2, 3], however, to program diffusion, which is as important factor for pattern formation as reaction, is
not so focused on.
Here, we demonstrate a method to program both reaction and diffusion in hydrogel for patternformation using synthetic DNAs. To program the reaction, a DNA logic gate is used and to immobilize
some of the DNA into the hydrogel medium, acrydite modified anchor DNA [4] is employed. Using the
DNA logic gate and the anchoring method, we have successfully demonstrated Voronoi pattern formationin hydrogel medium based on sites which is specified as the diffusion source of DNA. Additionally, thediffusion of DNA is modulated by tuning the reversible interaction between diffusible DNA andimmobilized DNA [5]. By the diffusion modulation, the sites were given each “weight” for weighted
Voronoi pattern formation [6].
As a next step, we simplify the reaction system and reduce the component DNAs. We have also
demonstrated similar Voronoi pattern formation with using a smaller number of DNA. We think that thesimplification is effective to reduce undesired interactions and extend the programmability. We willreport the method of the programmable pattern formation and its latest result.

[1] Turing, A. M., Philos. Trans. R. Soc., B, 237(641), 37 – 72, 1952.
[2] Zadorin, A. S., Rondelez, Y., Gines, G., Dilhas, V., Urtel, G., Zambrano, A., Jean-Christophe G.,
Estevez-Torres, A., Nat. Chem., 9(10), 990, 2017.
[3] Zenk, J., Scalise, D., Wang, K., Dorsey, P., Fern, J., Cruz A., Schulman, R., RSC Adv., 7(29),18032 – 18040, 2017.
[4] Kawamata, I., Hosoya, T., Takabatake, F., Sugawara, K., Nomura, S., Isokawa, T., Peper, F.,
Hagiya, M., Murata, S., 2016 Fourth International Symposium on Computing and Networking(CANDAR), IEEE pp. 215-221, 2016.
[5] Rodjanapanyakul, T.,Takabatake, F., Abe, K., Kawamata, I., Nomura, S., Murata, S., Phys. Rev.
E, 97, 2018
[6] Abe, K., Kawamata, I., Nomura, S., Murata, S., Molecular Systems Design & Engineering, 2019.

P8-08Reconstitution of kinesin-based transport complex using
DNA origami
Kodai Fukumoto 1

Yuya Miyazono 2

kfukumoto@protein.osaka-u.ac.jp

Hisashi Tadakuma 1
tadakuma@protein.osaka-u.ac.jp

1
2

Yoshie Harada 1
yharada@protein.osaka-u.ac.jp

Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
Graduate School of Frontier Science, The University of Tokyo, 5 -1-5 Kashiwanoha, Kashiwa,
Chiba 277-8561, Japan

Keywords: DNA nanostructure, DNA origami, Molecular motor, Coordination
Intracellular transport is central to the cellular phenomena, where kinesins and other multiple
motors orchestrate their function. Although it has been known that the intra- and inter-molecular
coordination of kinesins render the long distanc e transport of the cargo 1-3 , it remains elusive how
the molecular layout (e.g. distance between individual motors) affect the collective transport. To
elucidate the coordination mechanism, we have made a transport complex, in which kinesin motors
were integrated onto DNA origami through covalent bond between tag -protein and its ligand (e.g.
SNAP system). However, the low binding rate of the integration process hinder s the yield of theartificial transport complex, which might be attribute to the negative ch arge of the DNA scaffold.
Here, we introduced positively charged Lys-tag to C-terminal of kinesin-SNAPf proteins. The
Lys-tag allowed us to increase the binding rate of dimeric and monomeric kinesins onto DNA
origami by 700 times (k on =4x10 6 /M/s) and 300 times (k on =6x10 5 /M/s), respectively. Furthermore,
we succeeded in constructing artificial transport complex with 8 kinesin molecules. We found thatthe number of kinesin did not affect the speed of artificial transport complex, but affected the runlength. We now investigate the effect of the molecular layout on the transport activity. These
results suggest the potential of Lys-tag for DNA origami-based protein integration and transporter
formation.
[1] Miyazono, Y., Hayashi, M., Karagiannis, P., Harada, Y. and Tadakuma, H. Strain through theneck linker ensures processive runs: a DNA-kinesin hybrid nanomachine study, EMBO J
29:93-106, 2010.
[2] Hendricks, A.G., Perlson, E., Ross, J.L., Schroeder, H.W., Tokito, M. and Holzbaur, E.L.F.
Motor Coordination via a Tug-of-War Mechanism Drives Bidirectional Vesicle Transport ,Current Biology, 20:697–702, 2010.
[3] Furuta, K., Furuta, A., Toyoshima, Y.Y., Amino, M., Oiwa, K. and Kojima, H. Measuringcollective transport by defined numbers of processive and nonproces sive kinesin motors, PNAS,
110:501–506, 2013.

P8-09Gold nanoparticle-loaded DNA hydrogel microparticles
for catalysis in aqueous phase
Daisuke Ishikawa 1

Masahiro Takinoue 2

daisuke-ishikawa@tmu.ac.jp

takinoue@dis.titech.ac.jp

Toru Murayama 1

Masatake Haruta 1

murayama@tmu.ac.jp

haruta-masatake@tmu.ac.jp

1

Research Center for Gold Chemistry, Graduate School of Urban Environmental Sciences,
Tokyo Metropolitan University, 1-1 Minami-osawa, Hachioji, Tokyo, Japan2
Department of Computer Science, Tokyo Institute of Technology, 4259-J2-36 Nagatsuta-cho,
Midori-ku, Yokohama, Kanagawa, Japan

Keywords: DNA hydrogel, gold nanoparticle, catalysis, artificial cell
DNA is a biopolymer with designability that can create structures ranging in size from nanoscale
structures such as DNA origami[1] to macroscale materials such as gels depending on the design ofthe base sequence. In particular, DNA hydrogels have been expected to be applied as smart
materials such as sensing of specific ions by being complexed with metal nanoparticles, which are
based on the use of coordination of metal ions to the bases and phosphate groups of DNA[2].
Here, we present DNA hydrogel particles with molecular permeability and catalytic
function by utilizing the designability of the DNA. The DNA hydrogel particles were composed of
Y-motif subunits consisting of three single-stranded DNAs with a palindromic sequence as a sticky
end. Subsequently, gold nanoparticles (Au NPs) which have been known to show catalytic activity
in water were formed in the DNA hydrogel particles by immersion and reduction of the hydrogel
particles in HAuCl 4 aqueous solution followed by reduction of Au 3+ ions (Figure 1). The DNA
hydrogel particles grew larger when the cooling rate was reduced during annealing. The Au NPs
formed and dispersed uniformly inside the DNA hydrogel particles were confirmed by
HAADF-STEM and TEM observation. The size of Au NPs tended to decrease as the size of DNA
hydrogels increased. Assuming that large size DNA hydrogel particles have high crosslink density,
it suggests that the density inhibits the formation of Au NPs. Catalytic activity of the Au NPs in the
DNA hydrogel particles was examined via the model reaction of the reduction of 4-nitrphenol to
4-aminophenol, which suggested that Au NPs worked as a catalyst.

Figure 1. Scheme for preparation of the Au NP-loaded DNA hydrogel microparticles.
[1] D. Ishikawa, Y. Suzuki, C. Kurokawa, M. Ohara, M. Tsuchiya, M. Morita, M. Yanagisawa, M.
Endo, R. Kawano, and M. Takinoue, DNA Origami Nanoplate-Based Emulsion with Designed
Nanopore Function, Angewandte Chemie International Edition, 2019, accepted.
[2] I. Sissoëff, J. Grisvard, and E. Guillé, Studies on metal ions-DNA interactions: Specificbehaviour of reiterative DNA sequences, Progress in Biophysics and Molecular Biology,
31:165–199, 1978.

P8-10
Negating Latency for Fluid Interactions with Biomolecules
in a Client/Server VR System
Gregory Gutmann

gutmann@c.titech.ac.jp

Azuma Ryuzo 2

azuma.r.ac@m.titech.ac.jp

Akihiko Konagaya 2

kona@c.titech.ac.jp

1

Tokyo Institute of Technology, J3-18, 4259, Nagatsuta, Midori-ku, Yokohama, Kanagawa
226-8503, Japan

Keywords: VR, Real-time, Simulation, GPGPU, Latency, Client/Server
We are working towards creating a virtual research and learning environment at thenanoscale, by utilizing VR and GPGPU computation. VR enables us to visualize and interact withatomic-scale phenomenon in a way that makes them seen life-size, perceptually centimeter tometer scale in VR [1]. GPGPU computing offers large amounts of computational performance;
however, when using a single machine, performance limitations can still arise. Using a client andserver approach can increase the available compute power, but it increases the latency, the roundtrip time of interactions to results. In this work, we are discussing our solution termed “Predictive
Simulation”. By using this extrapolation based approach with a strong predictor we are able tosignificantly decrease the perceived latency while not placing a large compute load on the client
like many current methods for negating latency do [4,5]. Our initial results show a 3x reduction in
the discrepancy between the user’s live position and the results returning from the compute server.
This has enabled live interaction in VR with larger-scale simulations than previously possible.
[1] Gutmann, G., Azuma, R., & Konagaya, A. (2018). A Virtual Reality Computational Platform
Dedicated for the Emergence of Global Dynamics in a Massive Swarm of Objects. Journal of
the Imaging Society of Japan,57(6), 647-653. doi:https://doi.org/10.11370/isj.57.647[2] Azuma, R., Kishi, S., Gutmann, G., & Konagaya, A. (2018). All-atom molecular dynamics offilm supported flat-shaped DNA origami in water. Chem-Bio Informatics Journal,18(0),96-118. doi:10.1273/cbij.18.96[3] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (2017). Parallel Interaction Detection
Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation
System Accelerated by GPGPU. New Generation Computing,35(2), 157-180.
doi:10.1007/s00354-017-0011-5[4] Bernier, Y. W. (2001, March). Latency compensating methods in client/server in-game protocol
design and optimization. In Game Developers Conference (Vol. 98033, No. 425).
[5] Aggarwal, S., Banavar, H., Khandelwal, A.: Accuracy in dead-reckoning based distributed
multi-player games. In: SIGCOMM’04 Workshops, pp. 161–165. ACM Press, New York
(2004)

P8-11

Implementing Real Time Molecular Dynamics with inHaptic Molecular Modeling Environment
Yutaka Ueno 1

uenoyt@ni.aist.go.jp

Gregory Gutmann 2gutmann@c.titech.ac.jp

1

2

Arif Pramudwiatmoko 2

pramudwiatmoko.a.aa@m.titech.ac.jp

Akihiko Konagaya 2
kona@c.titech.ac.jp

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science andTechnology, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, JapanTokyo Institute of Technology, Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan

Keywords: Molecular Robotics, Virtual Reality, Molecular Dynamics
With recent advances of virtual reality hardware and graphics rendering performance, molecularmodeling environment have been improved also from novel software to support real time
molecular dynamics. We have studied hand tracking 3D user interface for the molecular modelingsoftware and introduced haptic rendering facility that allows user to feel touching and grasping
molecules. It is useful in particular to modeling macromolecular complex systems used inmolecular robotics application. Our studies started with existing physics engine, Open Dynamics
Engine [1], and Unity game development suite [2]. Recently, novel simulation code with highperformance rendering was introduced [3] to handle macr omolecules with large numbers of atoms.
Our molecular dynamics code is based on an elastic network model, a simplified force field which
supports modeling works by hand. While recent physics engine simulate position based dynamics,
we employed conventional force based simulation with Lenard-Jones potential and spring force
allowing various interactions between molecular objects . Our implementation topics usinggraphics library DirectX12 will be discussed.
[1] Ueno, Y., Ito, S., Konagaya, A., Implementing a modeling software for animatedprotein-complex interactions using a physics simulation library. Journal of Bioinformatics andComputational Biology 12:1442003, 2014.
[2] Pramudwiatmoko, A., Tsutoh, T., Gutmann, G., Ueno, Y., Konagaya, A., Haptic Rendering
Applied to Hand Tracking 3D User Interface for a Molecular Modeling Environment,
Proceedings of the 24th International Symposium on Artificial Life and Robotics , 109-114, 2019.
[3] Gutmann. G., Inoue, D., Kakugo, A., Konagaya, A., Real-time 3D microtubule gliding
simulation accelerated by GPU computing. International Journal Automation and Computing,
13:108–116, 2016.

P8-12

Design and optimization of a branching structure
for dendric DNA structure
Takayuki Kobayashi 1

Yuki Mano1

k714002@kansai-u.ac.jpk200768@kansai-u.ac.jp

Ryuzo Azuma 2

Yuichi Ohya 3

Yutaka Ueno 4

Akihiko Konagaya 2

yohya@kansai-u.ac.jp

uenoyt@ni.aist.go.jp

kona@c.titech.ac.jp

Akinori Kuzuya 3
kuzuya@kansai-u.ac.jp

1

The Graduate School of Science and Engineering, Kansai University, 3-3-35, Yamate-cho, Suita,
Osaka 564-8680, Japan
2
School of Computing, Tokyo Institute of Technology, J3-25, 4259, Nagatsuta, Midori-ku,
Yokohama, Kanagawa 226-8503, Japan3
Department of Chemistry and Materials Engineering, Kansai University, 3-3-35, Yamate-cho,Suita, Osaka 564-8680, Japan
4
Artificial Intelligence Research Center, AIST, 2-4-7, Aomi, Koutou-ku, Tokyo 135-0064, Japan

Keywords: DNA Origami, Dendrimer, Molecular Dynamics
We considered about optimal design of DNA dendrimer: dendric DNA structure. By utilizing a
GPU based simulation engine, we assessed flexibility of the branching structure of DNA origami
object. Our results demonstrate that three-dimensional DNA origami objects need more flexibility
than flat one to transform with electrostatic repulsion.
DNA origami object originate from the sequence design ofstaple strands, those are many short ssDNAs, so DNA
origami method allows design objects without considering
atomic details. But, it is important to assess structuralproperties of nano objects using atomic-level DNA origami
structure modeling and simulations with atomic detail.
In this study, by molecular dynamics (MD) simulation, we evaluated flexibility of the DNA
origami structure. It is already reported about flat one1) , but it is not reported about threedimensional one yet. According that, we consider the flexibility that is sufficiently to change
geometry with electrostatic interaction.
First, the DNA origami object model was created by
Maestro (Schrödinger’s molecular modeling interface).
Next, ions placement, solvation, and MD simulation by
Desmond (Schrödinger’s MD simulation program).
↑Tetrahedral shape model
We created 2 models of flat-shape one and tetrahedralshape one, and addressed conformational changes up to←Flat shape model
the order of nanoseconds simulation time. According to
the results, the former keeps flat-shape, but latter similar to flat-shape. It means, those objects
stabilized in flat-shape.
We expect that the structure stabilized in tetrahedral-shape with electrostatic interaction, since
phosphate groups have negative charges. So, this structure is less flexible than we expected.
[1] H. Yan, et al., Science 2003, 301, 1882-1884

P9-01

Attempts to establish the in vitro BBB model suitable for drug
development― Comparative study of 2D rat model, 2D human cell line model,
and 3D human cell line model―
Kimiko Kitamura 1,2

Yukari Shigemoto-Mogami 1,2

Agathe Figarol 3

Michiya Matsusaki 3

k.kitamura@nihs.go.jp
a-figarol@chem.eng.osaka-u.ac.jp

shigemot@nihs.go.jp

Seiichi Ishida 2

m-matsus@chem.eng.osaka-u.ac.jp

ishida@nihs.go.jp

1
2
3
4

Kazue Hoshikawa 1,2

hoshikawa@nihs.go.jp

Tomomi Furihata4

tomomif@toyaku.ac.jp

Kaoru Sato 1,2

kasato@nihs.go.jp

Laboratory of Neuropharmacology, Division of Pharmacology, National Institute of Health
Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, Japan
Division of Pharmacology, National Institute of Health Science, 3-25-26, Tonomachi,
Kawasaki-ku, Kawasaki, Kanagawa, Japan
Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1
Yamadaoka, Suita, Osaka, Japan
School of Pharmacy, Tokyo University of Pharmacy and Life Sciences,1432-1 Horinouchi,
Hachioji, Tokyo 192-0392 Japan

Keywords: Microphysiological system, blood-brain barrier, human cell line, permeability
Microphysiological systems（MPS）are new micro-engineered laboratory models that reproduce
tissue phenotypes on a chip. It is expected that MPS will be models which reproduce systemized
cell functions in in vivo and provide solutions to the industrialization of scientific findings. Limited
number of in vitro blood brain barrier (BBB) models are available at present. We therefore areattempting the establishment of the BBB-MPS with high ability of extrapolation to human. A key
challenge in this study is to determine the parameters reflecting BBB function. To this aim, we haveselected some parameters by which we can determine whether the model is BBB-alike or not. The
parameters include substrate permeability, trans-endothelial electrical resistance (TEER), the BBB
functional protein expression, and intracellular localization of these proteins.
In this study, we compared these parameters collected from three different in vitro BBB models,i.e., trans-well 2D rat model (rat endothelial cells, pericytes, and astrocytes), transwell 2D humancell line model (human immortalized endothelial cells, pericytes, and astrocytes) and 3D human cellline model in which immortalized human endothelial cells reproduced the hollow vasculature in theextracellular matrix gel including immortalized human pericytes and astrocytes. We will report thecharacteristic of each model and will suggest possible application range of them.

P9-02

Small Compound-based Direct Reprogramming Using
Large-scale Omics Data
Toru Nakamura 1

Michio Iwata 1

q238049t@mail.kyutech.jp

iwata121@bio.kyutech.ac.jp

Momoko Hamano 1
haman929@bio.kyutech.ac.jp

Ryohei Eguchi 1

Yoshihiro Yamanishi 1

q238008r@mail.kyutech.jp

yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science andSystems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka,
Fukuoka 820-8502, Japan,

Keywords: direct reprogramming, small compounds, pathway, transcriptome, machine learning, iPS
cell, Big data
Direct reprogramming is a research field on direct conversion of fully differentiated mature cells
into a variety of other cell types while bypassing an intermediate pluripotent state (e.g., iPS cell),and it has been receiving much attentions in regenerative medicine [1]. Direct reprogramming is
known to have several advantages over the other reprogramming methods (e.g., iPS cell-mediateddifferentiation) in terms of high efficacy and safety.
Cell differentiation by direct reprogramming is determined by gene induction and gene knockdownof transcription factors (TFs). Virus-based gene induction of necessary TFs is a standard procedurefor direct reprogramming, but there remain a risk of tumorigenesis because of virus. Therefore, thereplacement of TFs by small compounds is highly desired in order to avoid the risk of tumorigenesis[2]. A computational approach is expected to promote the direct reprogramming research, but there
is no information technology for small compound-based direct reprogramming.
In this study, we develop novel computational methods to predict small compounds that induce
direct reprogramming for a variety of human cells. The prediction is performed by integrative
analyses of small compound-induced transcriptome data, TF-perturbed transcriptome data, anddifferentiation-related pathway data.
[1] Sekiya S et al., Direct conversion of mouse fibroblasts to hepatocyte-like cells by defined
factors., Nature 2011;475(7356):390-3.
[2] H. Qin et al., Small molecules for reprogramming and transdifferentiation., Cell Mol. Life Sci.
2017;74:3553-3575.

P9-03
CNN can detect the sensitivity for radioresistance ofcancer cells.
Masamitsu Konno1 , Masayasu Toratani 1 , Jun Koseki 1 , Hideshi Ishii2
mkonno@cfs.med.osaka-u.ac.jp

1

Graduate School of Medicine, Osaka University, 2-2 Yamadaoka Suita Osaka 565-0871, Japan

Keywords: CNN, Cancer, and Radioresistance
Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent
technological advancement. Previously, several attempts have been made to train AI using medical
images for clinical applications. However, whether AI can distinguish microscopic images ofmammalian cells has remained debatable. This study assesses the accuracy of image recognition
techniques using the CNN to identify microscopic images. We also attempted to distinguishbetween mouse and human cells and their radioresistant clones. We used phase-contrast
microscopic images of radioresistant clones from two cell lines, mouse squamous cell carcinoma
NR-S1, and human cervical carcinoma ME-180. We obtained 10,000 images of each of the parental
NR-S1 and ME-180 controls as well as radioresistant clones. We trained the CNN called VGG16
using these images and obtained an accuracy of 96%. Features extracted by the trained CNN wereplotted using t-distributed stochastic neighbor embedding, and images of each cell line were wellclustered. Overall, these findings suggest the utility of image recognition using AI for predicting
minute differences among phase-contrast microscopic images of cancer cells and theirradioresistant clones. SIGNIFICANCE: This study demonstrates rapid and accurate identificationof radioresistant tumor cells in culture using artifical intelligence; this should have applications infuture preclinical cancer research.
[1] Toratani, M., Konno, M. et. al. A Convolutional Neural Network Uses Microscopic Images toDifferentiate between Mouse and Human Cell Lines and Their Radioresistant Clones. Cancer
Research 78(23):6703-6707. (2018)

P9-04

Media analysis of emerging sciences and technologies inJapan- implications for Molecular Robotics

1

2

Ryuma Shineha 1

Mikihito Tanaka 2

r_shineha@seijo.ac.jp

steman@waseda.jp

Faculty of Arts and Literatures, Seijo University, 6-1-20 Seijo, Setagaya-ku, Tokyo 157-0066,
Japan
Graduate School of Political Science, Waseda University, 1-6-1, Nishi-Waseda, Shinjuku-ku,
Tokyo, 169-8050, Japan

Keywords: Media Analysis, Stem Cell Science, GMO, Genome editing, Nanotechnology
Emerging Sciences and Technologies such as Stem Cell Science (SCR), GMO, Genome editing,and Nanotechnology has been recognized among the public through media discourses. For example
media analysis on SCR in Japan showed the appearance of framing of “national promotion” andperipheralization of “ethical, legal, and social issues (ELSI)” particularly after the establishmentof human iPS cell in 2007 [1]. Also on GMO controversies, many media discourses were analyzed[2-5].
To understand of dominant framings of these sciences and technologies will give us many insights
to consider the relationship between Molcular Robotics (Molbot) and the society. This is important
process to think about RRI of Molbot [6].
Thus, we conducted quantitative analysis and content analysis concerning newspaper articles on
SCR, GMO, genome editing, and nanotechnology, over thirty years. We will show the time-lined
change of main framings of these technologies.
[1] Ryuma, Shineha., Attention to Stem Cell Research in Japanese Mass Media: Twenty -Year
Macrotrends and the Gap between Media Attention and Ethical, Legal, and Social Issues , East
Asian Science, Technology and Society: An International Journal , 10(3): 229-246, 2016.
[2] Bauer M., Gaskell G. (eds) Biotechnology-the Making of a Global Controversy.
Cambridge University Press, 2002.
[3] Bauer M., Gaskell G. (eds) Genomics & Society Legal, Ethical & Social Dimension.
EARTHSCAN, 2006.
[4] Hibino, A., Nagata, M., Biotechnology in the Japanese Media? Comparative analysis ofNewspaper Articles on Genetic Engineering between Japan and Europe. Asian Journal of Social
Psychology, 9: 12-23, 2006
[5] Shineha, R., Hibino, A., Kato, K. Analysis of Japanese Newspaper Articles on Genetic
Modification. Journal of Science Communication, 2: 1–8, 2008.
[6] Yoshizawa, G. et al., Responsible innovation in molecular robotics in Japan , Chem-Bio
Informatics Journal, 18: 164-172, 2018.

P9-05

Potential concerns regarding designated ingredientcontaining food of Food Sanitation Law of Japan
Mitsuo Saito 1,2
mitsuosaito-kkr@umin.ac.jp

1
2

Yuzuru Hayashi 1
i_fumi_theory@tba.t-com.ne.jp

Institute for Health Vigilance, 4-25-5, Tanashi, Nishi-Tokyo City, Tokyo 188-0011, Japan
Japan Pharmaceutical Information Center, 2 -12-15, Shibuya, Shibuya-ku, Tokyo 150-0002,
Japan

Keywords: Designated ingredient containing food, Regulatory process, Health hazard potential,
Food Sanitation Law of Japan[Introduction] The Food Sanitation Law of Japan was amended on 13th June 2018, and new
article 8 defined designated ingredient containing food that special caution should be required. It
will be effective within two years, and the Ministry of Health and Welfare (MHLW) preparing forthe ministerial ordinance to designation. In this presentation, we analyzed the regulatory process
for designation including the evaluation process of the health hazard potential.
[Method] The committee materials were obtained from the MHLW and Food Safety Commission(FSC) website.
[Result] The working group meetings that select the candidate of designated materials wereclosed and no proceedings were opened to public. According to the discussion material of
Subcommittee of Newly Developed Food (part of Pharmaceutical and Food Sanitation Council) ,questioner survey of physical deconditioning during the dietary supplement intake was mainevidence of the risk evaluation for certain ingredient.
Some of the authors of the literature were also the member of Subcommittee of Newly Developed
Food of the Food Sanitation Committee and Subcommittee of Newly Developed Food of FSC.
[Discussion] According to the Food Safety Basic Law, FSC is independent risk assessment bodyand overviews risk management bodies such as MHLW. Because of the lack of the specialist of thehealth food field, creating the evidence, analysis of the risk (FSC) and management of the risk
(MHLW) are done by the same person/group.
To avoid the suspicion by the public and keep transparency, it is necessary to prepare the COI
management rules in this field.

P9-06

Multi-functional analysis for applicability evaluation ofhuman iPS cell-derived hepatocytes to DILI assays
Shinichiro Horiuchi

Yukie Kuroda

Su-Ryang Kim

Ryuya Fujii

hori542@nihs.go.jp

y-kuroda@nihs.go.jp

kim@nihs.go.jp 'r-fujii@nihs.go.jp

Yasunari Kanda

Seiichi Ishidakanda@nihs.go.jp

ishida@nihs.go.jp

Division of Pharmacology, National Institute of Health Sciences,
3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa, 210-9501, Japan

Keywords: human iPS cell-derived hepatocytes, drug induced liver injury, long culture,
bile canaliculi, drug metabolism[Purpose] Drug Induced Liver Injury (DILI) is the major factor leading to discontinuation of drug
development, and it is important to identify DILI risk as early as possible during drug development
process. The sustainable test system that can provide stable results is expected in DILI evaluation.
However, DILI evaluation system using cryopreserved / primary human hepatocytes which isutilized generally at the early stage of drug development, is difficult to produce sustainably stable
results because for the cell supply limitation and the differences among their donors. We have been
studying the application of human iPS cell-derived hepatocytes (hiPSC-hep) to DILI assays inorder to construct a system which can produce stable results repeatedly. In this study, we evaluatedthe functions of drug metabolism, fat storage, long-term culture and bile excretion required for
DILI evaluation in hiPSC-hep. In addition, we curried out cytotoxicity test for typical compounds
using hiPSC-hep.
[Methods] hiPSC-hep from vendor A was used for the experiment. The expression s of major CYPs
(CYP1A1, CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4) was compared with those in human
cryopreserved hepatocytes (cryo-hep) by qPCR. Oleic acid was added to the medium andaccumulation of lipid droplets in the cell was observed by Oil-Red O staining. Bile canaliculi wereobserved using fluorogenic substrates of biliary excretion transporters MRP2 or BSEP. In addition,
long-term culture was performed using the long-term culture medium from vendor B, and cellmorphology, CYP gene expression, and bile canaliculi formation were observed. Furthermore, thecytotoxicity to typical compounds was evaluated by ATP activity and released LDH activity, andwas compared with cryo-hep.
[Results and Discussion] The expressions of CYP1A1, CYP2C19, and CYP3A4 were at the samelevel to those of cryo-hep. The fat droplets were increased by addition of oleic acid and the fat
storage ability was confirmed. By using the long-term culture medium from vendor B, the cells
could be cultured for 28 days without the detachment. In addition, the expressions of CYPs wereincreased and the bile canaliculi were extended by long-term culture. Changes in ATP activity andreleased LDH activity by exposure to the compounds used in this study were similar to cryo -hep.
These results indicated that hiPSC-hep from vendor A has various functions required for DILI
evaluation and are tolerable for the long-term culture. Hereafter, we will collect data in cells ofdifferent lots and donors in order to construct a stable evaluation system.

P9-07

Evaluation of Cell Culture Profiling System with
HepG2 Cell Culture Using Microphysiological
Systems
Ryuya Fujii

Shinichiro Horiuchi

Su-Ryang Kim

Yasunari Kanda

Seiichi Ishida

kim@nihs.go.jp

kanda@nihs.go.jp

ishida@nihs.go.jp

r-fujii@nihs.go.jp

hori542@nihs.go.jp

Yukie Kuroda

y-kuroda@nihs.go.jp

Division of Pharmacology, National Institute of Health Sciences,
3-25-26 Tonomachi, kawasaki-ku, Kawasaki, 210-9501, Japan

Keywords: Microphysiological systems, HepG2, cell culture profiling system[Background]
In recent years, in order to reduce the cost of evaluating the safety and the pharmacokinetics ofcompounds in drug development, there is a demand for the development of a technology capable ofculturing cells while maintaining high functions in vitro. We focused on the cell culture profilingsystem that can measure 95 kinds of medium components including amino acids at once by
LC-MS/MS as one of the methods to evaluate expression of cell functions and to their quality.
HepG2 cells, hepatocyte-derived cancer cell, were cultured on a cell culture device, QuasiVivo, oneof the commercially available MPS (Microphysiological systems) which mimicks the environment
in vivo, and the effectiveness of quality control by the cell culture profiling system was evaluated.
[Method]
HepG2 cells were cultured in Dulbecco's Modified Eagle's Medium (4.5 g/L glucose DMEM
supplemented with 10% fetal bovine serum and 1% antibiotic and antifungal solution) for 5 days andthe culture supernatants were collected over time. After protein removal by addition of acetonitrile tothe culture supernatant, LC-MS/MS (LCMS-8050, Shimadzu) was used to quantify the culturecomponents. Cell culture profiling system (Shimadzu) was used for the culture medium profiling.
[Result]
One of the culture components strongly correlated with the cell number was found as a result of
measuring the culture supernatant of HepG2 cells over time using the cell culture profiling system.
When HepG2 cells were cultured with QuasiVivo (QV500, Kirkstall), and the medium component
changes were detected compared with those of the control in static culture. We are continuing the
HepG2 cell culture with other MPS and measuring their culture medium components, thus we would
like to discuss the effectiveness of this method.

P10-01

Evidence for the utility of human induced pluripotent stem
cell-derived neurons in safety pharmacology-fact data
indicating the achievement of network activities
Kanako Takahashi 1

Kaori Chujo 1

Yasunari Kanda 2

ktakahashi@nihs.go.jp

chu-773@nihs.go.jp

kanda@nihs.go.jp

Aoi Odawara 3 ,Ikuro Suzuki 3 ,i-suzuki@tohtech.ac.jp

Kaoru Sato 1
kasato@nihs.go.jp

1 Laboratory of Neuropharmacology, Division of Pharmacology, National Institute of Health
Sciences, Kanagawa 210-9501, Japan
2 Division of Pharmacology, National Institute of Health Sciences, Kanagawa 210-9501, Japan
3 Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology,
Miyagi, 982-8577, Japan

Keywords: Human induced pluripotent stem cell (hiPSC)-derived neurons, Neural networks,
Micro-electrode array (MEA), Synchronized burst firing (SBF), Calcium signaling,
Immunocytochemistry
Neural networks consisted of human induced pluripotent stem cell-derived neurons (hiPSCN)
have great potentials in drug development. However, little data are available concerning
reproducibility of stable hiPSCN networks and their specific characters. In this study, we
reproduced hiPSCN neural networks by the specific culture condition (high density culture, longculture period) and characterized the resulting neural networks.
XCell neurons (XCell Science) are commercially available hiPSCN mimicking forebrainneurons, which are comprised of glutamatergic excitatory neurons and GABAergic inhibitory
neurons. XCell neurons were seeded at 3.0×10 5 cells/cm2 and cultured for 2-3 months. We firstexamined synapse formation immunoytochemically. We used Scales, optical clearing methods,
owing to the poor transparency of the samples. The co-localization of presynaptic marker
synapsin1 and postsynaptic marker PSD95 on MAP2-positive dendrites appeared at DIV 42. The
number of synapsin1(+)PSD95(+) clusters was increased after DIV42-63. We also confirmed theneural networks spontaneous electrophysiological activities by micro-electrode array (MEA)
systems. Synchronized burst firing (SBF) activity due to synapse transmission within networks
was observed from DIV 42 and increased at DIV 56. The application of NMDAR antagonist AP5
and AMPA/kainate antagonist DNQX decreased SFB activities, respectively. Furthermore, wecharacterized these cells pharmacologically by fura-2 Ca 2+ imaging. Most cells with neuron-likeshape showed the responsiveness ([Ca 2+ ] in increase) to L-glutamate (L-Glu) from 14 DIV.
L-Glu-induced [Ca 2+ ] in elevations were suppressed by AP5 and DNQX at 28 DIV, respectively.
Spontaneous activity appeared at 14 DIV and thereafter. These activities were blocked by Na +channel blocker tetrodotoxin (TTX) and were enhanced by GABAA antagonist picrotoxin (PIC). At
90 DIV, 80.3 % hiPSCN were responsive to PIC. These data suggest that our culture protocol
enables the stable reproduction of hiPSCN neural networks and the resulting human in vitro
networks are promising tools for the drug development.

P10-02

Development of automatic analysis and quality control ofmass spectrometry-based metabolome data
Yasuko Aita 1
ait_18y@tokyo-med.ac.jp

12

Masahiro Sugimoto 12
mshrsgmt@tokyo-med.ac.jp

Research and Development Center for Minimally Invasive Therapies Health Promotion andPreemptive Medicine, Tokyo Medical University, Shinjuku, Tokyo 160-8402, Japan
Institute for Advanced Biosciences, Keio University 246-2 Mizukami, Kakuganji, Tsuruoka,
Yamagata, 997-0052, Japan

Keywords: Metabolomics, Quality control, AI
Various tools for processing measurement data of mass spectrometry with separation system, suchas, capillary electrophoresis and liquid chromatography, has been developed as both academic andcommercial software. The processes include data conversion, peak detection, alignment and peak
matching among samples, peak interpretation, compound identification, and quantification. Ideally,a pipeline conducting all process providing enough processing quality is preferable while nosoftware fulfill this requirement, so far [1-3].
Each process has various options, which make it difficult for automatic processing and require theoptimization of these options based on the given data [4]. In general, users are required torepeatedly optimize the options based on the analyzed results using something evaluation function
and visual inspections. However, this would reduce the reproducibility of the analyzed results.
Here, we have developed a novel tool named MasterHands that tries to reproduce the users’manual curation of metabolomic data. As a training data, manually curated and not curated data
should be given. This software learns the difference between these data and tries to optimize theoptions, which yields the high quality analytical results. We evaluated the performance of thedeveloped algorithm based on the peak area integrated with and without automatic option
optimizing. The latter showed the peak area close to the ones curated by users. The calculationengine and GUI were implemented in C ++ and Java, respectively. Also, Python API was
implemented for being controlled by scripting language.

[1] Sugimoto Masahiro, et al. "Bioinformatics tools for mass spectroscopy-based metabolomic dataprocessing and analysis." Current bioinformatics 7.1 (2012): 96-108[2] Sugimoto Masahiro. "Metabolomic pathway visualization tool outsourcing editing function."
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International
Conference of the IEEE. IEEE, 2015.
[3] Sugimoto Masahiro, et al. "MMMDB: mouse multiple tissue metabolome database." Nucleic
acids research 40.D1 (2011): D809-D814.
[4] Sugimoto Masahiro, et al. "Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis." Metabolomics 6.1 (2010): 27-41.

P10-03

CASLcDB: Comprehensive Annotation of human SLctransporters DataBase
Hafumi Nishi 1
hnishi@ecei.tohoku.ac.jp

Kazunori Yamada 1
kyamada@ecei.tohoku.ac.jp

Yuya Hanazono 1
hanazono@sb.ecei.tohoku.ac.jp

Hitoshi Yamagata 2
hitoshi.yamagata@medical.canon

Kengo Kinoshita 1
kengo@ecei.tohoku.ac.jp

12

Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki aza Aoba,
Aoba-ku, Sendai, Miyagi 980-8579 Japan
Advanced Research Laboratory, Canon Medical Systems Corporation, 1385 Shimoishigami,
Otawara, Tochigi 324-0036 Japan

Keywords: solute carrier transporter, database, genetic variants
SoLute Carrier (SLC) transporter is a transmembrane protein family which mediates thetransportation of various molecules and ions. The size of the family is extremely large (~ 500 proteins)and can be divided into more than 50 subfamilies, some of which are known to be responsible forbiologically crucial pathways, such as glucose intake. Additionally, SLC transporters have beenrecognized as therapeutic targets, as some are implicated in Mendelian diseases and others are related
to drug responses. Despite the importance in cell survival and human health, most of SLC proteins andsubfamilies are not well-characterized.
Aside from experimental studies of individual subfamilies and proteins, some SLC databases, such as
The Transporter Classification Database (TCDB) and iMusta4SLC, try to describe the whole picture of
SLCs and provide their functional information. However, these databases mainly focus on biochemical
features of transporters, namely substrates and transport modes, not their cellular functions. To gain
deeper understanding of SLC transporters, both biological and biochemical information should be
integrated into a single source.
Here we present CASLcDB, a comprehensive database of human SLC transporters, which aggregatesvarious biological and biochemical information of SLC transp orters. With the database, users can
explore structural information, biological networks/pathways and evolutionary features of each SLC
protein. Structural models are provided with genetic variations from major variant databases to help
identify harmful mutations and frequencies of variants in population, while biological networks andpathways imported from multiple data sources illustrate the functional relations between SLC andnon-SLC proteins in cells. Phylogenetic trees across different species demonst rate surprisingevolutionary diversity of subfamilies. CASLcDB is available at https://caslc.sb.ecei.tohoku.ac.jp/[1] César-Razquin, A., Snijder, B., Frappier-Brinton, T., Isserlin, R., Gyimesi, G., Bai, X.,
Reithmeier, R.A., Hepworth, D., Hediger, M.A., Edwards, A.M., Superti-Furga, G. A Call for
Systematic Research on Solute Carriers. Cell. 162(3):478-487, 2015
[2] Lin, L., Yee, S.W., Kim, R.B., Giacomini, K.M. SLC transporters as therapeutic targets:
emerging opportunities. Nature Review Drug Discovery. 14(8):543-546, 2015[3] Saier, M.H., Reddy, V.S., Tsu, B.V., Ahmed, M.S., Li, C., Moreno-Hagelsieb, G. The
Transporter Classification Database (TCDB). Nucleic Acids Research. 44(D1):D372-D379, 2016[4] Higuchi, A., Nonaka, N., Yura, K. iMusta4SLC: Database for the structural property andvariations of solute carrier transporters. Biophysics Physicobiology. 15:94-103, 2018

P10-04

Approach of the global QSAR modeling for fish acutetoxicity and the future issue for improvement.
Koji Jojima 1

Yumi Akahori 2

jojima-koji@ceri.jp

akahori-yumi@ceri.jp

Hideki Miyaura 1
miyaura-hideki@ceri.jp

1

2

Chemicals Assessment and Research Center, Chemicals Evaluation and Research Institute,Japan, 1600 Shimotakano, Sugito-machi, Kitakatsushika-gun, Saitama 345-0043, Japan
Chemicals Assessment and Research Center, Chemicals Evaluation and Research Institute,Japan, 1-4-25 Koraku, Bunkyo-ku, Tokyo 112-0004, Japan

Keywords: Fish Toxicity, Quantitative Structure-Activity Relationship, Machine Learning
Quantitative Structure-Activity Relationship (QSAR) models for prediction of fish acute toxicity
are one of methods that have been used as screening tools or alternatives to animal testing for
ecological risk assessment in TSCA or REACH regulation. However, most of freely available
QSAR models let their applicability domain limited for keeping predictability[1]. The goal of thisstudy is to develop the QSAR model for fish acute toxicity with wider applicability domain
keeping high predictability. For achieving this goal, we tried to construct the prediction model offish acute toxicity from chemical descriptors (chemical property, fingerprints, substructure
information etc.) using several machine learning methods. The fish 96-hr LC50 values for over
1,000 chemicals (including Class1~4 of Verhaar Classification) were collected from publicallyavailable databases and were used to model construction. The R 2 of the constructed model was 0.69
in cross-validation, and 0.65 in external validation. And the analysis of cross-validation result
revealed the low predictability for highly reactive chemicals. Chemical reactivity was reported as
one of the factors affect the aquatic toxicity; for example, RC50 (GSH) of Michael Acceptors werecorrelated with their IGC50 for Tetrahymena[2]. We confirmed that LC50 for fish was correlatedwith RC50 (GSH) than logP in reactive chemicals in the dataset. Therefore, the inclusion ofquantitative parameter of chemical reactivity has potential to improve the global QSAR model.

References[1] Bin-Le LIN, Chie YAMADA, Kikuo YOSHIDA, Situation and Challenge about in silico
Technique Development for Predicting Ecotoxicity of Chemical Substances for Risk Tradeoff
Analysis, Japanese Journal of Risk Analysis, 21(1), 23-31, 2011.
[2] David Asturiol, Andrew Worth, The Use of Chemical Reactivity Assays in Toxicity Prediction,
JRC Scientific and Technical Reports, 2011. Retrieved July 29, 2019, fromhttp://publications.jrc.ec.europa.eu/repository/bitstream/JRC65567/lbna24870enn.pdf

P10-05
Attempt to Construct a Primitive Metabolic Network
in Deep Hydrothermal Vent Environments
Ryo Hamano 1
191x213x@stu.kobe-u.ac.jp

Kohei Shimamura 1
shimamura@port.kobe-u.ac.jp

1
2

Yuto Kitajima 21645008t@stu.kobe-u.ac.jp

Shigenori Tanaka 1
tanaka2@kobe-u.ac.jp

Graduate School of System Informatics, Kobe University, 1 -1, Rokkodai, Nada, Kobe 657-8501, Japan
Department of Computer Science and Systems Engineering , Kobe University

Keywords: Origin of Life, Abiogenesis, Deep Hydrothermal Vent, Metabolic Network, Chemical
Reaction Network, Network Expansion Algorithm
In recent years, a variety of scenarios and models associated with origin of life (or abiogenesis) havebeen proposed. However, most discussions of the origin begin with the assumption that fundamental
enzymes and genes for starting metabolism already had existed (e.g. [1]). How a non-biologicalchemical reaction network was evolved to the current metabolic network by acquiring robustness inreaction processes is thus still unclear.
In an attempt to fill the gap, we construct a primitive metabolic reaction network from non-biologicalchemical reactions. The constructive procedure is as follows:
1.

2.
3.

The database is prepared from the literature by collecting the abiotic chemical reactions
occurring in the deep hydrothermal vents as promising places for the birth of life. Most of theinformation sources are from the literature that reported simulation experiments for thehydrothermal vent environment. In addition, we consider the cases in which the hydrothermalvent environment is not assumed but the experimental conditions are similar to it as well.
Materials (such as carbon dioxide, ammonia, hydrogen sulfide, and olivine) which wereabundant in the pre-biotic environment, are regarded as starting chemical seeds.
From the seeds, one performs network expansion algorithm [2] to obtain non-biologicalchemical reaction network, where the network expansions are iteratively carried out until
products are not generated while referring to the chemical reaction database.

Finally, 165 chemical reactions occurred, and a network consisting of 64 chemicals (nodes) was
created. By calculating the degree of each molecule contained in the constructed network , we havefound that the glycine and formaldehyde are the hub molecules that play an important role inmaintaining or growing the network of chemical reactions. As another characteristic, our network
showed high modularity. This trend can also be identified in the metabolic networks of current
organisms [3]. Conducting clustering for the constructed network resulted in the split of the generated
metabolites into four domains. Interestingly, the chemical reactions constituting the four clusters werebased on the iron-sulfur world hypothesis, Oro’s experiments, a series of amino acid production
experiments simulated for the hydrothermal environment, and serpentinization, respectively. Chemical
reactions inspired by the different abiogenesis may be mutually compensated and integrated.
We thus believe that the tracking of the network expansion will lead to an understanding of how eachcharacteristic of the network shows changes and it will also lead to an understanding of the entire
evolutionary process of non-biological chemical reaction network.

[1] Sojo V., Herschy B., Whicher A., Camprubi E., Lane N., ”The Origin of Life in Alkaline
Hydrothermal Vents”, Astrobiology, 16, 2 (2016).
[2] Goldford J.E., Hartman H., Smith T.F., Segrè D., ”Remnants of an Ancient Metabolism without
Phosphate”, Cell, 168(6),1126 (2017).
[3] Ravasz E., Somera A.L., Mongru D.A., Oltvai Z.N., Barabási A.L., ”Hierarchical Organizationof Modularity in Metabolic Networks”, Science, 297, 1551 (2002).

P10-06

Computational Direct Reprogramming by Integrating
Genome, Transcriptome and Epigenome Data
Ryohei Eguchi 1

Momoko Hamano 1

Michio Iwata 1

q238008r@mail.kyutech.jp

haman929@bio.kyutech.ac.jp

iwata121@bio.kyutech.ac.jp

Shinya Oki 2

Yoshihiro Yamanishi 1

Toru Nakamura 1
q238049t@mail.kyutech.jp
1

2

soki@dev.med.kyushu-u.ac.jp

yamani@bio.kyutech.ac.jp

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680 -4 Kawazu, Iizuka, Fukuoka 820-8502, Japan,
Department of Developmental Biology, Graduate School of Medical Sciences, Kyu shu
University, Fukuoka, Japan

Keywords: direct reprogramming, transcription factors, epigenome, transcriptome, machinelearning, iPS cell, Big data
Direct reprogramming is a research field on direct conversion of fully differentiated mature cells
into a variety of other cell types while bypassing an intermediate pluripotent state (e.g., iPS cell).
Direct reprogramming is known to have several advantages over the other reprogramming methods(e.g., iPS cell-mediated differentiation) in terms of high efficacy and safety, thus, it has been
receiving much attentions in regenerative medicine [1].
Cell differentiation by direct reprogramming is determined by a set of transcription factors (TFs),
but the experimental determination of the TF set is extremely difficult and costly. In fact, and there
remain numerous cell conversions for which the associated TFs have not been identified. A
computational approach for determining TFs is expected to promote the direct reprogramming
research, but reliable computational methods with the objective have not been established.
In this study, we develop novel computational methods to predict TFs that induce direct
reprogramming for a variety of human cells. The prediction is performed by an integrative analysis
of genomic data (e.g., enhancers and promoters), transcriptome data ( e.g., gene expression profiles
on human cells) and epigenome data (e.g., ChIP-seq data) for TFs. We show the usefulness of the
proposed methods on several direct cell conversions.
[1] S. Sekiya and A. Suzuki., Direct conversion of mouse fibroblasts to he patocyte-like cells bydefined factors. Nature 2011;475(7356):390-3.