P1-01

The juxtamembrane region of TrkA kinase is critical for
inhibitor selectivity
Noritaka Furuya 1,2 , Takaki Momose 1 , Kenji Katsuno 1 , Nobuhiko Fushimi 1 ,noritaka_furuya@pharm.kissei.co.jp

Hideyuki Muranaka 1 , Chiaki Handa 1 , Tomonaga Ozawa 1 , Takayoshi Kinoshita 2
kinotk@b.s.osakafu-u.ac.jp
1

2

Kissei Pharmaceutical, 4365-1, Kashiwabara, Hotaka, Azumino City, Nagano Pref. 399-8304,
Japan
Graduate School of Science, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai,
Osaka 599-8531, Japan

Keywords: TrkA kinase, Juxtamembrane, Crystal structure, Selective allosteric inhibitor
Although numerous crystal structures for protein kinases have been reported, many include only
the kinase domain but not the juxtamembrane (JM) region, a critical activity-controlling segmentof receptor tyrosine kinases (RTKs). In this study, we determined the X-ray crystal structure of thetropomyosin receptor kinase (Trk) A selective inhibitor A1 complexed with the TrkA kinasedomain and the JM region. This structure revealed that the unique inhibitor-binding pocket createdby a novel JM configuration yields significant potency and high selectivity against TrkB and TrkC.
Moreover, we validated the importance of the JM region for the potency of A1 using in vitro assays.
The introduction of moieties that interact with the JM region will be one of the most effective
strategies for producing highly selective RTK inhibitors.
[1] Furuya, N.; Momose, T.; Katsuno, K.; Fushimi, N.; Muranaka, H.; Handa, C.; Ozawa, T.;
Kinoshita, T., The juxtamembrane region of TrkA kinase is critical for inhibitor selectivity.
Bioorg Med Chem Lett 2017, 27 (5), 1233-1236.
[2] Benito-Gutiérrez, E.; Garcia-Fernàndez, J.; Comella, J. X., Origin and evolution of the Trk
family of neurotrophic receptors. Mol Cell Neurosci 2006, 31 (2), 179-92.
[3] Bertrand, T.; Kothe, M.; Liu, J.; Dupuy, A.; Rak, A.; Berne, P. F.; Davis, S.; Gladysheva, T.;
Valtre, C.; Crenne, J. Y.; Mathieu, M., The crystal structures of TrkA and TrkB suggest key
regions for achieving selective inhibition. J Mol Biol 2012, 423 (3), 439-53.
[4] Chang, D. S.; Hsu, E.; Hottinger, D. G.; Cohen, S. P., Anti-nerve growth factor in pain
management: current evidence. J Pain Res 2016, 9, 373-83.
[5] Wang, T.; Lamb, M. L.; Scott, D. A.; Wang, H.; Block, M. H.; Lyne, P. D.; Lee, J. W.; Davies, A.
M.; Zhang, H. J.; Zhu, Y.; Gu, F.; Han, Y.; Wang, B.; Mohr, P. J.; Kaus, R. J.; Josey, J. A.; Hoffmann,
E.; Thress, K.; Macintyre, T.; Omer, C. A.; Yu, D., Identification of 4-aminopyrazolylpyrimidines aspotent inhibitors of Trk kinases. J Med Chem 2008, 51 (15), 4672-84.

P1-02
Calculation of intramolecular Reaction of Hydroxymethyl
Rhodamine Derivatives for Development of Fluorescent
Probes Based on Computational Chemistry
Ryo Tachibana 1
ryo-tachibana@g.ecc.u-tokyo.ac.jp

Satoshi Suzuki 3
suzuki.satoshi.8v@kyoto-u.ac.jp

Mako Kamiya 2,4
mkamiya@m.u-tokyo.ac.jp

Keiji Morokuma 3
morokuma.keiji.3a@kyoto-u.ac.jp

Yasuteru Urano 1,2,5
uranokun@m.u-tokyo.ac.jp
1

2

3

4
5

Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-0033, Japan
Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-0033, Japan
Fukui Institute for Fundamental Chemistry, Kyoto University, Takano-Nishihiraki-cho 34-4,
Sakyou-ku, Kyoto 606-8103, JapanJST PRESTO
AMED CREST

Keywords: Free Energy Calculation, Intramolecular Reaction, Acid-base Balance, Transition State
Calculation, First-Shell Hydration
HMR (Hydroxymethyl Rhodamine) derivatives can exist in two forms (open, closed) byintramolecular spirocyclization. The proportion of two forms at equilibrium state changes
according to pH. Various fluorescence probes have been developed using p K cycl (pH value where
the concentration of open form and that of closed form are the same ) as an indicator [1][2] . In thisresearch, we analyzed this intramolecular reaction computationally and found a powerful method
to predict pK cycl of HMR derivatives directly, without synthesizing any reference compounds .
We focused on closed-to-open reaction at acidic condition, and performed free energy
calculation of local minima of HMR derivatives, considering first -shell hydration and protontransfer. Next, we searched each transition states which corresponded to calculated local minimum,and evaluated their contribution ratio to the reaction. As a result, we found energetically favorable
chemical pathway, which dominated almost all of close-to-open reaction. We made a formula for
calculating pK cycl , based on equilibrium model of HMR derivatives including acid -base balance of
their amino group and hydroxymethyl group. By calculating free energy gap throughout
above-mentioned reaction path, we succeeded in reproducing p K cycl of measured HMR derivatives
and predicting proportion of unknown derivatives.
Moreover, we succeeded in developing new red fluorescence peptidase probes by applying pK cycl
prediction, which shows the possibility of accurate and efficient molecular design using
calculation with careful analysis of solvent behavior including first-shell hydration.
[1] Sakabe, M.; Asanuma, D.; Kamiya, M.; Iwatate, R. J.; Hanaoka, K.; Terai, T.; Nagano, T.;
Urano, Y. Journal of the American Chemical Society 2013, 135, 409.
[2] Uno, S. N.; Kamiya, M.; Yoshihara, T.; Sugawara, K.; Okabe, K.; Tarhan, M. C.; Fujita, H.;
Funatsu, T.; Okada, Y.; Tobita, S.; Urano, Y. Nature chemistry 2014, 6, 681.

P1-03

Evaluation of log P of endocrine disruptors
using DFT methods
Masao Fujisawa 1
fujisawa@waka.kindai.ac.jp

Hirohito Ikeda 2
ikeda@fukuoka-u.ac.jp

1

2

3

Mamu Tabe 1
b14035tm@waka.kindai.ac.jp

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

Department of Biotechnological Science, Kindai University, 930 Nishimitani, Kinokawa-City,
Wakayama, Tokyo 649-6493, Japan
Department 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: DFT-D, Vibrational frequencies, log P
Solvation phenomena play a significant role in chemical reactions and biomolecular recognition;
however, it can be very difficult to determine the related thermodynamic quantities. For example,
to calculate the solvation Gibbs energy, one must know temperature dependence of thevaporization enthalpy or the vapor pressure. Moreover, the dispersion energy is an important
contributor to the solvation energy of a solute. In an attempt to predict solvation Gibbs energy,
theoretical methods including explicit or continuum solvent models have been developed andapplied [1-3]. In an explicit solvent model, there have been a n attempt of hydration Gibbs
energies computed using the molecular dynamics simulation and the energy -representation theoryof solvation [4]. In this study, the solvation Gibbs energies for several endocrine disruptors were
calculated using dispersion-corrected density functional theory (DFT-D) method, which were thencompared with experimental thermodynamic data. To decide the lowest energy structure,
conformation search was performed. The obtained lowest energy conformer was the initialstructure of the DFT-D calculations in gas phase. Vibrational frequencies were calculated forthese optimized geometries at the same level of theory in gas phase. The solute ge ometries were
optimized in water using DFT-D functions. Similarly, vibrational frequencies were calculated forthese optimized geometries at the same level of theory in water. Gibbs energies of solutes were
determined by vibrational frequencies in gas phase and in water. Partition coefficients (log P) were
determined using the differences in Gibbs energy between two solvents (water, n-octanol). These
predicted values well reproduced the experimental log P.

P1-04

Conformational regularity of the condensed tannins
investigated by free-energy calculation
Reina Takahashi 1

Takuma Todoroki 1

14a3031h@shinhsu-u.ac.jp

Hiroshi Fujii 1,2

hfujii@shinshu-u.ac.jp

14a3035a@shinshu-u.ac.jp	 Hidefumi Makabe 1

makabeh@shinshu-u.ac.jp

Koji Umezawa 1,2

koume@shinshu-u.ac.jp

12Faculty of agriculture, Shinshu University, 8304 Minami-minowa, Kami-ina,
Nagano, 399-4598, Japan
Department of Interdisciplinary Genome Sciences and Cell Metabolism, Institute for
Biomedical Sciences, Interdisciplinary Cluster for Cutting Edge Research, Shinshu University,
Minami-minowa, Kami-ina, Nagano, 399-4598, Japan

Keywords: Natural product, Flavan-3-ol, Proanthocyanidin
Three-dimensional (3D) molecular structure is responsible for biological function. Biopolymers
can adopt some stable conformations. The condensed tannins are known as the polymer ofpolyphenol in natural products. However, most of their conformations are still elusive. Then, we
have investigated the stable conformations of the condensed tannins by free-energy calculation to
figure out the conformational regularity.
Free-energy calculation was done for the natural polymer, the condensed tannins. The unit of thecondensed tannin is flavan-3-ol such as catechin derivatives. The two adjacent units are connectedby a single covalent bond; inter-flavan bond. The conformational freedom around the inter-flavanbond designates the relative direction between the upper and the lower units. In the free-energy
calculation, we conducted the umbrella-sampling molecular dynamics simulations with thereaction coordinate of the dihedral angle around the inter-flavan bond. Herein, we chose the unit ofcatechin (cat), epicatechin (epi), gallocatechin (gc) and epigallocatechin (egc). For dimers oftannins, all permutation of two units were calculated (e.g. cat-cat, cat-epi, epi-cat, cat-gc, gc-cat,etc; total of 16 dimers). For trimers of tannins, total of 4 trimers (cat-cat-cat, epi-epi-epi, gc-gc-gc
and egc-egc-egc) were investigated.
The results showed that the free-energy landscape (FEL) of the dimer depended on the upper unit.
The FELs of the dimers with the upper unit of cat and gc had one minima while those of epi andegc did two minimal basins. It may be explained by hydrogen-bond pattern between upper andlower units. The cat unit differs from the epi one mainly at the point of chirality of connection to
the lower unit. The hydrogen bonds between the upper and lower units can be formed according to
the upper-unit conformation, which can contribute the structural stability. For the trimers, theFELs of cat and gc showed two low regions whereas those of epi and egc did four, which might
indicate that the conformational stability of polymer depends on the adjacent units. In ourpresentation, the detail of conformation will be displayed. Furthermore, we will show the recent
results for cat- and epi-polymers up to 7-mer.

P1-05Functional profiling of asymmetrically-organized human
CCT/TRiC chaperonin

1

2

3

Kazutaka Araki 1

Atsushi Suenaga 1, 2

k-araki@aist.go.jp

atsushi.suenaga@aist.go.jp

Tohru Natsume 1, 3

Kazuhiko Fukui 1

t-natsume@aist.go.jp

k-fukui@aist.go.jp

Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology, Tokyo 135-0064, Japan
Department of Biosciences, College of Humanities and Science s, Nihon University, 3-25-40
Sakurajosui Setagaya-Ku, Tokyo 156-8550, Japan
Robotic Biology Institute, Inc., Tokyo 135-0064, Japan

Keywords: Chaperonin, CCT, Cysteine, Protein modeling, Electrostatic calculation
Molecular organization of the eukaryote chaperonin known as CCT/TRiC complex was recently
clarified. Eight distinct subunits are uniquely organized, providing a favorable folding cavity forspecific client proteins such as tubulin and actin. Because of its heterogeneous subunit
composition, CCT complex has polarized inner faces, which may underlie an essential part of its
chaperonin function. In this study, we structurally characterized the closed and open states of CCT
complex, using molecular dynamics analyses. Our results showed that the inte r-subunit interactionenergies were asymmetrically distributed and were remodeled during conformational changes of
CCT complex. In addition, exploration of redox related characteristics indicated changes in innersurface properties, including electrostatic potential, pKa and exposure of inner cysteine thiolgroups, between the closed and open states. Cysteine activation events were experimentally
verified by interaction analyses, using tubulin as a model substrate. Our data highlighted theimportance of dynamics-based structural profiling of asymmetrically oriented chaperonin function.

[1] Araki, K., Suenaga, A., Kusano, H., Tanaka, R., Hatta, T., Natsume, T., and Fukui, K.,
Functional profiling of asymmetrically -organized human CCT/TRiC chaperonin, Biochemicaland biophysical research communications, 481:232-238, 2016.
[2] Araki, K., Kusano, H., Sasaki, N., Tanaka, R., Hatta, T., Fukui, K., and Natsume, T ., Redox
Sensitivities of Global Cellular Cysteine Residues under Reductive and Oxidative Stress,
Journal of proteome research, 15:2548-2559, 2016.
[3] Araki, K., Ushioda, R., Kusano, H., Tanaka, R., Hatta, T., Fukui, K., Nagata, K., and Natsume,
T., A crosslinker-based identification of redox relay targets, Analytical biochemistry, 520:22-26,
2017.

P1-06

Flexible Docking using Replica-Exchange Molecular
Dynamics Simulation
Suyong Re 1

suyongre@riken.jp

Motoshi Kamiya 2

motoshi.kamiya@riken.jp

1

2

3

Hiraku Oshima 1

hiraku.oshima@riken.jp

Yuji Sugita 1,2, 3

sugita@riken.jp

RIKEN Quantitative Biology Center (QBiC), Integrated Innovation Building 7F, 6-7-1
Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, JapanRIKEN Advanced Institute for Computational Science (AICS), Integrated Innovation Building
7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, JapanRIKEN Theoretical Molecular Science Laboratory and iTHES, 2-1 Hirosawa Wako-shi, Saitama
351-0198, Japan.

Keywords: Molecular dynamics simulations, enhanced sampling, protein-ligand binding
Accurate prediction of ligand binding structure and energetics remains a challenge. Accounting forboth ligand binding and protein structure changes, such as binding site reorganization, is still very
difficult. Two-dimensional replica-exchange approach 1 is promising in that it enhances the binding
events to predict the binding structure with high statistical accuracy. Here, we extend the methodby introducing the generalized replica-exchange with solute tempering (gREST) 2 . The protein
flexibility is incorporated by scaling the temperature of a wide solute region, involving the ligand
and the active site residues, using gREST. We applied the method to study the ligand binding of
Src kinase. We show that the native binding structure is correctly predicted. Importantly, theconsideration of protein flexibility significantly improves the docking efficiency compared to theexisting methods. We also stress that the method enhances the binding events to give highstatistical accuracy, while long-time simulation could sample the event once per several
microseconds. This approach, followed by binding free energy calculation, allows us to accurately
predict protein-ligand binding affinity.
[1] Kokubo, H. et al. “Two-dimensional replica-exchange method for predicting protein-ligand
binding structures.” J. Comput. Chem. (2013) 34:2601-2614.
[2] Kamiya, M. et al. in preparation.

P1-07
FMO calculations on specific interactions between
vitamin-D receptor and its ligands
Ryosuke Takeda 1 , Ittetsu Kobayashi 1 , Kanako Shimamura 1 , Hiromi Ishimura 1 ,Ryushi Kadoya 1 , Kentaro Kawai 2 , Atsushi Kittaka 3 , Midori Takimoto-Kamimura 4 ,Noriyuki Kurita 1
kurita@cs.tut.ac.jp

1 Department of Computer Science and Engineering, Toyohashi University of Technology,
Tempaku-cho, Toyohashi, Aichi, 441-8580, Japan
2 Drug Research Center, Kaken Pharmaceutical Co. Ltd.,14, Shinomiya, Minamigawara-cho, Yamashina-ku, Kyoto, 607-8042, Japan
3 Faculty of Pharmaceutical Sciences, Teikyo University, 2-11-1 Kaga, Itabashi, Tokyo 173-8605, Japan
4 Teijin Institute for Bio-Medical Research, Teijin Pharma Ltd.,
4-3-2 Asahigaoka, Hino, Tokyo, 191-8512, Japan

Keywords: Molecular simulation, Fragment molecular orbital , Vitamin D receptor, Inhibitor
Vitamin D 3 is hydroxylated at both the 25 and the 1α sites in a liver and a kidney, resulting in theactive form of vitamin D 3 metabolites 1α,25-dihydroxyvitamin D 3 (1α,25(OH) 2 D 3 ), which plays
important roles in the regulation of calcium and phosphorus metabolism as well as in the boneformation. The physiological actions caused by 1α,25(OH) 2 D 3 are triggered by its specificinteraction with vitamin D receptor (VDR)[1]. The previous study [2] confirmed that almost allcell tissues in a living organism have VDR and that the binding of 1α,25(OH) 2 D 3 to the VDR is
deeply related to the pathogenesis of the immunological diseases such as cancer as well as the
response anomaly to hormone. For elucidating the biological effect of VDR, it is indispensable todetermine the structures of VDR bound by vitamin D derivatives. Therefore, various types of
1α,25(OH) 2 D 3 derivatives were synthesized [3] and the structures of their complexes with human
VDR were determined to clarify that the derivatives form hydrogen bonds to Arg274 residue of
VDR and have high binding affinity for VDR. However, it is not elucidate the reason why a slightdifference in structure of the derivatives causes a large difference in binding affinity between VDR
and the derivatives. This fact might be a bottleneck f or proposing novel derivatives as a potent
modulator or inhibitor to VDR.
In the present study, we employed two types of 1α,25(OH) 2 D 3 derivatives, whose structures arealmost the same but their effect on VDR activity is significantly different, and their specificinteractions with VDR were investigated at an electronic level, using ab initio molecularsimulations based on fragment molecular orbital (FMO) method [4]. Based on the results of FMO
calculations, we elucidated which parts of the derivatives and which residues of VDR are
important for the specific binding between VDR and the derivatives [5].
[1] Darwish H., DeLuca H. F., “Vitamin D-regulated gene expression,” Crit. Rev. Eukaryot. Gene Expr.,
3: 89−116, 1993.
[2] Walters M. R., “Newly identified actions of the vitamin D endocrine system, ” Endocr. Rev., 13:
719−764, 1992.
[3] Matsuo M., Hasegawa A., Takano M., Saito H., Kakuda S., Takagi K., Ochiai E., Horie K.,
Takimoto-Kamimura M., Takenouchi K., Sawada D., Kittaka A., “Design and synthesis of
2α-(tetrazolylethyl)-1α,25-dihydroxyvitamin D 3 as a high affinity ligand for vitamin D receptor,” J.
of Steroid Biochem. & Mole. Biol., 144: 201−203, 2014.
[4] Kitaura K., Ikeo E., Asada T., Nakano T., Uebayashi M., “Fragment molecular orbital method: anapproximate computational method for large molecules,” Chem. Phys. Lett., 313: 701−706, 1999.
[5] Takeda R., Kobayashi I., Shimamura K., Ishimura H., Kadoya R., Kawai K., Kittaka A.,
Takimoto-Kamimura M., Kurita N., “Specific interactions between vitamin-D receptor and its
ligands: ab initio molecular orbital calculations in water”, J. of Steroid Biochem. & Mole. Biol.,
171: 75−79, 2017.

P1-08

Repetition of binding and unbinding processes between
protein and ligand by supervised molecular dynamics
Takashi Mitsui 1
tmitsui@jp.fujitsu.com

1

Bio-IT R&D Office, Research& Development Division, Healthcare Systems Unit, Fujitsu
Limited, 1-17-25 Shin-Kamata, Ota-ku, Tokyo 226-8504, Japan

Keywords: Molecular dynamics, Ligand binding

The mechanism of the binding/unbinding processes between protein and ligand is a crucial issue
in in-silico drug design. In recent published papers, unbiased long-time molecular dynamics (MD)simulations reproduced experimentally observed protein-ligand complex structures. However,ligand binding to its receptor in correct pose is rare event, therefore it requires microsecond
time-scale MD simulation.
Supervised molecular dynamics (SuMD) [1][2] is tabu-likecomputational algorithm developed to follow protein-ligand
approaching processes within a relatively short time-scalecompared to traditional MD simulation. In this study, we
extended the original protocol to an unbinding process toenable repetition of multiple binding/unbinding events.
A demonstrative simulation was carried out using the X-ray
co-crystal structure, 1QY2(PDB)[3], of major urinary protein(MUP) and its ligand, 2-isopropyl-3-methoxypyrazine(IPMP). The initial structure was prepared by taking theligand apart from the binding pocket of the protein. Multiple
SuMD simulations were performed and multiple
binding/unbinding events occurred in each trajectory.
Resultant complex structures were clustered into several
binding poses. They included a ligand binding pose observedin traditional MD simulations of the X-ray co-crystal
structure.
Figure 1. A typical trajectory ofligand binding process.

[1] D. Sabbadin, S. Moro, “Supervised molecular dynamics (SuMD) as a helpful tool to depict
GPCR-ligand recognition pathway in a nanosecond time scale ”, J. Chem. Inf. Model., 54, 372(2014)[2] A. Cuzzolin, et al., “Deciphering the complexity of ligand -protein recognition pathway using
supervised molecular dynamics (SuMD) simulations”, J. Chem. Inf. Model., 56, 687 (2016)[3] R. J. Bingham, et al., “Thermodynamics of binding of 2-methoxy-3-isopropylpyrazine and2-Methoxy-3-isobutylpyrazine to the major urinary protein ”, J. Am. Chem. Soc., 126, 1675
(2004).

P1-09In silico protein design for functional modification ofphotoactivated adenylate cyclase
Mayu Tanaka 1

Toru Ekimoto 1

w165425b@yokohama-cu.ac.jp

Sam-Yong Park 1

park@yokohama-cu.ac.jp

1

Mio Ohki 1

Tsutomu Yamane 1

Mitsunori Ikeguchi 1

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

Keywords: Photoactivated adenylate cyclase, Mutation analysis, Protein design
Photoactivated adenylate cyclase (PAC) is a photoreceptor protein that produces the second
messenger cyclic-AMP (cAMP) upon light illumination. PAC consists of the blue light using flavin(BLUF) and the adenylate cyclase (AC) domains. Recently, the crystal structure of Oscillatoria
acuminate photoactivated adenylate cyclase (OaPAC) has been solved [1].
Optogenetics is a rapidly growing field in which light is used to control biological systems.
OaPAC is expected as an optgenetic tool to produce cAMP upon blue-light illumination. In
addition to cAMP, the control of the cGMP level upon light illumination would be useful toinvestigate the effects of the cGMP on biological systems. Therefore, in this study, we attemptin-silico protein design to modify OaPAC to produce cGMP instead of cGMP.
In the crystal structure of OaPAC, ATP or ATP analog was not bound to the ATP binding site.
Therefore, in this study, the crystal structure of CyaC, a homolog of OaPAC, was used to design
the nucleotide-binding site, because ApCpp, an ATP analog, was bound to the crystal structure of
CyaC. First, ApCpp was replaced to GTP in the crystal structure of CyaC. Then, amino acid in thebinding site were comprehensively mutated, and the changes in GTP affinity upon mutations were
calculated. Also, the changes in stability of the protein upon mutations were calculated. Mutants
predicted to have high affinity of GTP forms hydrogen bonds to GTP and not to ATP. Interactions
with both two GTPs of the CyaC dimer were found to be important to raise affinity to GTP.
[1] Ohki, M. et al., Structural insight into photoactivation of an adenylate cyclase from a
photosynthetic cyanobacterium, PNAS, 113:6659-6664, 2016

P1-10
Validation of Epigenetic Therapeutic Target Proteinsfor Homogenous Assay Performance
Masato Yonezawa 1 , Mary Anne Jelinek 1 , Melissa Ritland 1 , Jake Dabrowski 1 , Wei Gong 1 ,Lingchun Kong 2 and Fei Lan 2
1

Active Motif Inc., Carlsbad CA, USA
Active Motif China, Shanghai, China
Email: yonezawa@activemotif.com
2

Keywords: Protein-small molecule interaction, Enzymatic reaction
The cancer genome atlas project (TCGA) revealed a great number of potential new therapeutictargets among epigenetic factors. To accelerate epigenetic drug discovery, we produced a protein
toolbox of reagents including active enzymes, recombinant substrates and detection antibodies,and set up homogeneous screening assay platforms for histone deacetylases (HDACs), histone
acetyltransferases (HATs), lysine methyltransferases (KMTs), lysine demethylases (KDMs), andbromodomain proteins (BRDs). Here, we present data obtained by AlphaLISA (amplified
luminescent proximity homogeneous assay) platforms for HDAC3, LSD1, p300, SETDB1, and
BRD family members. In general, IC50 values of these proteins are well within published results
for the various compounds. This protein toolbox continues to expand to include “designer” oligo
nucleosomes which include site directed installation of modified amino a cids. Additional
candidates can now be screened using these alternate nucleosome -based substrates. Thus, our assay
platforms will be very useful to screen and validate candidate epigenetic drugs as well as to study
properties of epigenetic factors.
[1] https://cancergenome.nih.gov/[2] www.activemotif.com/catalog/5[3] www.activemotif.com/catalog/744[4] www.activemotif.com/catalog/1068

P1-11

Comprehensive database analysis ofprotein kinase 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 Suehirocho,
Tsurumi-ku, Yokohama 230-0045, Japan

Keywords: Protein-small molecule interaction, Molecular dynamics method, Protein kinase,
KLIFS database, Structural bioinformatics
Protein kinases are ATP phosphotransferases targeting at Ser/Thr/Tyr residues of specificsubstrate proteins and then play an important role for signaling in cells. While some kinase
proteins involved in diseases have been extensively studied as drug-discovery targets, few ofstudies have been done to understand complete structural dynamics for overall protein kinases. A
protein kinase database, KLIFS [1], collects a variety of human/mouse protein kinases with various
kinase families and species, kinase activities, bound (drug) molecules, site-directed mutations, andso on, which however takes interest on local atom interactions between the proteins and ligands. In
the present study, by use of the KLIFS database, we have attempted comprehensive analyses ofprotein kinase collective motions in relation to the way of local protein-drug interactions.
Data collection for this study was carried out from 849 human tyrosine kinases in the KLIFS
database by excluding structures with a number of lacking atoms or with large similarities, leading
to 150 representative kinase structures. Motion Tree [2,3] calculated using 207 C atoms after
multiple alignment of the 150 structures demonstrated a significance of the domain motion
between kinase N-lobe and C-lobe, as well as the motions of both activation loop and C-helix
which have often been discussed in previous studies. Structural classifications were performed byprojecting of each protein kinase onto the three motions and by assigning the information on
kinase activities (DFG-in/C-helix-in, etc) and kinase species, which were then analyzed in relation
to the atom interactions with bound molecules such as ATP-analog and drugs, and in combination
with the structural dynamics data calculated from comprehensive molecular dynamics simulations.
These findings yielded a general picture of how protein kinases are inactivated due to what kindsof drugs on which binding pockets, which will be useful for designing new drug molecules for
specific protein kinases.
[1] A. J. Kooistra et al., “KLIFS: a structural kinase-ligand interaction database”, Nucleic Acids
Res 44: D365-D371 (2016).
[2] K Moritsugu, R Koike, K Yamada, H Kato, A Kidera, "Motion Tree Delineates Hierarchical
Structure of Protein Dynamics Observed in Molecular Dynamics Simulation", PLoS ONE 10:e0131583 (2015).
[3] R Koike, M Ota, A Kidera, "Hierarchical description and extensive classification of protein
structural changes by Motion Tree", Journal of Molecular Biology 426: 752-762 (2014).

P1-12Finite-size effect on the charging free energy in thealchemical perturbation and ``warp drive’’ method
Toru Ekimoto

Tsutomu Yamane

ekimoto@yokohama-cu.ac.jp

tyamane@yokohama-cu.ac.jp

Mitsunori Ikeguchi
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

Keywords: Binding free energy, Free energy perturbation, Charging free energy, Finite-size effectWith increase of computational power, calculations of the binding free energy by exact methods,
such as the alchemical free energy perturbation (alchemical-FEP), routinely become possible with
all-atom molecular dynamics (MD) simulations. In the alchemical-FEP, the bound ligand isthermodynamically eliminated from the binding site and is emerged to a bulk region. The
alchemical-FEP can treat buried ligands in deep pocket, however, accuracy for highly charged
ligands is poor. One of the reasons is due to the finite-size effect on the charging free energy (CFE)in periodic systems. The CFE is the free energy change for tuning on the electrostatic interactionof the solute with the solvent. The finite-size effect refers to the cell-size dependence of the CFE atdifferent cell sizes. [1] When the CFE is left uncorrected, a comparison of the CFE among
different charged states is erroneous as shown in the examinations for ions and proteins [1,2]. The
CFE is a key component of the binding free energy, t herefore, a correction scheme is necessary toobtain the CFE at the limit of large cell size.
In this study, we examine the finite-size effect on the CFE in the alchemical-FEP withsystematically varied cell sizes of the MD unit cell , assess the performance of the correction
scheme formulated by Hummer et al. [1], and introduce an alternative perturbation, termed “warpdrive method” [3], providing the CFE at the limit of large cell size without any corrections. The
phosphotyrosine peptide (-5e) bound to the Src homology 2 domain (+1e) is employed as a testcomplex, and the CFE is calculated by thermodynamic integration method. We show that thefinite-size effect arises at ~92 kcal/mol in the annihilation process and ~180 kcal/mol in theemerging process. The self-energy correction essentially corrects them within ~2 kcal/mol and ~3
kcal/mol, respectively, and the additional correction from the solvation effect reduces the remains
to ~2 kcal/mol and ~1 kcal/mol, respectively. This shows that the corrections scheme is absolutely
necessary to obtain the CFE at the limit of large cell size, however, its performance at the small
cell sizes is less effective in this examination. We also examine the warp drive method in which
both processes in the alchemical-FEP is simultaneously executed using a solution system
consisting of a protein-ligand complex and a distantly-positioned unbound-ligand in one MD unit
cell. The eliminated partial charges of the bound ligand simultaneously emerge on the other ligand
in bulk, and therefore, the total charge of the system does not change at all intermediate sates. The
CFE by this method does not show the finite-size effect even at small cell sizes, and the value
without any corrections is in good agreement with the summation of the corrected CFEs calculatedby the alchemical-FEP.
[1] Hummer, G., Pratt, L.R., Garcia, A.E., Journal of Physical Chemistry, 100: 1206-1215, 1996.
[2] Ekimoto, T., Matubayasi, N., and Ikeguchi, M., Journal of Chemical Theory and Computation,
11:215-223, 2015.
[3] Ekimoto, T., Yamane, T., and Ikeguchi, M., in preparation.

P1-13
Molecular dynamics and ab initio FMO calculations foramyloid-β nonamer
Shogo Tomioka 1 , Haruki Sogawa 1 , Hiromi Ishimura 1 , Akisumi Okamoto 1 ,Sergiy Shulga 2 , Pavel Karpov 2 , Yaroslav Blume 2 , Noriyuki Kurita 1
kurita@cs.tut.ac.jp

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

Keywords: Molecular simulation, fragment molecular orbital, molecular dynamics, Alzheimer’s
disease, amyloid-beta, aggregate
The accumulation of amyloid-β (Aβ) oligomers and fibrils in a brain has been recognized to be amajor cause of the onset of Alzheimer’s disease (AD). It is thus expected that the inhibition of Aβ
aggregations can prevent the onset of AD, and many kinds of agents with strong binding affinity toAβ have been developed. In the design of these potent inhibitors against the Aβ aggregation, it isnecessary to make clear the structures of the Aβ oligomers and fibrils as well as the mechanism of
the aggregation. Recently, Lu et al.[1] revealed structural models for the in vivo Aβ nonamer,
based on the solid-state nuclear magnetic resonance analyses for the Aβ fibrils derived from thebrains of two different AD patients. Each of these Aβ nonamer models has a single andpatient-specific structure possessing three-fold symmetry with respect to the axis of fibril growth .
However, it is not elucidated why such high symmetry structure of Aβ fibrils can be stabilized.
Molecular simulations such as molecular mechanics (MM) and molecular dynamics (MD) o nes
for Aβ fibrils are efficient for investigating their stable structures and the mechanism of
aggregation at an atomic level. Kahler et al.[2] conducted a systematic computational study basedon all-atom classical MD simulations for many types of fibrill ary Aβ oligomers and concluded thatthe pairs of Aβ protofilaments are important as a seed for forming Aβ fibrils. However, they
considered only Aβ(9-42) fragment, missing the residues of N-terminal region, which were foundto be important for Aβ aggregation in the previous experiment.
In our previous studies [3,4], replica-exchange MD (RE-MD) simulations were conducted inwater for monomer and dimer models of Aβ(1-42) peptides, in order to search widely for theirstable conformations in water. In addition, we carried out ab initio fragment molecular orbital(FMO) calculations for the conformations obtained by the RE -MD simulations and determined thestable conformations of the solvated Aβ(1-42) monomer and dimer, with considering watermolecules explicitly. We furthermore investigated the specific interactions between Aβ peptides in
the Aβ hexamers at an electronic level, using ab initio FMO method [5]. Our ab initio simulations
elucidated the importance of structural water molecules for the stabilization of Aβ fibrils.
In the present study, to clarify the reason why the Aβ nonamer with three -fold symmetry
obtained by Lu et al.[1] is stable, we investigated the change in structure of the Aβ nonamer by useof classical MD simulations in water. In addition, ab initio FMO calculations were carried out forsome snapshots obtained by the MD simulations. The results revealed that the interactions between
the Aβ peptides of stacked Aβ pairs make resultant contribution in stability of the Aβ nonamer.
[1] Lu J.X., Qiang W., Yau W.M., Schwieters C.D., Meredith S.C., Tycko R., Cell 154 (2013) 1257–1268.
[2] Kahler A., Sticht H., Horn A.H.C., Plos One 8 (2013) e70521.
[3] Yano A., Okamoto A., Nomura K., Higai S., Kurita N., Chem. Phys. Lett. 595/596 (2014) 242–249.
[4] Okamoto A, Nomura K., Yano A., Higai S., Kondo T., Kamba S., Kurita N., Chem. Phys. Lett. 577(2013) 131–137.
[5] Ishimura H., Tomioka S., Kadoya R., Simamura K., Okamoto A., Shulga S., Kurita, N.,
Chem. Phys. Lett. 672 (2017) 13–20.

P1-14
Specific interactions between mycobacterial FtsZ andcurcumin derivatives: molecular docking and ab initio
molecular simulations
Mitsuki Fujimori 1 , Haruki Sogawa 1 , Shintaro Ota 1 , Pavel Karpov 2 , Sergiy Shulga 2 ,Yaroslav Blume 2 , Noriyuki Kurita 1
kurita@cs.tut.ac.jp

1 Department of Computer Science and Engineering, Toyohashi University of Technology,
1-1 Hibarigaoka, Tenpaku-cho, Toyohashi, Aichi, 411-8580, Japan
2 Institute for Food Biotechnology and Genomics, National Academy of Scienc es of Ukraine,
2a. Osypovskogo Str., Kyiv-123, 04123, Ukraine

Keywords: Curcumin; Inhibitor; FtsZ; Tuberculosis; Cell division; Fragment molecular orbital;
Protein-ligand docking
Tuberculosis (TB) is one of the most widespread infectious diseases caused by the bacillus
Mycobacterium tuberculosis (Mtb). In the treatment of TB, many kinds of drugs such as isoniazid,rifampicin, pyrazinamide and ethambutol have been administered. However, there is a considerable
potential for Mtb to have resistance against these drugs. In particular, the Mtb having resistanceagainst multiple drugs is called multidrug-resistant TB (MDR-TB), and the number of MDR-TBs is
increasing rapidly. It is thus necessary to develop new anti-TB drugs targeting the mostconservative proteins, which cannot be mutated easily [1]. As a candidate of anti-TB drugs, newcompounds were proposed, which suppress the growth of Mtb by inhibiting the division of Mtb cell[2]. These drugs are targeted to cytoskeletal protein FtsZ (filamenting temperature-sensitive
mutant Z), which plays an essential role in the cell division mechanism [3]. The inhibitor against
FtsZ function is expected to suppress the Mtb cell growth.
As a novel inhibitor against Mtb FtsZ, we here considered curcumin derivatives. Curcumin is anatural product and contained in the root of Curcumae Rhizoma, while other curcuminoids such as
demethoxycurcumin and bisdemethoxycurcumin are also included in the root [4]. These curcumin
derivatives have been widely used [4] as conventional drugs for treating many diseases. It was
found that curcumin suppresses bacterial cell proliferation by inhibiting the FtsZ function [ 5].
However, since there are some ligand-binding pockets in Mtb FtsZ, the binding site of curcumin on
FtsZ and the specific interactions between curcumin and FtsZ are not elucidated yet.
In the present study, we investigated the specific interactions between Mtb FtsZ and somecurcumin derivatives, using ab initio molecular simulations based on protein-ligand docking,classical molecular mechanics optimization and ab initio fragment molecular orbital (FMO)
calculation. Based on the FMO results, we attempted to reveal which curcumin derivative can bindmore strongly to FtsZ. In addition, we elucidated which parts of FtsZ and curcumin derivative areimportant for the specific interactions between them. The result will be useful for proposing novelanti-TB drugs based on curcumin derivatives.
[1] H. Tomioka, Kekkaku, 85: 815–822, 2010.
[2] P.A.J de Boer, et al., Cell, 56: 641–649, 1989.
[3] M. Osawa, et al., Cell, 56: 641–649, 1989.
[4] F. Kiuchi, et al., Chem. Pham. Bull, 41: 1640–1643, 1993.
[5] D. Rai, et al, Biochem. J., 410: 147–155, 2008.

P1-15QCLObot: an automation engine of canonical MO
calculation in proteins
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, electronic structure calculation, proteins
The initial guess of the SCF calculation is one of the most important factors to successfully achievecanonical molecular orbital (CMO) calculation. In order to get a precise initial guess for large
systems, the QCLO method [1, 2] has demonstrated some great performances [3-5]. The QCLO is akind of localized orbital, which is not only localized in a certain region of the molecule but also
canonical in that region. Gathering of the subsystem QCLOs, we can obtain the precise initial guessof the super-system. Considering the protein structure, such as secondary structure and ion pairs,
we can get more precise initial guess which is almost equal to the SCF convergence [2]. However,the procedure of computing and gathering QCLOs is so complicated and difficult.
We have developed an automation program, QCLObot [6]. The QCLObot is inspired by an opensource automation provisioning program, Ansible [7]. Both of the applications are written by the
Python, and the format of input file is based on the YAML human-readable serialization data format[8]. The QCLObot playbook is also idempotent, in order to prevent redundant calculations andunexpected side-effects on the QCLO method. The QCLObot has also used template engine basedon Jinja2 [9], so using variables and dynamic expression are enabled in the playbook.
The fragments, which are components of the computing molecule named as the frame molecule,are encouraged to define by the hierarchic structure of the YAML format. The corresponding QCLOs
of the fragments are automatically formed the basis for the YAML playbook. Therefore, not only
contiguous amino-acid residues, but also spatially-designated chemical components can be treatedas a QCLO fragment. Heteroatoms, such as coenzyme and prosthetic group, can be also easily
computed based on the QCLO method by using the QCLObot program.
We have performed some CMO calculations of proteins by using the QCLObot program. Recently,
the simple geometrical optimization method (steepest descent and conjugate gradient method) basedon the QCLO method has been implemented. Computational efficiency strategy by using the
combination of corresponding orbitals in the previous optimization step is under consideration. And,in order to provide an easy way to make a calculation model, an elementary modeling system forproteins has been developed. In this presentation, we report these new features and some simulation
results using the QCLObot application.
[1] H. Kashiwagi, H. Iwai, K. Tokieda, M. Era, T. Sumita, T. Yoshihiro, F. Sato, Mol. Phys., 101:8186, 2003.
[2] N. Nishino-Uemura, T. Hirano, F. Sato, J. Chem. Phys., 127:184106, 2007.
[3] K. Chiba, T. Hirano, F. Sato, M. Okamoto, Quant. Chem., 113: 2345-2354, 2013.
[4] Y. Yagi, Y. Naoshima, Trans. J. Soc. Sim. Tech., 4:127-136, 2012.
[5] T. Inaba, N. Tsunekawa, T. Hirano, T. Yoshihiro, H. Kashiwagi, F. Sato, Chem. Phys. Lett.,
434:331-335, 2007.
[6] https://github.com/ProteinDF/QCLObot[7] https://www.ansible.com[8] http://yaml.org[9] http://jinja.pocoo.org

P1-16
FMO calculations on specific interactions between
androgen receptor and non-steroid agents
Ittetsu Kobayashi 1 , Ryosuke Takeda 1 , Rie Suzuki 1 , Kanako Shimamura 1 , Hiromi Ishimura 1 ,Ryushi Kadoya 1 , Kentaro Kawai 2 , Midori Takimoto-Kamimura 3 ,Noriyuki Kurita 1
kurita@cs.tut.ac.jp
12

3

Department of Computer Science and Engineering, Toyohashi University of Technology,
Tempaku-cho, Toyohashi, Aichi, 441-8580, Japan
Drug Research Center, Kaken Pharmaceutical Co. Ltd.,14, Shinomiya, Minamigawara-cho, Yamashina-ku, Kyoto, 607-8042, Japan
Teijin Institute for Bio-Medical Research, Teijin Pharma Ltd.,
4-3-2 Asahigaoka, Hino, Tokyo, 191-8512, Japan

Keywords: Molecular simulation, Fragment molecular orbital, Androgen receptor,
Non-steroid agent
Androgen receptor (AR) is a family of nuclear receptor proteins and a ligand-activatedtranscription factor [1]. The bindings of ligand as well as coactivator to AR are considered to be a
trigger for the onset of the progression mechanism of prostate cancer. Accordingly, many types ofantagonists against AR have been developed as promising agents for treating prostate cancers.
Among these agents, bicalutamide [2] with a non-steroid skeleton compound has been widely used.
However, since some mutant-type ARs with strong drug-resistance have appeared, it is essential todevelop novel agents alternative to bicalutamide.
In the present study, we investigated the specific interactions between AR and several types of
non-steroid agents at an electronic level, using ab initio molecular simulations. We first obtained
the stable structures of AR+ligand complexes in water by classical molecular mechanics (MM)calculations, and the electronic properties of the stable structures were investigated by the ab initio
fragment molecular orbital (FMO) method [3]. The interaction energies between AR and each of
the ligands evaluated by FMO were confirmed to explain the trend of the observed binding affinity
between AR and the ligands. It was highlighted that Asn705 and Arg752 of AR contribute main ly
to the binding between AR and the ligands.
In addition, we proposed some novel agents promising as potent ligands against AR andinvestigated the binding properties between AR and these agents [4]. The ab initio molecularsimulations indicate that some of our proposed agents can bind more strongly with AR than theexisting ligands and be a potent ligand against AR.
[1] S. Grosdidier, L.R. Carbó, V. Buzón, G. Brooke, P. Nguyen, J.D. Baxter, C. Bevan, P. Webb, E.
Estébanez-Perpiná, J. Fernández-Recio, Allosteric conversation in the androgen receptor
ligand-binding domain surfaces, Mol. Endocrinol., 26:1078–1090, 2012.
[2] C.E. Bohl, Z. Wu, J. Chen, M.L. Mohler, J. Yang, D.J. Hwang, S. Mustafa, D.D. Miller, C.E.
Bell, J.T. Daltona, Effect of B-ring substitution pattern on binding mode of propionamide
selective androgen receptor modulators, Bioorg. Med. Chem. Lett., 18:5567–5570, 2008.
[3] K. Kitaura, E. Ikeo, T. Asada, T. Nakano, M. Uebayashi, Fragment molecular orbital method:
an approximate computational method for large molecules, Chem. Phys. Lett., 313:701–706,
1999.
[4] I. Kobayashi, R. Takeda, R. Suzuki, K. Shimamura, H. Ishimura, R. Kadoya, K. Kawai, M.
Takimoto-Kamimura, N. Kurita, Specific interactions between androgen receptor and its
ligand: ab initio molecular orbital calculations in water, J. of Mole. Graph. & Model.,75:383-389, 2017.

P1-17
Binding properties between curcumin and malarial
tubulin: molecular-docking and ab initio FMO
calculations
Shintaro Ota 1 , Mitsuki Fujimori 1 , Sergiy Shulga 2 , Noriyuki Kurita 1
kurita@cs.tut.ac.jp
12

Department of Computer Science and Engineering, Toyohashi University of Technology,
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: Fragment molecular orbital, Protein and ligand docking, in silico drug design
Malaria is caused by the protozoa of the genus Plasmodium. Because of its prevalence, virulence,and drug resistance, it is the most serious and widespr ead parasitic disease. In spite of decades ofintense researches, malaria remains a lethal disease in the world. Therefore it is urgently required
to develop inexpensive and effective anti-malarial drugs.
Curcumin, a polyphenolic organic molecule derived from turmeric, has been used widely as a
traditional medicine [1]. It was found that curcumin is effective against malaria parasites, and thepossibility that curcumin can be used a novel therapeutic agent against malaria was recognized [2].
Curcumin is also known to have inhibitory effect on mammalian tubulin, a protein that isessential for cell division and cellular trafficking. It was found that curcumin can bind strongly totubulin in vitro to inhibit polymer assembly and induce tubulin dimer aggregation, resulting in
depolymerization of mitotic microtubules during cell division [3].
Recent immunofluorescence as well as molecular docking studies for the human cancer cell lines
suggested that curcumin might bind at the interface of alpha -beta tubulin heterodimer leading toaltered microtubule morphology [4]. This binding site was confirmed to be similar to that of the
microtubule destabilizing drug colchicine. Therefore, curcumin is expected to have a similar effecton tubulin as the existing drug colchicine. However, it is not elucidated why curcumin binds to thecolchicine site and not to the other ligand-binding sites of tubulin.
In the present study, we searched widely the binding site of curcumin and its derivatives on thealpha- and beta-tubulin monomers, using ab initio molecular simulations based on fragment
molecular orbital (FMO) method. In addition, the same simulations were conducted for the existing
drugs (colchicine and vinblastine), and the results for these different types of drugs were compared.
The results obtained by our simulations will contribute to the development of new therapeutic
agents for malaria.
[1] R. K. Maheshwari, A. K. Singh, J. Gaddipati, R. C. Srimal, Multiple biological activities of
curcumin: A short review, Life Sciences, 78:2081-2087, 2006.
[2] R. C. Reddy, P. G. Vatsala, V. G. Keshamouni, G. Padmanaban, P. N. Rangarajan, Curcumin formalaria therapy, Biochemical and Biophysical Research Communications, 326:472-474, 2005.
[3] K. K. Gupta, S. S. Bharne, K. Rathinasamy, N. R. Naik, D. Panda, Dietary antioxidant
curcumin inhibits microtubule assembly through tubulin binding, The FEBS Journal, 273:
5320-5332, 2006.
[4] R. Chakrabarti, P. S. Rawat, B. M. Cooke, R. L. Copeel, S. Patankar, Cellular Effects of
Curcumin on Plasmodium falciparum Include Disruption of Microtubules, PLOS ONE, 8(3),2013.

P1-18A soft-core potential suitable to a special purpose computerfor molecular dynamics

1

Teruhisa S. Komatsu 1

Makoto Taiji1

teruhisa.komatsu@riken.jp

taiji@riken.jp

Research and Development Project of Special Purpose Supercomputers for Drug Design,
Quantitative Biology Center, RIKEN,
2F QBiC Building B, 6-2-4 Furuedai, Suita Osaka 565-0874, Japan

Keywords: molecular dynamics simulation, soft-core potential, special purpose computer
We are developing a special purpose computer for classical molecular dynamics, MDGRAPE-4A, aimproved version of MDGRAPE-4 [1], which aimes a long time molecular dynamics simulation with the
speed of 10 microseconds per day. One of the main application of its ability to perform a long time
simulation is expected to accelerate a drug design process. The system is designed to perform simple
molecular dynamics simulation faster as possible. Although the basic software interface is developed basedon GROMACS [2], most of the molecular dynamics code for our special purpose computer should be
rewritten from a scratch and thus only a limited number of the specific schemes available in GROMACS
will be implemented from the viewpoints of computational speed and software developmental cost.
Soft-core potential is a common scheme to treat particle creation/annihilation process often used indouble-decoupling/alchemical binding free energy calculations [3]. The full calculation of these schemes
require massively parallel computation for many lambda states and is not considered as the task
expected/suitable for MDGRAPE-4A. But long time simulation for a lambda state may have a merit. We
are planing to implement (without loss of computational speed) soft-core potential in a limited way, mainly
usable for double-decoupling scheme. The soft-core potential form implemented in MDGRAPE-4A is
chosen as a suitable form to special purpose computer, more concretely, the force exerted by the new
soft-core potential inside the softening region (r<rc) is simply proportional to the particle distance r,f∝rwhich is different from that [4] used in GROMACS and also from recently proposed one [5]. In order toconfirm that our new soft-core potential form performs well, we simulate simple test systems and compare
the results.
[1] I. Ohmura, G. Morimoto, Y. Ohno, A. Hasegawa, M. Taiji, MDGRAPE-4: a special-purpose computer
system for molecular dynamics simulations, Phil. Trans. R. Soc. A 372 (2014) 20130387.
[2] M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, E. Lindahl, GROMACS: Highperformance molecular simulations through multi-level parallelism from laptops to supercomputers.
SoftwareX 1 (2015) 19-25.
[3] Y. Deng, B. Roux, Calculation of standard binding free energies: aromatic molecules in the T4
Lysozyme L99A mutant. JCTC 2 (2006) 1255-1273.
H. Fujitani, Y. Tanida, A. Matsuura, Massively parallel computation of absolute binding free energy withwell-equibrated states, Phys. Rev. E79 (2009) 021914.
[4] T. C. Beutler, A. E. Mark,R. C. van Schaik, P. R. Gerber, W. F. van Gunsteren, Avoiding singularities
and numerical instabilities in free energy calculations based on molecular simulations, Chem. Phys. Lett.
222 (1994) 529-539.
[5] V. Gapsys, D. Seeliger, B. L. de Groot, New soft-core potential function for molecular dynamics based
alchemical free energy calculations, J.C.T.C. 8 (2012) 2373-2382.

P1-19Free-energy analysis of the urea and alkylurea
effects on the structure of protein
Yu Yamamori
yamamori@cheng.es.osaka-u.ac.jp

Nobuyuki Matubayasi
nobuyuki@cheng.es.osaka-u.ac.jp

Division of chemical Engineering, Graduate School of Engineering Science, Osaka
University, Toyonaka, Osaka 560-8531, Japan

Keywords: protein denaturation, cosolvent effect, free-energy calculation, molecular dynamics

Solute-solvent interactionenergy component (kcal/mol)Themechanismofurea-induced
denaturation of a protein has been discussed
100
for a long time. The major controversy is0which interaction component is responsible
electrostaticfor denaturation: electrostatic, van der Waals
excluded volume-100(dispersion)or
excluded-volume
effect(cavitation). In this study, the transfer-200
energetics
ofcytochrome
c
[1]
and
-300
total
T4-lysozyme [2] from pure-water solvent tocosolvent-water mixed solvent was analyzed.
-400
van der Waals
For cytochrome c , urea is adopted and forT4-lysozyme,
urea,
methylurea,
-500
-360
-320
-280
-240
1,1-dimethylurea and isopropylurea wasTransfer free energy (kcal/mol)
adopted as cosolvent. A variety of protein
structures from near-native to partiallyF i g u r e Transfer free energy against
unfolded ones were generated by molecular
the solute-solvent interaction energy
dynamics simulation in each mixed solvent.
(black), the electrostatic component
Each structure in this set was subject to the(green), the van der Waals component
calculation of the solvation free energy in(red),
andtheexcluded-volume
pure-water solvent and cosolvent-watercomponent (blue). 50 structures in total
mixed solvent . The energy-representation
were subject to the free energy
calculations.
method was employed to compute thesolvation free energy. The correlations of thetransfer free energy of cytochrome c from pure-water solvent to urea-water mixed
solvent against the transfer values of the average of solute-solvent interaction and its
components are shown in the figure. The strong correlation is found against the van der
Waals component, while the correlations are weak for the electrostatic andexcluded-volume components. Thus the van der Waals interaction is the dominantcomponent among the three. The transfer free energy is further decomposed into theurea and water contributions; the former is referred as the “direct mechanism” and thelatter as the “indirect mechanism”. A similar analysis of correlation supports the“direct mechanism”. The direct mechanism is also supported in the cases ofdenaturation of T4-lysozyme caused by alkylurea.
[1]
Yamamori, Y.; Ishizuka, R.; Karino, Y.; Sakuraba, S.; Matubayasi, N. J. Chem. Phys. 2016,
144, 085102.
[2]
Yamamori, Y.; Matubayasi, N. J. Chem. Phys. 2017, 146, 225123.

P1-20Efficient calculation of electrostatic potential of
biomolecule based on fragment molecular orbital method
Takeshi Ishikawa 1
t-ishi@nagasaki-u.ac.jp
1

Department of Molecular Microbiology and Immunology, Graduate School of Biomedical Science,
Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan

Keywords: electrostatic potential, fragment molecular orbital method, point-charge approximation
Accurate calculation of electrostatic potential (ESP) is one of the important issues in thecomputational chemistry and biology. For example, ESP at the molecular surface plays a key role in theprotein-protein or protein-ligand interaction, and is also responsible for the catal ytic activity of many
enzymes. In this study, efficient quantum chemical calculations of the ESP were performed based on
fragment molecular orbital (FMO) method [1] . In FMO method, ESP at a position r m is defined as thefollowing equation:(1)

where I and J are indices of the fragments, and α is index of the atom. The φ I is a direct contribution
from the monomer, and Δφ IJ is a two-body correction from the dimer. They are calculated as( 2)
where μ and ν are indices of the basis functions. The matrix D I is the density matrix of the monomer,
ΔD IJ is the difference density matrix [2] , and u μν is a one-electron integral like a nuclear attraction
integral. The numerical errors of the ESP associated with FMO scheme were examined at HF, MP2, and
RI-MP2 [3,4] levels of theory. As a result, the FMO errors in ESP were significantly smaller than theamplitude of the electron correlation effect, indicating that the FMO method provides sufficiently
accurate electrostatic properties for chemical and biological researches.
Additionally, an attempt to reduce the computational effort was proposed by combining the FMO
scheme and a point-charge approximation. When the distance between a position r m and monomer or
dimer is sufficiently large, the monomer contribution and two-body correction in equations (2) areevaluated using a point-charge approximation as the following equations:(3)

where q α I and Δq α IJ are a monomer contribution and a two-body correction to the atomic point-chargeof the electron, respectively. The error of this approximation was examined using two proteins, prion
protein and human immunodeficiency type 1 protease. Finally, as an illustrative example, ESP at themolecular surface of these proteins at MP2 level of theory were performed. In this study, PAICS
program package [5] was used for all the calculations.
[1] K. Kitaura, et al, Chem. Phys. Lett., 313 (1999) 701[2] T. Nakano, et al, Chem. Phys. Lett., 351 (2002) 475[3] T. Ishikawa and K. Kuwata, Chem. Phys. Lett., 474 (2009) 195[4] T. Ishikawa and K. Kuwata, J. Phys. Chem. Lett., 3 (2012) 375[5] T. Ishikawa, et al, J. Comput. Chem., 30 (2009) 2594

P1-21

Mechanism analysis of estrogen receptor beta selective
agonist by molecular dynamics and fragment molecularorbital method
Kensuke Misawa 1
misawa.kensuke@kao.co.jp

Yoshiya Sugai 1
sugai.yoshiya@kao.co.jp

Tadahiro Ozawa 1
ozawa.tadahiro@kao.co.jp

Taketoshi Fujimori 1
fujimori.taketoshi@kao.co.jp

Takatsugu Hirokawa 2,3
t-hirokawa@aist.go.jp

1

2

3

Biological Science Laboratories, Kao Corporation, 2606 Akabane, Ichikai -machi, Haga-gun,Tochigi 321-3497, Japan
Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), 2 -4-7 Aomi, Koto-ku, Tokyo 135-0064,
Japan
Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1 -1-1 Tennodai,
Tsukuba, Ibaraki 305-8575, Japan

Keywords: Estrogen receptor, Ligand docking, Molecular dynamics, Fragment molecular orbital
Estrogen receptors (ERs) are nuclear transcription factors that play an important role in theregulation of various physiological processes in humans and located in the various tissues such asbreast, bone and skin [1]-[2] . There are two major subtypes of ERs: estrogen receptor alpha (ERα)
and estrogen receptor beta (ERβ) [1] . Estradiol binds both the ERs equally, but this often leads to anincreased risk of breast and endometrial cancers because of ERα agonism [2]-[3] . Therefore, manyERβ selective agonists have been developed so far [4] .
We focused on ERβ selective agonist meso-2,3-bis(4-hydroxyphenyl)succinonitrile [5] andanalysed the mechanism of ERβ selectivity by the combination of induced-fit docking, molecular
dynamics and fragment molecular orbital. The details will be argued on the day.
[1] Paterni, I., Granchi, C., Katzenellenbogen, J. A., and Minutolo, F., Estrogen receptors alpha(ERα) and beta (ERβ): Subtype-selective ligands and clinical potential, Steroids, 90:13-29,
2014.
[2] Eyster, K. M., The Estrogen Receptors: Methods and Protocols, Humana Press, 2016.
[3] Jordan V. C., The secrets of selective estrogen receptor modulation: cell -specific coregulation,
Cancer Cell, 1:215-217, 2002.
[4] Mohler, M. L. et al., Estrogen receptor β selective nonsteroidal estrogens: seeking clinicalindications, Expert Opinion on Therapeutic Patents, 20:507-534, 2010.
[5] Meyers, M. J. et al., Estrogen Receptor-β Potency-Selective Ligands: Structure-Activity
Relationship Studies of Diarylpropionitriles and Their Acetylene and Polar Analogues , J. Med.
Chem., 44:4230-4251, 2001.

P1-22

Study of BACE1/2 selectivity mechanism for highly
selective inhibitor using metadynamics
Naoya Asada 1
naoya.asada@shionogi.co.jp

Kazunari Hattori 1
kazunari.hattori@shionogi.co.jp

Ken-ichi Kusakabe 1
ken-ichi.kusakabe@shionogi.co.jp

1

Shionogi Pharmaceutical Research Center, SHIONOGI & CO., LTD., 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan

Keywords: Selectivity, Molecular Dynamics, Metadynamics, Mutation analysis
BACE1 (beta-site APP cleaving enzyme 1) is one of the aspartic proteases and protein targets for
Alzheimer’s disease. Selectivity against BACE2 is desired for BACE1 inhibitor to avoid potential
side effects. Highly selective inhibitor of BACE1 was reported [1], however the mechanism of
selectivity has been unknown. We performed metadynamics simulation using Desmond [2] for the
elucidation of the selectivity mechanism of this compound.
It was noted that a shift of flap loop region, which is a part of the binding pocket, to a more open
orientation was occurred when the selective inhibitor bound [1] and comparison of X-ray crystal
structures between BACE1 and BACE2 revealed that the flap orientation was different between
them. Based on this information, we hypothesized that flap motion got involved in the selectivity.
To validate this hypothesis, metadynamics simulation was performed with the setting of collective
variables as the distance between catalytic region and the flap for the calculation of Free Energy
Surface (FES). As a result, it was observed that FES was different between two proteins. In
BACE1, free energy kept stable from close to open orientation of the flap, on the other hand, freeenergy became high when the flap was open in BACE2. This result showed that BACE2 was moreunstable than BACE1 when the selective inhibitor bound with the flap opening and this could be
the main factor for BACE1/2 selectivity. We also performed protein mutation analysis in virtual
model for identifying amino acid residues responsible for the difference of FES. On the flap region,
four residues are different between BACE1 and BACE2 (BACE1/BACE2: Y68/T84, P70/K86,
K75/S91, E77/T93). We made four BACE2 models which mutated one of the four residues to that
correspond to BACE1 and FES was calculated using metadynamics simulation for each model.
Finally, we found that FES of two mutation models (T84Y and S91K) showed similar results withthose of BACE1, which indicated that these two residues might contribute to the difference of theflap motion.
[1] Michael S. Malamas, Keith Barnes, Matthew Johnson, Yu Hui, Ping Zhou, Jim Turner, Yun Huc,
Erik Wagner, Kristi Fan, Rajiv Chopra, Andrea Olland, Jonathan Bard, Men elas Pangalos, Peter
Reinhart, Albert J. Robichaud, Bioorg. Med. Chem. 2010, 18, 630–639.
[2] Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2016.
Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2016.

P1-23Comparing two molecular dynamics simulation
trajectories in terms of residue-residue interaction
Chie Motono

c.motono@aist.go.jp

Takatsugu Hirokawa

t-hirokawa@aist.go.jp

Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064,
Japan

Keywords: molecular dynamics simulation, Protein, Mutation, residue-residue interaction.

Molecular Dynamics (MD) simulations provide hi-resolution information on biomolecular
dynamics. It has been used to elucidate the effects of mutations to a protein molecule, orbinding/dissociation of chemicals to biomolecules on the motion and function of the molecules at a
atomic level.
It is non-trivial a task to compare the two MD trajectories with different conditions (ex. with orwithout mutations, binding other molecules) and to detect the fundamental differences. As initialand intuitive analyses, time evolutions of root mean square deviations (RMSD), root mean squarefluctuations (RMSF) of each position, and average structures of each trajectory are often compared.
To observe statistical differences in molecular motions, Principal Component Analysis (PCA) isused. 1 It was proposed to use a more sophisticated technique, Partial Least Square for Discriminant
Analysis (PLS-DA) for comparison of two MD trajectories. 2 These statistical methods suffer fromthe anisotropy of the molecular structures. Linear Discriminant Analysis with ITERative procedure(LDA-ITER) is an excellent method which maximizes the ratio of the between-ensemble
fluctuation compared to the within-ensemble fluctuation to diminish the anisotropy effect. 3
In this study, we propose a new approach to compare the fluctuations of residue-residue
interactions of two MD trajectories. Identification of the positions where the dynamics ofresidue-residue interactions differs between two trajectories leads to the determination of the causeof different molecular motions or functions. This method indexes the residues with which a residue
interacts and the durability of the interactions within a trajectory, and then compare the index withthat in another trajectory to give a similarity. We applied the procedure to compare the trajectories
of a wild-type protein and its mutant, and identify the most affected positions. This method is
versatile and applicable to any different conditions like mutations, binding of other molecules, or
unfolding, etc.
[1] Kitao, A. and Go,N., Investigating protein dynamics in collective coordinate space, Current
Opinion in Structural Biology, 9:164-169, 1999.
[2] Peters, J. H. and de Groot, B. L., Investigating protein dynamics in collective coordinate space,
PLoS Comput. Biol., 8: e1002704, 2012.
[3] Sakuraba, S. and Kono, H., Spotting the difference in molecular dynamics simulations of
biomolecules, J. Chem. Phys., 145074:116, 2016.

P1-24

An integrated workflow for accurately predicting standard
free energy of binding
Yoshiaki Tanida

tanida.yoshiaki@jp.fujitsu.com

Azuma Matsuura
matsuura.azuma@jp.fujitsu.com

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

Keywords: standard free energy, alchemical free energy calculation, metadynamics, cluster
analysis, reweighting

To realize computational “de novo” drug design (e.g. w/o structural information inligand-receptor complex), we propose a new strategy of binding affinity evaluation and binding
structure search in combination.
(i)
(ii)
(iii)

Well-tempered metadynamics is used for exploring the available binding structures of new
drug-like molecule to a target in a suitable few collective variables space.
Some “pose” of the ligand providing local free energy minima is obtained by cluster
analysis and reweighting.
“Local” standard free energies can be estimated by alchemical free energy calculations;the standard free energy ligand binding is obtained.

We also introduce the application of this procedure in some typical case. We believe that our
workflow described here will become a promising way in rational drug discovery.

P1-25

An efficient approach for finding fragment-binding
conformations
Hiroyuki Sato 1
shryk@jp.fujitsu.com

Yoshiaki Tanida 1
tanida.yoshiaki@jp.fujitsu.com

Azuma Matsuura 1
matsuura.azuma@jp.fujitsu.com

1

Fujitsu Laboratories Ltd., 10-1 Morinosato-Wakamiya, Atsugi 243-0197, Japan

Keywords: Protein pocket, Fragment molecule, Binding conformation, Non-bonding potential
We present a novel and efficient approach for finding various fragment-binding conformations to
a protein using molecular dynamics (MD) simulation. In general, a long-time MD simulation isrequired to get possible binding variations in the conventional approaches[1]. Our approach adopts
randomly exchangeable protein replicas with high concentration of fragment molecules whose
non-bonding potential functions are different from each other. We applied this approach to humancoagulation factor Xa[2] with 4 M concentration of 6-chloro-1-benzothiophene-2-ol, and foundthat fragment molecules bound to protein pocket more than 60 times during a 10-ns MD simulation.
This indicates that our approach provides a sufficient number of fragment-binding conformations
through a short-time MD simulation.
[1] Guvench, O., et al., PLoS Comput. Biol. 2009, 5, e1000435.
[2] Maignan, S., et al., J. Med. Chem. 2003, 46, 685-690.

P1-26

Computational Study on Structure-Activity Relationshipin FAS Inhibitors Based on Three-Dimensional Electronic
Similarity
Takafumi Inoue 1
175d8303@st.kumamoto-u.ac.jp

Toshihiro Ideo 1

152d9201@st.kumamoto-u.ac.jp

Manabu Sugimoto 1,2,3

sugimoto@kumamoto-u.ac.jp

1

Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami,
Chuo-ku, Kumamoto 860-8555, Japan

2

Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami,
Chuo-ku, Kumamoto 860-8555, Japan

3

Research Center for Advanced Science and Technology (RCAST), University of Tokyo, 4-6-1
Komaba, Meguro-ku, Tokyo, Japan

Keywords: Structure-activity relationship, FAS Inhibitor, Electronic similarity
The fatty acid synthase (FAS) inhibitor is expected to work as an anti-cancer drug. Several
candidates as the FAS inhibitor have experimentally suggested by Wang et al. [1] and Li et al.
[2-3]. In order to investigate the structure-activity relationship of these molecules, we aredeveloping a computational method evaluating three-dimensional topology similarity where thetopology is obtained by using electronic-structure calculations. We call this similarity“three-dimensional electronic-shape similarity (3D-ESS)”. Herein we focus on electro-static
potential (ESP) as an example of 3D-ESS. In the presentation, we will introduce a program forscoring ESP similarity among the compounds. We will discuss correlation between the similarity
score and IC 50 for the FAS inhibitors through evaluation of 3D-ESS. The combined use of 3D-ESS
with energy-based electronic- similarity (EB-ES) will also be discussed.
[1] Wang, X., Song, K. S., Guo, Q. X., and Tian, W. X., The galloyl moiety of green tea catechinsis the critical structural feature to inhibit fatty acid synthase, Biochem. Pharma., 66:
2039-2047, 2003.
[2] L i, B . H ., a n d T ia n , W . X ., Inhibitory effects of flavonoids animal fatty acid synthase, J.
Biochem., 135: 85-91, 2004.
[3] Li, B. H., Ma, X. F., Wang, Y., and Tian, W. X., Structure-activity relationship of polyphenols
that inhibit fatty acid synthase, J. Biochem., 138: 679-685, 2005.

P1-27

Analysis of Ras/Raf-RBD Complex by Molecular Dynamics Simulation
Shota Matsunaga

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

Shigenori Tanaka

tanaka2@kobe-u.ac.jp

Department of Computational Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo
657-8501, Japan

Keywords: Ras/Raf-RBD complex, cancer, molecular dynamics (MD) method, GTPase-activating proteins (GAPs)

The Ras/Raf/MEK/ERK signal transduction pathway plays an important role in controlling cell proliferation and differentiation[1].
Abnormal activation of Ras or Raf genes has frequently been reported in human cancer[2]. In this study, to elucidate the mechanismof the Ras/Raf interaction, we investigate the interconversion between two conformational states, State 1 and State 2[3], of theRas/Raf-RBD complex using the molecular dynamics (MD) method. In particular, we analyze the two switch regions (Switch I
and Switch II) of both GTP-bound form and GDP-bound form. To accelerate the conformational change of GDP-bound form, theGDP molecule was given the thermal energy generated when the GTP molecule was hydrolyzed by GTPase-activating proteins(GAPs) [4].
As a result of calculating dT (the distance between the oxygen atom of Thr35 of Ras and either the γ-phosphorus atom of GTP or
the β-phosphorus atom of GDP) and dY (the oxygen atom of Tyr64 of Ras and either the γ-phosphorus atom of GTP or the βphosphorus atom of GDP) [5], it was found that Switch II is repeatedly opened and closed because of the allosteric property of binding
in the standard MD simulation. On the other hand, we found that the thermal energy from hydrolysis of GTP is important for theSwitch I to be opened sufficiently. From the results of the simulations with and without the added energy, we succeeded to obtainthe comprehensive pictures of the signal transduction in the Ras/Raf-RBD complex.

References[1] S. K. Fetics, H. Guterres, B. M. Kearney, G. Buhrman, B. Ma, R. Nussinov, C. Mattos, Allosteric Effects of the oncogenic
RasQ61L mutant on Raf-RBD, Struct. Des., 23: 3, 2015.
[2] L. Santarpia, S. L. Lippman, A. K. EI-Naggar, Targeting the mitogen-activated protein kinase Ras-Raf signaling pathway incancer therapy, NIH Pub., 16: 1, 2012[3] S. Matsumoto, N. Miyano, S. Baba, J. Liao, T. Kawamura, C. Tsuda, A. Takeda, M. Yamamoto, T. Kumasaka, T. Kataoka, F.
Shima, Molecular mechanism for conformational dynamics of Ras·GTP elucidated from In-Situ structural transition in crystal, Sci.
Rep., 6: 25931, 2016.
[4] K. Ogata, J. Shen, S. Sugawa, S. Nakamura, MD simulation study of Ras/Raf dissociation and the resonating structure ofdeactivated Ras, Chem. Soc., 85: 12, 2012.
[5] S. Matsunaga, Y. Hano, Y. Saito, K. Fujimoto, T. Kumasaka, S. Matsumoto, T. Kataoka, F. Shima, S. Tanaka, Structuraltransition of solvated H-Ras/GTP revealed by molecular dynamics simulation and local network entropy, J. Mol. Graph. Model.,2017, in press.

P1-28Energy correlation of protein at equilibrium fluctuationand its connection with ligand binding
Hikaru Iba 1

Yuu Ymamori 1

iba0119@cheng.es.osaka-u.ac.jp

yamamori@cheng.es.osaka-u.ac.jp

Nobuyuki Matubayasi 1
nobuyuki@cheng.es.osaka-u.ac.jp

1

Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University,
Toyonaka, Osaka 560-8531, Japan

Keywords: Molecular dynamics simulation, Ligand binding, Energy correlation
Protein exchanges the energy among constituent residues and the surrounding solvent. The modeof energy exchanges is then coupled to the structural fluctuation and can lead to the structuralchange due to an external perturbation such as ligand binding. In the present work, we investigate
the correlations of intra-residue structural energy and the interaction energy with solvent water.
We treat shikimate kinase and address the connection to the structural change upon binding of ATP
ligand.
To sample a set of protein structures, all-atom MD simulations were conducted over 10 ns at asampling interval of 1 ns in the NPT ensemble with 300 K and 1 bar. Each sampled structure wassubject to the energy analyses, and the intra-residue structural energy and the interaction energy
with solvent water (v) were computed at fixed structure of the protein in the ensembles of the apo
and holo forms.
Figure shows the correlation coefficient of v among the residues of the apo and holo forms. The
off-diagonal correlations are significant in the apo form in Figure (a) between residues 10-20 and
115-120, between residues 10-20 and 135-140, and between residues 115-120 and 135-140. The
ATP binding does not affect the correlation features and strengthens the correlations for 10-30 and
105-110, for 10-20 and 145-155, and for 105-110 and 150-160. These residues correspond to the
ATP binding sites. They come close at the binding, leading to the strengthened correlation of v.
(a)

(b)

Figure. Energy correlation map of v among the residues of shimate kinase in the (a) apo and(b) holo states.

P1-29In silico binding affinity analysis forphosphodiesterase-10A inhibitors.
Chisa Yuasa 1
w175438e@yokohama-cu.ac.jp

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

1

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

Mitsunori Ikeguchi 1
ike@tsurumi.yokohama-cu.ac.jp

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

Keywords: Ligand docking, Binding free energy calculation, Molecular Dynamics simulation

Phosphodiesterase (PDE) is an enzyme that hydrolyzes cyclic -AMP (c-AMP) and cyclic-GMP(c-GMP), which play important roles in signal transduction. PDE10A specifically expresses inbrains, and PDE10A is considered as a drug target of schizophrenia.
In order to understand how the drug activity relates to interactions manner between PDE10A andligand, we performed docking simulations and calculated the binding free energies between
PDE10A and 24 ligands using 4 approaches: the Glide docking score, MM-GBSA, FEP+ and
MP-CAFEE method. The calculated binding free energies were compared with experimental IC 50 .
Although no correlation between the Glide docking score and experimental IC 50 was observed, thebinding free energies calculated by MM-GBSA and FEP+ were correlated with IC 50 .
In addition, we researched the docking-pose dependence of the binding free energy calculations.
We examined effects of water molecules in the binding site on the docking poses. Inclusion ofwater molecules improved the correlation betwee n the calculated binding free energy and IC 50 .
FEP+ showed the best performance among the methods considered here. From these examinations,
we found two important points for the high correlation: precise docking poses of ligands andaccount of hydration water molecules in binding free energy calculations.
[1] J. Kunitomo, M. Yoshikawa, M. Fushimi, A. Kawada, J F. Quinn, H Oki, H. Kokubo, M.
Kondo, K. Nakashima, N. Kamiguchi, K. Suzuki, H. Kimura, and T. Taniguchi,
Discovery of 1‐[2-Fluoro-4-(1H‐pyrazol-1-yl)phenyl]-5-methoxy-3-(1phenyl‐1H‐pyrazol-5-yl)pyridazin-4(1H)‐one (TAK-063), a Highly Potent, Selective, and Orally
Active Phosphodiesterase 10A (PDE10A) Inhibitor .
J. Med. Chem. 2014, 57, 9627−9643[2] L. Wang, B. J. Berne and A. Friesner
On achieving high accuracy and reliability in the calculation of relative protein –ligand binding
affinities. Pr oc. Natl. Acad. S ci. USA 2012, 109, 1937-1942[3] E. Harder, W. Damm, J. Maple, C. Wu, M. Reboul, J. Y. Xiang, L. Wang, D. Lu pyan, M. K.
Dahlgren, J. L. Knight, J. W. Kaus, D. S. Cerutti, G. Krilov, W. L. Jorgensen, R. Abel, and R. A.
Friesner, OPLS3: A Force Field Providing Broad Coverage of Drug -like Small Molecules
and Proteins. J. Chem Theory Comput. 2016, 12, 281-296

P1-30Computational Determination of the Partial Charges
of Neutral Sialic Acid
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

12

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

Keywords: Electronic structure calculation, Force field, Glycan, Parameter fitting
The sugar chain is actively studied as the third “life molecule” in recent years. Molecular
dynamics (MD) simulation is a useful tool to clarify the relationship between structure andfunction of biomolecules including proteins and sugar chains, where force field called
GLYCAM [1] is introduced in MD software AMBER. However, in this force field, only
carboxyl groups in deprotonated state are prepared, which are considered to be unsuitable forcalculations in acidic solution.
Sialic acid is a monosaccharide having the carboxyl group and is known to play an importantrole in biological mechanisms such as virus infection. Here we report the determination of theatomic charges in the neutral state of this sialic acid (N-acetylnueraminic acid). Furthermore, we
discuss the difference in the atomic charges between neutral and deprotonated states, and its
influence on MD results.
Sialic acid (N-acetylnueraminic acid)

Deprotonated state

Neutral state[1] Kirschner, K. N., et al., GLYCAM06: a generalizable biomolecular force field.
Carbohydrates, J. Comput. Chem., 29(4):622-55, 2008.

P1-31Time-dependent Variance-covariance Matrix Analysis of
Molecular Dynamics for Protein-ligand Interaction
Masanori Yamanaka 1
yamanaka.masanori@nihon-u.ac.jp

1

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

Keywords: Molecular dynamics, Protein ligand interaction, Variance-covariance matrix, Randommatrix, Cluster analysis
We study the protein-ligand interaction using the characteristic scale of coupling formation [1]developed in random matrix theory. The eigensystems of the timescale -dependent
variance-covariance matrix, which are obtained from the time series data of t he atomic coordinatesof a protein produced by the all-atom molecular dynamics of the solvent, are analyzed. [2] As an
example, we present a result for a protein, N-terminal PDZ domain of ZASP in complex withmyotilin C-terminal peptide, PDBID:4YDP, which has a ligand of peptide with three amino acids.
We find that there are at least four different time scales involved in the coupling formation ofcorrelated sectors of atoms and an oscillation of the eigenvalue as a function of the samplinginterval of the variance-covariance matrices. These time scales and the oscillation behavior coexist
simultaneously. From the study of the eigenvector components, the weights of the ligand atoms are
dominant in a small time scale. We further compare the results with those without ligand and withthe dataset which we artificially displace the coordinates of the ligand.
[1] For example see a review article, J. Kwapień and S. Drożdż, Phys. Rep. 515, 115 (2012).
[2] M. Yamanaka, J. Phys. Soc. Jpn. 84, 063801 (2015).

P1-32In silico protein-protein interaction analysis of axon
guidance molecule semaphorin and receptor plexin
Erena Shimoji 1

Tsutomu Yamane 1eshimoji@tsurumi.yokoama-cu.ac.jp

yamane@tsurumi.yokohama-cu.ac.jp

Toru Ekimoto 1

Nogi Terukazu 1

ekimoto@tsurumi.yokohama-cu.ac.jp

nogi@tsurumi.yokohama-cu.ac.jp

Mitsunori Ikeguchi 1
ike@tsurumi.yokohama-cu.ac.jp

1

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

Keywords: Semaphorin, Guidance molecule, Protein-protein interaction
Semaphorin has been known as an axon guidance molecule, secreted around cells.
Plexin is a receptor of semaphorin, and their interactions are guidance cues for axons ofneurons during development of the nervous system. Because the protein-protein
interactions (PPI) of semaphorin and plexin have wide interaction surfaces, it is nottrivial to understand the determinants of PPI. The surface of protein is too shallow andwide to bind small compound, so it is difficult to discover drug for PPI target. It is
important to understand the mechanism to control PPI for drug discovery. Semaphorin
and plexin include “Sema domain” with high sequence similarity, and they form PPI andinteract through the sema domain. Therefore, we analyzed the PPI using in silico
approaches to recognize the feature of specific interactions. Mor e specifically, we used
program Rosetta to calculate the binding free energy and to perform in-silico alanine
scanning [1,2] , and identified the “hotspots” which are significant amino acids inbinding. Then, the details of the hotspot interactions were analyzed to understand thedeterminants of PPI.

[1] T. Kortemme and D. Baker, A simple physical model for binding energy hot spots inprotein–protein complexes. Proc. Natl. Acad. Sci. 99, 14116-14121, 2002.
[2] T. Kortemme, D. E. Kim, and D. Baker, Computational Alanine Scanning of Protein-Protein
Interfaces. Sci. STKE 10, 2004.
[3] B. J. C. Janssen, R. A. Robinson, F. Perez-Brangul , C. H. Bell, K. J. Mitchell, C. Siebold & E.
Y. Jones, Structural basis of semaphorin–plexin signaling. Nature 467 1118-1122, 2010.

P1-33Characterization of TCR-pMHC interaction atresidue-level based on FMO and PIEDA
Hiromichi Tsurui 1
tsurui@juntendo.ac.jp

Yoshiki Namiuchi 3
Yoshiki.namiuchi@riken.jp

Yuko Tsuchiya 2
tsuchiya@protein.osaka-u.ac.jp

Hiroshi Wako 4
wako@waseda.jp

1

Department of Pathology, Juntendo University School of Medicine, Bunkyo-ku, Tokyo, Japan
Institute for Protein Research, Osaka University, Suita, Osaka, Japan
3
RIKEN Quantitative Biology Center, Suita, Osaka, Japan
4
School of Social Sciences, Waseda University, Shinjuku-ku, Tokyo, Japan
2

Keywords: TCR-pMHC, FMO, PIEDA, CH-, hydrogen bond,Interaction between T cell receptor (TCR) and peptide-MHC (pMHC) is essential for determining
T cell fate and immunological response. However, determining actual binding profile of TCR by
experiment requires vast amount of labor and by computer simulation is still a challenge. To
investigate what interactions determine binding profile of TCR, we examined TCR-pMHC
complexes having common pMHC and different TCRs with variety of binding profile, namely
narrow (PDBID: 3c5z, 3rdt, 4p5t), modestly (3c6l), and highly cross-reactive (3c60). TCRs withspecific profile formed more hydrogen bonds (about 10) between CDRs to presented peptide thanthat with cross-reactive profile (about 6 to 7), suggesting the contribution of hydrogen bond to thespecific interaction. CH-p interaction seemed between CDR3 aromatic residues and MHC helix
in both complexes containing specific and cross-reactive TCRs. Both hydrogen and CH-p bonds
are principally determined geometrically and actual interaction-energies are obscure. We examinedthose bonds with FMO calculation at MP2/6-31G* level and PIEDA.

P1-34

A QM/MM study on binding affinity computation of
tankyrase 2-ligand system
Yoshinori Hirano 1
hirano@riken.jp

1

Takao Otsuka 1
totsuka@riken.jp

Noriaki Okimoto 1

Makoto Taiji 1

okimoto@riken.jp

taiji@riken.jp

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

Keywords: Binding affinity computation, QM/MM method
Accurate binding affinity computation of protein-ligand system is of significant importance forthe drug discovery. There are various computational methods to predict the protein-ligand binding
affinity. One of the most popular approach for protein-ligand binding affinity is the free energy
calculation based on molecular dynamics (MD) simulation, such as the linear interaction energy
method, the molecular mechanics/Poisson-Boltzmann and surface area (MM-PB/SA) method, thealchemical free energy calculation, and so on. Most of these methods use classical molecularmechanics (MM) force fields.
Protein-ligand interactions consist of various non-bonded interactions such as π-stacking, chargetransfer, polarization effects, and dispersion interaction, as well as the usual and/or weak hydrogenbonds. Therefore, it is difficult to treat these interactions by the classical MM force fields.
Quantum mechanical (QM) calculations can consider these non-bonded interactions. We haveexpected to improve the accuracy of protein-ligand binding affinity compared with that of the MMforce fields. In our previous study, we focused on the evaluation of binding affinity through a fullQM calculation and proposed the accurate and efficient computational scheme by using multilayer
fragment molecular orbital (MFMO) method 1 .
In this study, we explore a computational-cost effective scheme to calculate protein-ligand
binding affinity by using QM/MM method. One of the key points is how the size of the QM region
influences the performance of binding affinity prediction. We will report the binding affinities oftankyrase 2-ligand systems using QM/MM method 2-4 with some different QM regions.
[1] Otsuka, T.; Okimoto, N.; Taiji, M., J. Comput. Chem., 2015, 36, 2209-2218.
[2] Schrödinger Release 2017-2: QSite, Schrödinger, LLC, New York, NY, 2017.
[3] Murphy, R. B.; Philipp, D. M.; Friesner, R. A., "A mixed quantum mechanics/molecularmechanics (QM/MM) method for large-scale modeling of chemistry in protein environments," J.
Comp. Chem., 2000, 21, 1442-1457[4] Philipp, D. M.; Friesner, R. A., "Mixed ab initio QM/MM modeling using frozen orbitals andtests with alanine dipeptide and tetrapeptide," J. Comput. Chem., 1999, 20, 1468-1494

P1-35The distinct dynamic mechanisms of peptide recognitionby specific and cross-reactive T-cell receptors
Yuko Tsuchiya 1
tsuchiya@protein.osaka-u.ac.jp

1
2
3

4

Yoshiki Namiuchi 2
yoshiki.namiuchi@riken.jp

Hiroshi Wako 3

Hiromichi Tsurui 4

wako@waseda.jp

tsurui@juntendo.ac.jp

Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, JapanRIKEN Quantitative Biology Center, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
School of Social Sciences, Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo
169-8050, Japan
Department of Pathology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku,
Tokyo 113-8421, Japan

Keywords: Cross-reactive TCR recognition, binding affinity-interface area relationship,
Molecular dynamics (MD) simulation, Fragment molecular orbital (FMO) method
Some T-cell receptors (TCRs) recognize an antigen peptide presented by Major
Histocompatibility Complex (pMHC) specifically, whereas others recognize it cross-reactively. We
focused on five TCRs in complex with the same class II MHC-peptide, three and two of which
recognize the peptide specifically and cross-reactively, respectively. These complexes have similarbinding affinities, whereas the interface areas between TCR and pMHC differ significantly. In thisstudy, we investigated static and dynamic structural features of the TCR-pMHC complex and of
TCR in free state, and the relationship between binding affinity and interface area. Our dynamics
study revealed differences in the recognition mechanisms of TCRs, i.e., differences in interaction
types and balances of enthalpy gain and entropy loss upon binding.

P1-36

Interaction Analysis between Beta-Secretase and its Inhibitors
by Fragment Orbital Method
Norihito KAWASHITA 1

Yuji HASHIMOTO 2

Hirotomo MORIWAKI 2

kawashita@life.kindai.ac.jp

yuji71070516@gmail.com

hirotomo.moriwaki@gmail.com

Yu-Shi TIAN 2
yushi-tian@phs.osaka-u.ac.jp

1

2

Tatsuya TAKAGI 2
ttakagi@phs.osaka-u.ac.jp

Faculty of Science and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka City,
Osaka 577-8502, Japan
Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka
565-0871, Japan

Keywords: Beta-Secretase, Fragment molecular orbital (FMO) method, Inter-fragment interactionenergy (IFIE)
Beta-Secretase (BACE) 1 is an aspartyl protease, it’s active site is made up of two aspartate
residues: Asp32 and Asp228. The β-hairpin loop over the active site, known as the “flap”, is also
an important feature to BACE1. While the active site remains inactive, the flap stays in its open
conformation. However, the flap is stabilized while closed over its subst rate or some inhibitor [1].
Currently, aminoquinolines, aminoisoquinolines, and aminopyrimidone with cyclic-amidine
structure were reported as potent fragment hits binding to BACE1 [2]. Since then, several studies
on similar inhibitors with cyclic-amidine structure have been reported.
In this study, we performed FMO calculation to examine the molecular interactions between
BACE1 and its ligands with cyclic-amidine structure for identified the key information forscreening of more potent inhibitors.
Cocrystal structures of ligands and BACE1 were obtained from PDB. MOE [3] was used forstructure preparation. MMFF94x force field was used for energy minimizations. FMO calculations
were carried out in ABINIT-MP (Ver. 6.0+) [4] with MP2/6-31G*.
From the results, we confirmed the correlation between IC 50 and IFIE-SUM (IFIE between
ligand and receptor). The coefficient of determination R 2 was 0.728, suggesting a good correlation.
Next, Asp28 and 228’s IFIE notably contributed to the stabilization of protein -ligand interaction.
And we detected tendency that ligands without aromaticity contributed to st abilization more thanligands with aromaticity. Therefore, when we design BACE1 inhibitors with cyclic-amidine
structure, it is better that we choose structure without aromaticity.
In this study, we performed FMO method to examine the molecular int eractions between
BACE1 and its ligands. Not only good correlation between IC 50 and IFIE-SUM but also interaction
tendency in BACE1-ligand complex were obtained. Hereafter, we will execute further calculations
and more detailed IFIE analyses. Eventually, we aim at contribution to d esign novel inhibitors.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp150160, hp160103, and hp170183). 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.

P1-37In silico modeling of PAX8–PPARγ fusion protein withunknown three-dimensional structure in follicular thyroid
adenoma and carcinoma
Kaori Sakaguchi 1

Yoshio Okiyama 2sakaguchi@eniac.scitec.kobe-u.ac.jp

okiyama@nihs.go.jp

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

12

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
National Institute of Health Sciences, 1 -18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501, Japan

Keywords: Follicular neoplasm, fusion protein, protein modeling, Molecular dynamics simulation
Follicular neoplasms including follicular thyroid adenoma (FTA) and follicular thyroid carcinoma(FTC) have no molecular-targeted agents. Specifically in these tumor cells, a t(2,3) (q13,p25)
chromosomal translocation results in production of paired -box gene 8 (PAX8)-peroxisome
proliferator-activated receptor γ gene (PPARγ) fusion protein (PPFP). While the three-dimensional(3D) structure of PPFP and its functions are not reported so far, a transgenic mouse model showsthat pioglitazone, an effective agonist of PPARγ, makes PPFP behave as PPARγ-like transcription
factor; it turns tumor cells into adipocytes and deprives their malignant characters [1]. Based on
this finding, it is possible to obtain more effective anticancer agents for FTA and FTC if we candesign compounds specifically bound to PPFP.
We here propose a 3D structure of PPFP for establishment of targeted therapies in FTA and FTC.
We constructed amino acid sequence of PPFP that was composed of PAX8 (Met1-Ala396) andPPARγ (Met1-Tyr505) based on the currently proposed schematic diagram of PPFP [2]. In PDB,
there are X-ray crystal structure of PAX6 paired domain with DNA (PDB ID: 6PAX) having highhomology with PAX8 and the intact PPARγ on DNA (PDB ID: 3DZY). Using them, we assumed afusion construct containing both PAX8 and PPARγ on DNA, and predicted the structure of PAX8
and the fusing region by I-TASSER [3] online servers. After some modification and relaxation, thefinal 3D structures were obtained in this study. To validate the predicted models, we analyzed their
physicochemical characteristics, Ramachandran plot distributions and dynamical stabilities withmolecular dynamics simulation.
[1] B. Xu et al., J. Biol. Chem. 291, 19274-19286 (2016).
[2] P. Raman et al., Nat. Rev. Endocrinol. 10, 616-623 (2014).
[3] J. Yang et al., Nat. Methods. 12, 7-8 (2015).

P1-38Computational characterization of influenza A virus
hemagglutinin recognition sites by a cross-neutralizing
monoclonal antibody
Manabu Igarashi 1,2

Masakazu Sekijima 3

Nobuaki Yasuo 3

igarashi@czc.hokudai.ac.jp

sekijima@gsic.titech.ac.jp

yasuo.n.aa@m.titech.ac.jp

Takashi Abe 4
takaabe@ie.niigata-u.ac.jp

1
2

3

4
5
6

Teruaki Watabe 5
twatabe-mi@umin.ac.jp

Rashid Manzoor 1
manzoor@czc.hokudai.ac.jp

Takatsugu Hirokawa 6

Ayato Takada 1,2

t-hirokawa@aist.go.jp

atakada@czc.hokudai.ac.jp

Research Center for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido, Japan
Global Station for Zoonosis Control, Global Institution for Collaborative Research and
Education (GI-CoRE), Hokkaido University, Sapporo, Hokkaido, Japan
Global Scientific Information and Computing Center, Tokyo Institute of Technology,
Meguro-ku, Tokyo, Japan
Department of Information Engineering, Niigata University, Niigata, Japan
Center of Medical Information Science, Kochi Medical School, Kochi University, Kochi, Japan
Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan

Keywords: Molecular dynamics, Influenza virus, Antibody, Protein modeling, Binding free energy
Background
The hemagglutinin (HA) of influenza A viruses is classified into 16 subtypes (H1-H16). It is
generally known that HA-specific antibodies have little cross-neutralizing activity against multiple
HA subtypes. Recently, however, several broadly neutralizing antibodies were reported and haveattracted attention due to their potential application to therapeutics and vaccine design. We havepreviously reported a cross-reactive antibody, designated S139/1, which neutralizes H1, H2, H3,
H13, and H16 subtypes and its crystal structure in complex with the HA of the A/Victoria/3/1975(H3N2) strain. However, detailed structural basis of its cross-neutralizing activity still remain tobe elucidated. In this study, we characterized the S139/1 recognition sites on different HAs using
computational structural biology methods.
Materials and Methods
The HAs from eight strains (subtypes H1, H2, H3, H6, H9, and H13) were analyzed. The structure
models of the HA-S139/1 complex were constructed by homology modeling. Using the structures
as starting points, we performed molecular dynamics (MD) simulations, and then calculated
binding free energies (ΔG) between S139/1 and each HA.
Results and Discussions
The ΔG values of the strains neutralized by S139/1 were lower than the other strains tested. We
next investigated the contribution of individual residues on each HA to the interaction with S139/1
and found that amino acids at positions 98, 136, 153, 156, 158, 159, 193, 194, 196, and 226 (H3numbering) on HA strongly contributed to S139/1 binding as for the strains neutralized by S139/1.
Additionally, we found that, in the strains neutralized by S139/1, K156 formed a salt bridge with
the Ig heavy chain E50. Indeed, amino acid substitutions at adjacent residues were experimentallyobserved in the mutant viruses escaping from neutralization by S139/1. Thus, our computational
methods identified the amino acid residues critical for the cross-neutralizing activity of S139/1.

P1-39Characteristics of Biomolecule Dynamics under the
Crowding Environment of Cytoplasm Discovered by
Massive All-atom simulation and Big-data analysis
Isseki Yu 1,2Po-hung.wang 2

isseki@riken.jp

po-hung.wang@riken.jp

Michael Feig 5

Yuji Sugita 1,2,3,4

feig@msu.edu

sugita@riken.jp

1

iTHES Research Group, RIKEN, Saitama, Japan
Theoretical Molecular Science Laboratory, RIKEN, Saitama, Japan
3
Laboratory for Biomolecular Function Simulation, RIKEN Quantitative Biology Center, Kobe, Japan
4
Computational Biophysics Research Team, RIKEN Advanced Institute for Computational Sci ence, Kobe,
Japan
5
Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, UnitedStates2

Keywords: Cellular environment, Molecular crowding, Protein diffusion, Metabolites
Inside of a cell is highly crowded with a large number of macromolecules together with solventsand metabolites. How variable specific/non-specific interactions within dense cellularenvironments may affect the structure and dynamics, and ultimately function is one of the mostfundamental questions in life science. We constructed full atomistic model of the cytoplasm ofbacteria and various levels of protein crowding models. Using these model, we performed massive
all-atom molecular dynamics (MD) simulation with the highly parallelized MD simulator
GENESIS on K-computer. Influence of crowding and non-specific interaction between
macromolecules on the translational/rotational /collective motion of proteins are analyzed.
Anomalous diffusion and the localization of metabolites, such as ATP and amino acids, on themacromolecule surface are also investigated. These work is an important step towards physicallyrealistic in silico whole-cell models that connect molecular with cellular biology.

[1] Michael Feig, Isseki Yu, Po-Hung Wang, Grzegorz Nawrocki, Yuji Sugita, Crowding in Cellular
Environments at an Atomistic Level from Computer Simulations , Journal of Physical chemistry B (inpress)[2] Po-hung Wang, Isseki Yu, Michael Feig, Yuji Sugita, Influence of Protein Crowder Size on
Hydration Structure and Dynamics in Macromolecular Crowding, Chemical Physics Letters 671, 63-70,(2017)[3] Isseki Yu, Takaharu Mori, Tadashi Ando, Ryuhei Harada, Jaewoon Jung, Michael Feig, Yuji
Sugita, Biomolecular interactions modulate macromolecular structure and dynamics in atomistic modelof a bacterial cytoplasm, eLife 2016;5:e19274[4] Michael Feig, Ryuhei Harada, Takaharu Mori, Isseki Yu, Koichi Takahashi, Yuji Sugita , Complete
atomistic model of a bacterial cytoplasm for integratingphysics, biochemistry, and systems bio logy
Journal of Molecular Graphics and Modelling , 58, 1–9, (2015)

P1-40

Molecular Dynamics Simulations of HIV Tat proteins andAmyloid-β peptides
Kazumi Omata 1

Satoru G. Itoh 2

komata@hosp.ncgm.go.jp

itoh@ims.ac.jp

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

12
3

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
To identify binding sites of an HIV transactivator of transcription (Tat) protein and an amyloid-β(Aβ) peptide, the present study has investigated their docking, using molecular dynamics (MD)simulations.
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.
The results of our MD simulations could be used for the inhibition of the Tat-Aβ complex
formation and the development of drugs for neurocognitive dysfunction of HIV-infectedindividuals.
[1] Hategan, A., et al., HIV Tat protein and amyloid-β peptide form multifibrillar structures that cause
neurotoxicity. Nature Structural & Molecular Biology 24, 379–386, 2017.
[2] Okumura, H. and Itoh, S. G., Structural and fluctuational difference between two ends ofAβamyloid fibril: MD simulations predict only one end has open conformations . Scientific
Reports 6, Article number:38422, 2016.
[3] Okumura, H., Temperature and pressure denaturat ion of chignolin: Folding and unfolding
simulation by multibaric-multithermal molecular dynamics method. Proteins, 80: 2397-2416,
2012.

P1-41An index to select appropriate homologous sequences for functional region
prediction of a protein
Shoichiro Kato 1
16rmb05@ms.dendai.ac.jp

Hiroyuki Toh2

tohhir@kwansei.ac.jp

Wataru Nemoto 1
w.nemoto@mail.dendai.ac.jp
1

2

Division of Life Science and Engineering, Graduate School of Science and Engineering,
Graduate School of Tokyo Denki University (TDU), JAPAN.
D epa rtment of Bi om edica l C hemis tr y, Sch ool of S c ienc e a nd Te chnol og y, Kw a ns ei
Ga kuin U nivers it y, J A PA N.

Keywords: sequence selection, phylogenetic tree, c onservation score,
The conserved amino acid residues among homologous amino acid sequences are ofte n closely
located each other at and near the functional regions in the tertiary structures. Based on theobservations, several methods have been developed to predict functional regions, in which both
homologous amino acid sequences and tertiary structures are used for the analysis. We have also
proposed a method with the same idea [1]. Given a target protein for the prediction, homologous
sequences are collected under the condition that the sequence identity to the target protein is lessthan or equal to a given threshold value. Then, a multiple sequence alignment is made for thetarget protein and the collected sequences, and the conservation score is calculated at eachalignment site. Next, the conservation score of each alignment site is mapped on the cor responding
residue of a tertiary structure of one of the homologous proteins or the target protein. Finally,clusters of conserved residues are detected as functional regions in the tertiary structure.
In this strategy, a set of homologous sequences to calculate conservation score is required tocontain the sequences with their functional regions at the corresponding sites on their structures. If
a set of homologous sequences includes the sequences with their functional regions at distinct
sites on their structures, it would be difficult to accurately evaluate residue conservation for thisstrategy. Hence, we introduced a new index based on the spatial autocorrelation to evaluate thespatial clustering of the conserved residues [1]. By changing the threshold value for the sequencecollection from 90 % to 10 % with an interval of 5 %, different sets of the homologous sequences
are generated, to each of which the new index is calculated. We selected a set of the homologous
sequences with the maximum value of the index as the most appropriate set for the prediction
because the set is appropriate to evaluate the spatial clustering of the conserved residues . Our
method can not only predict the functional regions, but also determine the best set of thehomologous sequences for the prediction.
Our method successfully improved the performance of functional region pred ictions [1]. As
suggested in our manuscript, however, the collection of the homologous sequences based on thesequence identity to the target protein is too rough for such an approach, and the phylogenetic
relationship among the collected sequences should be taken into account to generate a set of thehomologous sequences for the prediction. In this work, hence, a set of homologous sequences wasconstructed as a cluster corresponding to a subtree in the phylogenetic tree of the target and its
homologs. The performance of the prediction was evaluated by shifting the subtree from theclosely related node to the distantly related node. The experimentally identified catalytic sites
obtained in Catalytic Site Atlas was used for the performance evaluation. We will explain detailsof the procedure of our method and discuss advantages and disadvantages of the new approach.
[1] Nemoto, W. & Toh, H. BMC Struct. Biol. 12, 11, 2012

P1-42
Influence of Molecular Force Field on Performance ofProtein Ligand Docking
Daisuke Takaya 1
daisuke .takaya@riken.jp

Kikuko Kamisaka 1

Shunpei Nagase 1

kikuko .kamisaka@riken.jp

shunpei.nagase@riken.jp

Teruki Honma 1
honma.teruki@riken.jp

1

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi, Yokohama
230-0045, Japan.

Keywords: SBDD, Protein-Ligand docking, Benchmark
Structure Based Drug Design (SBDD) is one of the most powerful approach when a target protein is
known and its 3D-coordinates are experimentally determined by X-ray crystal structure analysis or canbe constructed based on its template by using homology modeling. Since protein-ligand complex
structures are not always available in drug design, protein-ligand docking programs (e.g. AutoDock
Vina 1 , FRED 2 , Glide 3 , and so on) are used for ligand binding mode prediction. Moreover, the docking
programs are also useful for predicting its affinity as a docking score to the target protein, searching
putative inhibitors from compound database and discriminating between active and inactive
compounds. Docking programs are widely used for in silico screening by not only computational
chemists but also biological researchers. 4,5
Generally, docking programs provide some parameters and conditions which influence the output suchas predicted binding modes and docking score of ligands. Therefore, it affects the prediction
performance of docking programs. For example, preprocess of the receptor structure such as additionof hydrogen atoms, assignment of atom types of molecular force field, coefficients of interaction termsof the score function, exploring methods for molecular conformation and position. Additionally, their
combination should be considered.
There are several approaches to handle the docking conditions. Firstly, the docking condition is
performed under default setting. Secondly, if there is data of known inhibitors for the target protein,cross docking and test docking are performed for optimizing the conditions for the target. Finally, theconditions are determined by visual inspection of the prediction results and experience ofcomputational researchers. It was thought that it would be useful for selecting docking setting if theinfluence of the parameters and conditions were known in advance. In this study, we examined theextent to which docking conditions affect the prediction performance by using benchmark sets. The
results will give guidance on whether the condition should be changed or not from the default setting.

[1] Trott, O. et al., AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring
Function, Efficient Optimization, and Multithreading J. Comput. Chem. 31:455-461, 2010[2] McGann, M. FRED Pose Prediction and Virtual Screening Accuracy J. Chem. Inf. Model.,51(3):578-596, 2011
[3] Friesner, R. A. et al., Glide: A New Approach for Rapid, Accurate Doc king and Scoring. 1. Method
and Assessment of Docking Accuracy J. Med. Chem. 2004, 47, 1739-1749[4] Takaya, D., et al., A new method for induced fit docking (GENIUS) and its application to virtual
screening of novel HCV NS3-4A protease inhibitors, Bioorg. Med. Chem. 19:6892–6905, 2011[5] Tajiri, H., et al., Targeting Ras-Driven Cancer Cell Survival and Invasion through Selective
Inhibition of DOCK1,Cell Reports 19(5):969-980, 2017

P1-43

Prediction of cancer-associated hotspot mutations that
affect GPCR oligomerization
Wataru Nemoto1,2

Vachiranee Limviphuvadh3

w.nemoto@mail.dendai.ac.jp

Sebastian Maurer-Stroh3

vachiraneel@bii.a-star.edu.sg

Yoshihiro Yamanishi4
yamanishi@bioreg.kyushu-u.ac.jp

sebastianms@bii.a-star.edu.sg

Motoaki Yamanoi2
17rmb24@ms.dendai.ac.jp

Hiroyuki Toh5
tohhir@kwansei.ac.jp

1

Division of Life Science and Engineering, School of Science and Engineering, Tokyo
Denki University, ishizaka, Hatoyama-machi, Saitama 350-0311, Japan
2
Division of Life Science and Engineering, Graduate School of Science and Engineering,
Graduate School of Tokyo Denki University, ishizaka, Hatoyama-machi, Saitama 3500311, Japan

3

Bioinformatics Institute (BII), Agency for Science, Technology and Research(A*STAR), 30 Biopolis Street, #07-01 Matrix, 138671, Singapore
4
Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8581, Japan
5

Department of Biomedical Chemistry, School of Science and Technology, Kwansei Gakuin
University, 2-1 Gakuen, Sanda-shi, Hyogo, 669-1337, Japan.

Keywords: membrane proteins, protein–protein interaction, interaction partner prediction,
support vector machine, drug design;
At present, approximately 30% of marketed pharmaceutical medicines target G ProteinCoupled Receptors (GPCRs). Numerous studies have reported that GPCRs function not only as
monomers but also as homo- or hetero- dimers or higher-order molecular complexes(oligomers). Many GPCRs exert a wide variety of functions by forming specific combinationsof GPCR subtypes. Several hetero GPCR hetero dimers are reportedly associated with diseases.
Therefore, GPCR oligomerization is recognized as an important event in various biologicalphenomena, and many researchers are investigating this subject. Hence, we developed a method
to predict interacting pairs for GPCR oligomerization by integrating the structure and sequence
information (GGIP). The area under the Receiver Operating Characteristic curve was 0.938.
GGIP is the only prediction method for interacting pairs among GPCRs. Our prediction serveris available at http://protein.b.dendai.ac.jp/GGIP/.
A recent study reported that somatic mutations in GPCR-encoding genes are frequently foundin various types of cancer. Hotspot mutations are defined as recurrent amino acid changes
occurring in coding sequences among hotspot mutations. It has been revealed that many GPCRs
have hotspot mutations in the same types of cancer tissues although most of the hotspot
mutations have not been characterized yet. In addition, their effects on the cancer pathways
remain unknown. Some of the hotspot mutations may be related to cancers through modifying
GPCR oligomerization since they are considered to be present on the surface of transmembrane
helices. In this work, we examined the predicted interacting pairs including the GPCRs withhotspot mutations. We will discuss the characteristics of these mutations and introduce several
examples.
[1] Nemoto, W. et al., Proteins. 12, 11, 2012

P1-44Structure and Dynamics
of RNA Aptamer to Human Immunoglobulin G
Hisae Yoshida 1,2
cehs17001@g.nihon-u.ac.jp

Sho Yamazaki 1
Takeshi Ishikawa 4

Keisuke Masukawa 1
ceke14138@g.nihon-u.ac.jp

Emire Inomata 3

cesi14155@g.nihon-u.ac.jp

t-ishi@nagasaki-u.ac.jp

Masahiro Sekiguchi 1
cema16020@g.nihon-u.ac.jp

e.inomata@ribomic.com

Taiichi Sakamoto 5
taiichi.sakamoto@p.chibakoudai.jp

Kazumasa Akita 3
k.akita@ribomic.com

Kenji Yamagishi 1
yamagishi.kenji@nihon-u.ac.jp

1

College of Engineering, Nihon University, 1 Nakagawara, Tokusada, Tamura, Koriyama,
Fukushima 963-8642, Japan
2
JSPS Research Fellow, Kojimachi Business Center Build ing, 5-3-1 Kojimachi, Chiyoda-ku,
Tokyo 102-0083, Japan
3
Ribomic Inc., 3-16-13 Shirokanedai, Minato-ku, Tokyo 108-0071, Japan
4
Graduate School of Biomedical Sciences, Nagasaki Universit y, 1-12-4 Sakamoto, Nagasaki,
Nagasaki 852-8523, Japan
5
Faculty of Advanced Engineering, Chiba Institute of Technology, 2 -17-1 Tsudanuma, Narashino,
Chiba 275-0016, Japan

Keywords: RNA, Aptamer, Tertiary structure, Dynamics, Interaction energy, MD, FMO
RNA aptamers are short single-stranded nucleic acids with high affinity and specificity for theirtarget molecules, which can be nucleic acids, proteins, or small organic compounds. RNA aptamers
may be of value as therapeutic agents for the treatment of a wide
variety of diseases. In order to be used in biological applications,the 2’ position of the sugar moiety of one or more nucleotide units
has to be suitably modified to make it resistant to nuclease digestion.
However, it is not well understood how dose chemical modification
for nucleotide units affect the uniquely tertiary structure of RNA
aptamer.
The aim of this study is to understand the structure and dynamics
of RNA aptamer. Our target aptamer is an optimized 23-nucleotide
aptamer, which was shown to bind with high affinity to the Fc
domain of human IgG (hFc1) [1]. The crystal structure of the hFc1
complexed with the RNA aptamer has been determined [2].
In order to elucidate the conformational behaviors and dynamical
features of IgG-RNA aptamer, we performed molecular dynamics(MD) calculations for some modified IgG-RNA aptamers. We
evaluated the interaction energies of base-pair and base-stacking
using the ab initio Fragment Molecular Orbital (FMO) calculationof IgG-RNA aptamers.
Figure. Interaction energies ofReference
the base-pair in the aptamer[1] S. Miyakawa, et al., RNA, 14:1154-1163, 2008.
[2] Y. Nomura, et al., Nucleic Acids Research, 38:7822-7829, 2010.

P2-01

WhichP450: Predicting Which Cytochrome P450 Isoforms
are involved in the Metabolism of a Xenobiotic
Peter Hunt 1

Jonathan Tyzack 1

peter@optibrium.com

1
2

jon@optibrium.com

Sumie Tajima 2
tajima@hulinks.co.jp

Marina Takahashi 2

Matthew Segall 1

m_takahashi@hulinks.co.jp

matt@optibrium.com

Optibrium Ltd., 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL, UK
Hulinks Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-0015, Japan

Keywords: Cytochrome P450 metabolism, isoform specificity, QSAR models, metabolite profile
Predicting which cytochrome P450 isoforms are involved in the metabolism of a molecule isimportant in assessing its metabolic fate in vivo. The various P450 isoforms have active sites ofdifferent shapes, sizes and characters [1], favouring different binding pharmacophores which canlead to metabolism at different sites within the molecule. Therefore, predicting the P450 isoforms
likely to be involved in metabolism is a useful precursor to predicting the metabolites that arelikely to be formed and considering which isoform-specific predictive models should be applied.
'Which P450' models also have application in assessing the risk of drug -drug interactions and theimpact of genetic polymorphisms. A molecule reliant on a single isoform for metabolic clearance isat an increased risk of exhibiting drug-drug interactions or a greater impact of genetic
polymorphisms on its pharmacokinetics.
Here we present a QSAR model to predict which P450 isoforms, out of a list of the 7 leading
CYP450 isoforms, are likely to be involved in the metabolism of novel molecules. This model was
generated using a multi-class categorical Random Forest method using the Auto-Modeller modulein StarDrop [2] and has a 75% success rate with a Top-1 criterion and a 90% success rate for aTop-2 criterion and showed significantly improved pe rformance over random estimates.
We furthermore illustrate how 'which P450' models can be combined with isoform -specificmodels for prediction of site of metabolism to predict the metabolite profile due to P450
metabolism of a xenobiotic.
[1] M.B.G. Cruciani et al., Molecular Interaction Fields, Wiley-VCH, 12:273-290, 2006.
[2] StarDrop. www.optibrium.com/stardrop

P2-02

Escape from ‘availability bias’ in compound designAndras Stracz 1
Gabor Imre 1

Ivan Solt 1

Akos Tarcsay 1
Daisuke Miyazaki 2
miyazaki@patcore.com

1
2

ChemAxon Ltd. 1031 Budapest. Zahony u. 7, Hungary
Patcore,Inc. Sapia Tower 26F, 1-7-12 Marunouchi, Chiyoda-ku, Tokyo 100-0005, Japan

Keywords: Compound Design Support, Fast Structure Search
Small molecule design is an information demanding activity, since all relevant knowledge is to beaccessible within a single space and requires synchronized application of computational models to
assist decision making on synthesis candidates. Our study aims to evaluate a software platform
coping with this complexity (Marvin Live). The tool provides central management of innovativeideas and a framework that helps triage these. Decision support is based on predicted properties
that span phys-chem descriptors, combined metrics like CNS MPO score, 3D overlay andmodelling results conducted with KNIME on the one hand, and cross-checking with available
knowledge collected from a variety of sources to do rapid freedom to operate analysis and SAR bycatalog: by ultra-fast searching of patent and compound catalog databases, respectively on theother. This hypothetic study shows the potential evolution of a compound idea from the referencecompound to a synthesis candidate through an example discovery project of 5-hydroxytryptamine
receptor 6 (HTR6) ligands.
[1] Wager, Travis T, et al. ACS Chem. Neurosci., 2010, 1 (6), pp 435-449[2] Papadatos, George, et al. Nucleic Acids Res. 2016 Jan 4; 44(Database issue): 01220-01228.
Database downloaded 01 May 2017[3] https://nodejs.org/ Accessed 04 June 2017[4] https://www.chemaxon.com/products/madfast/ Accessed 04 June 2017[5] Shivanyuk, A. N., et al. Chemistry Today 2007,25,58-59. Database downloaded 01 May 2017[6] Tolmachev, Andrey, et al. ACS Combinatorial Science 2016, 18.10,616-624.
[7]https://docs.chemaxon.com/display/docs/Chemical+Hashed+Fingerprint Accessed 04 June 2017[8]https://www.chemaxon.com/products/calculator-plugins/molecular-modelling/ Accessed 04
June 2017

P2-03
Virtual Screening of New Inhibitors from Indonesian
Herbal Compounds against Mycobacterium tuberculosis
CYP121
Vivitri Dewi Prasasty 1

Sandra Cindana 2

vivitri.dewi@atmajaya.ac.id

sandra.cendana@surya.ac.id

Fransiskus Xaverius Ivan 2
fx.ivan@surya.ac.id1

2

Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta, 12930,
Indonesia
Faculty of Life Sciences, Surya University, Tangerang, Banten, 15810, Indonesia

Keywords: M. tuberculosis CYP121, molecular docking, molecular dynamics, kaempferol
Tuberculosis (TB) is a serious threat with the emergence of resistant bacterial variants, namely
multidrug-resistant tuberculosis (MDR-TB) and extremely drug-resistant tuberculosis (XDR-TB).
This study aims to find new inhibitors for M. tuberculosis CYP121 from Indonesian medicinal
plants, especially Rhoeo spathacea and Pluchea indica. A virtual structure-based screening was
performed on 16 molecules through molecular docking using AutoDock Vina and subsequentmolecular dynamics simulations were performed using YASARA. The two best compounds, i.e.
KAE (kaempferol) and KAE3 (kaempferol derivative) were selected based on their lowest binding
energies (-9.1 kcal/mol and -10.5 kcal/mol, respectively) and their adherence to the Lipinski rule.
Results from 50 ns molecular dynamics simulation suggest that KAE and KAE3 have an inhibitory
mechanism againsts M. tuberculosis CYP121 that is different from GGJ (control inhibitor). Basedon the RMSF scores comparison, GGJ alters the overall dynamics of the receptor characterized by
the changes in the flexibility of amino acid residues far away from the receptor binding side. In
contrast, the binding of KAE and KAE3 changes the flexibility of amino acids around the bindingsite of the receptor.
[1] Collins, D., Hafidz, F. & Suraratdecha, C., The Economic Burden of Tuberculosis in Indonesia,
Cambridge: TB CARE I - Management Sciences for Health, 2013.
[2] Radji, M., Kurniati, M. and Kiranasari, A., Comparative antimycobacterial activity of someIndonesian medicinal plants against multi-drug resistant Mycobacterium tuberculosis. Journal ofApplied Pharmaceutical Science, 05(01): 019-022, 2015.
[3] McLean, K. J. and Munro, A. W., Structural biology and biochemistry of cytochrome P450
systems in Mycobacterium tuberculosis. Drug Metabolism Review, 40: 427-446, 2008.
[4] Sliwoski, G., Kothiwale, S., Meiler, J. and Lowe, E. W., Computational methods in drug
discovery. Pharmacological Reviews, 66(1): 334-395, 2014.

P2-04

A new rotation-translation invariant molecular descriptor for
Ligand-Based Virtual Screening and beyond
Francois Berenger1
berenger@bioreg.kyushu-u.ac.jp

Yoshihiro Yamanishi1,2
yamanishi@bioreg.kyushu-u.ac.jp

System Cohort Division, Medical Institute of Bioregulation, Kyushu University
3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
2
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
1

Keywords: Ligand-Based Virtual Screening, molecular similarity, chemical fingerprints, autocorrelation,
3D.
Various methods exist to compare ligands: fingerprints like MACCS, ECFP4 or PubChem and specialized
software like ROCS[1], EON, ElectroShape[2], ShaEP[3] and Blaze[4]. There are also various methods tocompare protein binding sites like sup-CK[5], SuMo[6], Shaper[7], SiteAlign[7] and FuzCav[7].
While fingerprints are only working in 2D, binding site comparison methods are complex and incorporate
several parameters. Interestingly, both kind of methods perform the same task of comparing two sets ofatoms.
In this study, we propose a novel method, scarce in parameters and working in 3D, that can do both ligand
and binding site similarity search, modulo re-parametrization of the method towards the intended use case(to maximize performance). The proposed method will be able to directly find ligands given a binding site;
without docking or prior knowledge of ligands for this binding site. Similarly, the opposite task of finding abinding site given a ligand is theoretically possible. We will show the usefulness of our proposed method bycomparing its performance with popular 2D chemical fingerprints. Our method considers ligands in 3D andis rotation-translation invariant. Ligands do not need to be optimally superposed prior to being compared.
Another innovative point of our method is that it can work in any or a combination of "chemical spaces" likeshape, electrostatic, hydrophobic and hydrogen bonding.
[1] Rush, T. S., Grant, J. A., Mosyak, L., & Nicholls, A. (2005). A shape-based 3-D scaffold hopping method
and its application to a bacterial protein− protein interaction. JMedChem, 48(5), 1489-1495.
[2] Armstrong, M. S., Morris, G. M., Finn, P. W., Sharma, R., Moretti, L., Cooper, R. I., & Richards, W. G.
(2010). ElectroShape: fast molecular similarity calculations incorporating shape, chirality andelectrostatics. JCAMD, 24(9), 789-801.
[3] Vainio, M. J., Puranen, J. S., & Johnson, M. S. (2009). ShaEP: molecular overlay based on shape andelectrostatic potential.
[4] Cheeseright, T., Mackey, M., Rose, S., & Vinter, A. (2006). Molecular field extrema as descriptors of
biological activity: definition and validation. JCIM, 46(2), 665-676.
[5] Hoffmann, B., Zaslavskiy, M., Vert, J. P., & Stoven, V. (2010). A new protein binding pocket similarity
measure based on comparison of clouds of atoms in 3D: application to ligand prediction. BMC
bioinformatics, 11(1), 99.
[6] Jambon, M., Andrieu, O., Combet, C., Deléage, G., Delfaud, F., & Geourjon, C. (2005). The SuMo
server: 3D search for protein functional sites. Bioinformatics, 21(20), 3929-3930.
[7] Kellenberger, E., Schalon, C., & Rognan, D. (2008). How to measure the similarity between protein
ligand-binding sites? CCADD, 4(3), 209.

P2-05

Improving 3D-QSAR in AutoGPA using Gaussiandistributed physicochemical descriptors
Yoshirou Kimura 1 , Akitoshi Okada 1 , Ryoichi Kataoka 1 , Atsushi Kanou 1
kimura.yoshirou@molsis.co.jp
1

Life Science Department, MOLSIS Inc., 1-28-38 Shinkawa, Chuo-ku, Tokyo 104-0033, Japan

Keywords: 3D-QSAR, AutoGPA, CoMFA, CoMSIA,
AutoGPA [1] is fully automated 3D-QSAR application developed on MOE [2]. It can build 3DQSAR models based on CoMFA [3] from the collection of ligand structural formulae and activity
values without a need of active conformers. AutoGPA can predict active conformers by aligningligands based on common pharmacophore features by making use of Pharmacophore Elucidation
function implemented in MOE.
In this study, we employed CoMSIA [4] method in AutoGPA with more and unique 3D descriptors
compared to the original CoMSIA. Weights and center positions of the new 3D descriptors are
assigned by pharmacophore features and atomic contribution of molecular properties. The available
pharmacophore features include cation, anion, aromatic ring, hydrogen -bond (H-bond)donor/acceptor. Furthermore, in case of H-bond donor/acceptor, H-bond donor/acceptor strength arederived from modified Hückel theory [5] and used as a weight of their Gaussian distribution. The
available molecular properties are volume, charge, logP, Molecular Refractivity.
We applied the molecular alignment of AutoGPA and the new 3D -QSAR method tophosphoinositide-dependent kinase-1 (PDK1) inhibitor dataset. The obtained 3D-QSAR model wascompared to one of the crystal structure of PDK1 and inhibitor complex (PDB ID: 2PE2). We foundthe positions and the properties of 3D descriptors which contribute to activity are similar to thoseof the ligand binding site (Fig. 1). The prediction accuracy of the 3D-QSAR model (q 2 = 0.834, r pred 2= 0.846) was better than that of the CoMFA model reported by previous AutoGPA (q 2 = 0.760, r pred 2= 0.811) [1]. We believe our new method enhances AutoGPA in its applicability and flexibility by
the use of additional 3D descriptors.

Figure 1. The isosurfaces of the 3D-QSAR model (left) and the 2D depiction around ligand binding siteof PDK1.
[1] Asakawa, N., Kobayashi, S., Goto, J., Hirayama, N.; Int. J. Med. Chem. 2012, 2012, 498931.
[2] Molecular Operating Environment (MOE), 2016.08; Chemical Computing Group Inc., 1010
Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016.
[3] Cramer, R.D., Patterson, D.E., Bunce, J.D.; J. Am. Chem. Soc. 1988, 110, 5959-5967.
[4] Klebe, G., Abraham, U., Mietzner, T.; J. Med. Chem. 1994, 37, 4130-4146.
[5] Gerber, P.R.; J. Comput.-Aid. Mol. Des. 1998, 12, 37–51.

P2-06

In silico search for proanthocyanidins binding proteins
Takuma Todoroki 1

Reina Takahashi 1

14a3035a@shinshu-u.ac.jp

14a3031h@shinhsu-u.ac.jp

Hiroshi Fujii 1,2
hfujii@shinshu-u.ac.jp	 Hidefumi Makabe 1
makabeh@shinshu-u.ac.jp

Koji Umezawa 1,2
koume@shinshu-u.ac.jp

12Faculty of agriculture, Shinshu University, 8304 Minami-minowa, Kami-ina,
Nagano, 399-4598, Japan
Department of Interdisciplinary Genome Sciences and Cell Metabolism, Institute for
Biomedical Sciences, Interdisciplinary Cluster for Cutting Edge Research, Shinshu University,
Minami-minowa, Kami-ina, Nagano, 399-4598, Japan

Keywords: Natural products, Ligand docking, Binding-pocket search
There are a lot of natural products good for health via a specific biological function while, inmost cases, the receptors for the molecules derived from the natural products are still unknown.
Proanthocyanidins belong to the functional natural products of polyphenols involved in grape,apple and so on, which exhibit various functions such as suppression of cell proliferation. The
receptors for proanthocyanidins have not been identified widely.
The proanthocyanidin is the polymer conjugating the units of catechin (cat), epicatechin (epi) andtheir derivatives of gallocatechin (gc) and epigallocatechin (egc). Therefore, the chemical structureof proanthocyanidin has a lot of variety depending on combination of the species of units and thenumber of polymerization. For instance, the dimer of egc (egc-egc) is called prodelphinidin B2(PDB2). Interestingly, the PDB2 exhibits a biological activity although the dimer of gc (gc-gc)
does not. Thus, the chemical variety is crucial for the biological activity. It should be related to a
receptor for proanthocyanidins. However, the receptor proteins that proanthocyanidins bind are notfound comprehensively. Then, we have explored the proanthocyanidin-binding proteins fromProtein Data Bank.
For the purpose of searching the proanthocyanidin-binding proteins from the database, we took
the two steps: local-similarity search and ligand-docking simulation. First, in the local-similarity
search, we chose the known cat-binding and epi-binding protein structures to constructbinding-pocket templates for cat and epi. The binding-pocket template is composed of thecoordinates of protein atoms in the vicinity of cat or epi. We compared the binding-pocket
templates with the all structures deposited in Protein Data Bank. We collected the proteins thathave the similar sites with the binding-pocket templates. In the next step, we conducted theligand-docking simulation for the collected proteins with proanthocyanidins. The
proanthocyanidin-binding candidates were ranked in order of the docking scores. Then, we could
search proteins related to biological functions including cell proliferation. These proteins might be
future drug targets as lead compounds of proanthocyanidins.

P2-07

Construction of FMO IFIE-database
Chiduru Watanabe 1

Daisuke Takaya 1

Shunpei Nagase 1

chiduru.watanabe@riken.jp

daisuke.takaya@riken.jp

shunpei.nagase@riken.jp

Kikuko Kamisaka 1

Yoshio Okiyama 1 ,2Kaori Fukuzawa 3

kikuko.kamisaka@riken.jp

yoshio.okiyama@riken.jp

k-fukuzawa@hoshi.ac.jp

Teruki Honma 1
honma.teruki@riken.jp

1
2
3

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
Division of Medicinal Safety Science, National Institute of Health Sciences, 1 -18-1 Kamiyoga,
Setagaya-ku, Tokyo 158-8501, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University 2-4-41 Ebara,
Shinagawa-ku, Tokyo 142-8501, Japan

Keywords: interaction energy database, FMO, ligand binding
The fragment molecular orbital (FMO) method proposed by Kitaura et al. enables us to efficiently
perform ab initio quantum mechanical calculations for large biomolecules by many-body
expansion technique with fragmentation. Inter-fragment interaction energy (IFIE) analysis basedon FMO is able to reveal important interactions in molecular recognition such as protein-ligand
binding in units of amino acid residues and a ligand. Moreover, the information of importantinteractions estimating from decomposed electronic energy terms (e.g. electrostatic and dispersion
interactions) is quite useful for drug design. On the other hand, it is not easy to perform FMO
calculations for not experienced researchers in the matter of structure preparation and FMO setting.
To publicly disclose the IFIE data, we have started to comprehensively collect IFIE data andconstruct IFIE-database (Figure 1). However, there are limits to prepare huge amount of various
protein structure data (e.g. PDB having ca. 40,000 entries
of protein-ligand complex) by manual labor. Thus, wedeveloped “semi-automated FMO calculation protocol” toefficiently carry out structure preparation and subsequent
FMO calculations for multiple X-ray crystal structures. To
validate the FMO protocol, we manually prepared protein
structure and carry out FMO calculations using the same
crystal structures. As a result, IFIE values of the FMO
protocol data showed good agreement with those ofmanual labored data. Therefore, we confirmed that thisprotocol can be used for structure preparation and FMO
calculations of massive protein structures. There are atpresent the IFIE data of 300 complexes (e.g. ER, ER,
p38-MAP kinase, Aurora, CHK1) obtained from the FMO
protocol. They were registered in newly developed
IFIE-database with GUI. This research was done inactivities of the FMO drug design (FMODD) consortium[http://eniac.scitec.kobe-u.ac.jp/fmodd/index.html].
The
results of FMO calculations were obtained using the K
Figure 1 IFIE-database
computer (project ID: hp170183).

P2-08The Role of Water Molecules in Protein-Ligand Binding:Fragment Molecular Orbital Calculations on the
Complexes of Renin with its Inhibitors
Yoichiro Yagi 1

Takatomo Kimura 2

yagi@ee.ous.ac.jp

kimura@konankako.co.jp

Chiduru Watanabe 3

Yoshio Okiyama 4

chiduru.watanabe@riken.jp

okiyama@nihs.go.jp

Shigenori Tanaka 5

Teruki Honma 3

Kaori Fukuzawa 6

tanaka2@kobe-u.ac.jp

honma.teruki@riken.jp

k-fukuzawa@hoshi.ac.jp

1

2
3

4
5

6

Department of Electrical and Electronic Engineering, Okayama University of Science,
1-1 Ridai-cho, Kita-ku, Okayama 700-0005, Japan
Konan Chemical Industry Co., Ltd., 5-12 Nakagawa-cho, Takatsuki, Osaka 569-0066, JapanRIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
National Institute of Health Sciences, 1 -18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501, Japan
Department of Computational Science, Graduate School of System Informatics,
Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan

Keywords: Fragment Molecular Orbital (FMO) Calculation, Renin, Inhibitor, Water molecules,
Interaction energy, Binding energy, Pair interaction energy decomposition analysis (PIEDA)
We have carried out the fragment molecular orbital (FMO) calculations on the 22 different
renin-inhibitor complexes at FMO2-MP2/6-31G* level to clarify a relationship between thecalculated binding energy for the renin-inhibitor complexes and the activity value on 50%
inhibitory concentration (IC 50 ) of the inhibitors. The PDB structures of complexes with TIP3P
model waters around 8Å of complex were optimized by an energy minimization using AMBER.
After energy minimization, TIP3P model waters were removed and FMO calculations using
MIZUHO/BioStation program were performed on the K computer [1]. FMO computations
indicated that the binding energy can be correlated to IC 50 . In the FMO calculation that contains
crystal waters within 5Å of ligand, the binding energies showed a good correlation with IC 50 foreach inhibitor [2]. The aim of this study is to elucidate a role of water molecules in binding ofrenin and inhibitor. By analyzing of the interactions between ligand and water molecules andbetween water molecules and amino acid residues in protein, we found that ligand interacts withprotein through the water molecules. This result suggests that the water molecules may support thebinding of ligand in active site of protein. In addition, we performed pair interaction energy
decomposition analysis (PIEDA) calculation to examine the energy components.
This research was done in activities of the FMO drug design consortium (FMODD). The results
were obtained using the K computer (project ID: hp170183).
[1] Tanaka, S., Mochizuki, Y., Komeji, Y., Okiyama, Y., and Fukuzawa, K., Electron -correlated
fragment-molecular-orbital calculations for biomolecular and nano systems, Physical Chemistry
Chemical Physics, 16:10310-10344, 2014.
[2] Yagi, Y., Kimura, T., Watanabe, C., Okiyama, Y., Tanaka, S., Honma, T., and Fukuzawa, K.,
Fragment Molecular Orbital Calculations on the Interaction and Binding Energies of Human
Protease Renin and its Inhibitors, Chem-Bio Informatics Society (CBI) Annual Meeting 2016 ,P1-08, 2016.

P2-09Using multiple molecular fingerprints for improvement ofdrug activity predictions
Yusuke Matsuyama 1,3

matsuyama@cb.cs.titech.ac.jp

Takashi Ishida 2,3

ishida@c.titech.ac.jp

1

Department of Computer Science, Tokyo institute of technology, Meguro, Tokyo, Japan

2

School of Computing Department of Computer Science, Tokyo institute of technology, Meguro, Tokyo, Japan

3

Education Academy of Computational Life Sciences, Tokyo Institute of Technology, Meguro, Tokyo, Japan

Keywords: Virtual screening; Machine learning; Molecular fingerprint; Stacking; Ensemble
learning
Drug activity prediction based on machine learning method is useful for ligand-based virtual screening.
And it is drawing attention as a key to keep down drug discovery costs which are said to be 300
billion[1] and still increasing now.
When using machine learning technique, the information of molecule have to be converted into avector with fixed length.
For many years, huge number of molecular fingerprints are proposed, however, it is known that thebest fingerprint is different for the target[2], and it is difficult to select the most suitablefingerprint.
To solve this problem, we propose a new technique to use multiple fingerprint by emsemble
learning for drug activity prediction.
As a result of performance evaluation, the proposed method greatly increased the predictedperformance compared to the prediction model using single molecular fingerprint.
In addition, we compared the feature and prediction performance of major molecular fingerprints,
and analyzed the reason why our ensemble method works well.

[1] Mullard, A.: New drugs cost US$2.6 billion to develop. Nature reviews. Drug discovery 13(12),877 (2014). doi:10.1038/nrd4507[2] Riniker, S., Landrum, G.A.: Open-source platform to benchmark fingerprints for ligand-based
virtual screening. Journal of Cheminformatics 5(5) (2013). doi:10.1186/1758-2946-5-26

P2-10K 4 : An in silico Drug Design Suite combined with HPCs
Biao Ma 1,2

Yuta Isaka 1,2
isaka@fbri.org

biao-ma@fbri.org

Mitsugu Araki 3,4

Hiroaki Iwata 3

araki.mitsugu.6w@kyoto-u.ac.jp

iwata.hiroaki.3r@kyoto-u.ac.jp

Yasushi Okuno 2,3,4
okuno.yasushi.4c@kyoto-u.ac.jp
1

2

3

4

Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundationfor Biomedical Research and Innovation (FBRI) , 1-6-5, Minatojima-Minamimachi Chuo-ku,
Kobe 650-0047, Japan
Simulation-Driven Drug Discovery Group, IBRI laboratory, Foundation for Biomedical
Research and Innovation (FBRI), 1-6-5, Minatojima-Minamimachi Chuo-ku, Kobe 650-0047,
Japan
Graduate School of Medicine, Kyoto University, 53 Kawahara-cho, Shogoin Sakyo-ku, Kyoto
606-8507, JapanRIKEN Advanced Institute for Computational Science, 7 -1-26 Minatojima-Minamimachi,
Chuo-ku, Kobe, Hyogo 650-0047, Japan

Keywords: SBDD, Molecular dynamics simulation, Docking simulation, Virtual screening,
Binding free energy calculation, MP-CAFEE, HPCs, K-computer, Graphical user interface, GUIIn drug discovery process, computational methods to efficiently explore and optimize drug candidates
are strongly required. Since virtual screening from huge amount of chemical compounds (> 10 60 ) andaccurate prediction of the protein-drug binding affinity need a large computational cost, we attempt toovercome this problem by utilizing high-performance computers (HPCs) such as K-computer, Osaka
Univ VCC, and so on. However, in silico drug discovery on HPCs requires complicated operation. To
solve this problem, we have developed an in silico drug design suite, K 4 (K-Force), which has a friendly
graphical user interface (GUI). The medicinal chemists can easily utilize computational power ofK-computer and other HPCs.
In K4 , we constructed a single platform that integrates the following main functions: 1) protein
structure sampling using temperature replica-exchange molecular dynamics (T-REMD), 2) docking
simulation (DS) or virtual screening (VS) based on rDock[1], 3) Ab initio calculation of compound
partial charges based on GAMESS, 4) the protein-compound free energy calculation based onmolecular dynamics (MD) simulation using MP-CAFEE[2,3] or MM-PB/GBSA method.
We will provide more methods for rational design of drug molecules in next version, such asprotein-ligand inter-fragment interaction energy calculation(IFIE) by use of fragment molecular orbital(FMO) method and ensemble docking, which enables to dock a chemical library against multiple
receptor conformations. If you are interested into our applications suite, please visit KOBE-CITY andFBRI booth.

[1] Ruiz-Carmona, S., Alvarez-Garcia, D., Foloppe, N., Garmendia-Doval, A. B., Juhos, S.,
Schmidtke, P., Barril, X., Hubbard, R. E., and Morley, S. D. rDock: A Fast, Ver satile and Open
Source Program for Docking Ligands to Proteins and Nucleic Acids. PLoS Comput. Biol. (Prlic,
A., Ed.) 10, e1003571, 2014.
[2] Fujitani, H., Tanida, Y., and Matsuura, A. Massively parallel computation of absolute binding
free energy with well-equilibrated states. Phys. Rev. E 79, 21914 , 2009.
[3] Araki, M., Kamiya, N., Sato, M., Nakatsui, M., Hirokawa, T., and Okuno, Y. The Effect ofConformational Flexibility on Binding Free Energy Estimation between Kinases and Their
Inhibitors. J. Chem. Inf. Model. 56, 2445–2456, 2016.

P2-11
ReCGen: Development of a novel approach to generate
virtual compounds

1

Chisato Kanai 1

Edmund Taylo 1

kanai@k-ct.jp

taylo@k-ct.jp

Kyoto Constella Technologies Co., Ltd., fourth floor Kyozome Kaikan,
481 Tourouyama-cho, Nakagyo-ku, Kyoto City, 604-8225, Japan

Keywords: de novo design, ECFP, RECAP, synthetic accessibility
It is widely accepted th at on e of the importan t e le men tal tech nologies o f de novo
design [1] is the creation of no vel structu res. To date, various meth ods have been
pro po sed which are roughly classified into the following th ree types: atom-based
method, fragment-base d meth od, an d ch emical reaction (syn thetic route)-based
method. The most co mmon type is fragment-based stru cture gen eration method
wh ich is represen ted by those u sin g RECAP [2], BRICS [3] and MMPA (Match ed
molecu lar pair analysis) [4]. RECAP, in particu lar, is a type of re trosynth etic
analysis method focusing on che mical reaction s frequen tly used in drug discove ry.
Th e structures gen erated by this meth od are relatively easy to synthe size, so th e
method is considered good for de novo design .
We have recently developed a n ew fragmen t-based method that utilizes the con ceptof ECFP [5], a type of fin gerprin t, in the generation of new fragme nts that aresubse qu ently used as building blocks in the assembly of new stru ctures. In our
method, the size of ECFP fragments can be set arbitrarily by specifying th e diameterof the graph, so a wide variety of fragmen ts can be obtain ed as compared with the
RECAP method. Therefore, compared to RECAP, wh ich is subject to rough structu raltransformation, relatively fine stru cture transformation is possible wh en usin g our
method. Furthermore, synthetic accessibility (SA) [6] of structures generated by our
method is as favorable as that generated using the RECAP method. Because of thosefeatures, we refer to it as “Refined Compound Generator (ReCGen)”.
In this poster we present a co mparison between the prin ciple of our metho d and thefeature s of RECAP method.

[1] Schneider G. and Fechner U., Nature Reviews Drug Discovery 4:649–663, 2005.
[2] Lewell, X.Q., Judd, D.B., Watson, S.P. and Hann, M.M., J. Chem. Inf. Comput. Sci. 38:511-522,
1998.
[3] Degen J, Wegscheid-Gerlach C, Zaliani A and Rarey M., ChemMedChem, 3:1503-1507, 2008.
[4] Kenny P. W., Sadowski J., “Structure Modification in Chemical Databases”. In
Chemoinformatics in Drug Discovery. Oprea, T. I., Ed. Wiley-VCH: Weinheim, Germany.
pp. 271−285, 2004.
[5] David R. and Mathew H., J. Chem. Inf. Model. 50:742-754, 2010.
[6] Fukunishi Y., Kurosawa T., Mikami Y. and Nakamura H., J. Chem. Inf. Model. 54:3259-3267,
2014.

P2-12

Quick and precise homology modeling method of GPCRs
Mika Nabeno 1
nabeno.mika@mv.mt-pharma.co.jp

1

2

Takatsugu Hirokawa 2
t-hirokawa@aist.go.jp

Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi
Tanabe Pharma Corporation, 1000 Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033,
Japan
Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai,
Tsukuba, Ibaraki, 305-8575, Japan

Keywords: GPCR, Homology modeling, Extracellular loop II, Ligand docking, Molecular
dynamics simulation
G protein-coupled receptors (GPCR) are major drug discovery targets, and the reports on the
X-ray crystal structures of GPCRs, which are rapidly increasing in recent years, are very useful for
GPCR-targeted drug design. However, because X-ray crystallography of GPCRs is stillchallenging, we cannot acquire the structures complexed with compounds at the time when they
can be utilized in drug design effectively. Therefore, the needs of homology modeling are high at
the drug discovery field. A number of modeling methods of GPCRs have been studied to date 1 .
Particularly in recent years, there is a tendency to perfor m a large amount of calculation to build ahighly accurate model 2 . However, in the drug discovery research, we often have multiple
compounds to be modeled, and the predicted models need to be revised according to SAR ofcompounds. In addition to that, when a new X-ray crystal structure more suitable for a template isrevealed, the homology models have to be built from scratch. For the reasons shown above, it isdesirable that the modeling method be not only highly accurate but also simple.
In this study, we propose the quick and precise homology modeling method in the case of thecompound binding to the orthosteric site of GPCR. The site is covered with the extracellular loop
II (ECL2), which affects binding of the compound seriously. However, because of its diversity
among GPCRs, its modeling is generally difficult. Here, a multi -template method was adopted
which selected different templates for transmembrane(TM) region and ECL2. We found out thatthe accuracy of modeling is improved by selecting one templat e for the TM region with highsequence homology and another template for the ECL2 with almost the same number of amino acidresidues. In drug design, although the prediction of the docking pose of the compound is quite vital,
it is the most difficult step in GPCR modeling. Then, we built an easy method to narrow down thepossible docking poses after searching them in a wide range. It was also confirmed that theaccuracy of the models was remarkably improved by performing molecular dynamics simulationfor 50 ns on finishing of the obtained models. In our validation, the RMSDs of the bound ligands
predicted by the above method were less than 2 Å from those of the X-ray crystal structures.
Although further verification is necessary, this method can be promising for drug design.
[1] Koehler Leman, J., Ulmschneider, M. B., Miller, and W., Gray, J.J., Computational modeling of
membrane proteins, Proteins, 83(1):1-24, 2015.
[2] Rodríguez, D., Ranganathan, A., Miller, W., and Carlsson, J., Strategies for Improved Modelingof GPCR-Drug Complexes: Blind Predictions of Serotonin Receptors Bound to Ergotamine ,Journal of Chemical Information and Modeling, 54:2004-2021, 2014.

P2-13

KampoDB: An integrated platform for mode -of-action
analysis and repositioning of natural medicines
Ryusuke Sawada 1
rsawada@bioreg.kyushu-u.ac.jp

Masahiro Umezaki 2

Michio Iwata 1
m-iwata@bioreg.kyushu-u.ac.jp

Yoshihiko Usui 2

masume@inm.u-toyama.ac.jp

Takaki Kubono 3
m1661217@ems.u-toyama.ac.jp

Makoto Kadowaki 3
makotok@inm.u-toyama.ac.jp

Yoshihiro Yamanishi 1,4
yamanishi@bioreg.kyushu-u.ac.jp

1

2

3

4

Division of System Cohort, Multi-scale Research Center for Medical Science, Medical Instituteof Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582,
Japan.
Division of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama,
Toyama 930-0194, Japan.
Division of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of
Toyama, Toyama 930-0194, Japan.
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332 -0012, Japan.

Keywords: Kampo medicines, functional analysis, target prediction, Kampo repositioning
Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment ofmultifactorialandchronic
diseases.
Here,
we
present
KampoDB(http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel database of natural medicines, which
provides various useful scientific resources on Japanese traditional formulas Kampo medicines,
constituent herbal drugs, constituent compounds, and target proteins of these constituent
compounds. Potential target proteins of these con stituent compounds were predicted by docking
simulations and machine learning methods based on large -scale omics data (e.g., genome,
proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo
medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369
potential target proteins, and has functional annotations for biological pathways and molecularfunctions. KampoDB is useful for mode -of-action analysis of natural medicines and prediction of
new indications for a wide range of diseases.

P2-14Integrated multiomics analysis for dengue hermorrhogic
fever
Takayuki Amemiya 1
takayuki-amemiya@aist.go.jp

1

2

M. Michael Gromiha 1,2
gromiha@iitm.ac.in

Katsuhisa Horimoto 1

Kazuhiko Fukui 1

k_horimoto@aist.go.jp

k-fukui@aist.go.jp

Molecular Profiling Research Center for Drug Discovery (molprof), National Institute ofAdvanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan
Department of Biotechnology, Indian Institute of Technology Madras, Tamilnadu 600 036,
India

Keywords: Multi-omics analysis, Drug repositioning, Dengue hermorrhagic fever
Due to recent advances in the development of various kinds of high throughput
technology in life sciences, it is possible to collect and analyze multiple "omics" data.
Omics analysis as integrative system biology has been studied for drug discovery,
biomarker and assessment of their toxicity and efﬁcacy. Here, a multi-omics approach
is applied for dengue hermorrhagic fever (DHF) characterized by vasculopathy, which
results in sudden plasma leakage that reduces the blood volume and may result inhypovolemic shock, known as dengue shock syndrome [1]. However, there are no
approved drug, DHF is classified as a neglected tropical disease by WHO.
In our analysis, three layers of experimental data such as transcriptomic, proteomic
and interactomic data are considered. In each layer, we obtained the signature genes
and proteins to find the drug candidates and the significant biological pathway [2]-[4].
The effective drug candidates are identified by using a drug repositioning method [5] to
inverse drug-disease relationship in patients and healthy control gene expression
profile and using interactomic relationships between signature proteins and chemicalcompounds. We will present the integrated multi-omics analysis for DHF, which leads
to identify and narrow down drug candidates based on expression profiles andmolecular interaction.

[1] D. J. Gubler, Dengue and dengue hemorrhagic fever., Clin. Microbiol. Rev., 11:480–96, 1998.
[2] J. Lamb, The Connectivity Map: Using Gene-Expression Signatures to Connect Small
Molecules, Genes, and Disease ,,Science, 313:1929–1935, 2006.
[3] A. Subramanian et al., Gene set enrichment analysis: a knowledge-based approach forinterpreting genome-wide expression profiles., Proc. Natl. Acad. Sci. U. S. A., 102:15545–15550,
2005.
[4] D. Szklarczyk et al., STITCH 5: Augmenting protein-chemical interaction networks with tissue andaffinity data, Nucleic Acids Res., 44:D380–D384, 2016.
[5] Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small
molecules, genes, and diseases, Science, 313: 1929-1935, 2006.

P2-15Development of Structure-Free Design and Analysis for
Compound Libraries Based on Chemical and Bioactive
Diversity
Yugo Shimizu 1

Kazuyoshi Ikeda 1

shimizu-yg@pha.keio.ac.jp

ikeda-kz@pha.keio.ac.jp

Masanori Osawa 1
osawa-ms@pha.keio.ac.jp

1

Keio University, Faculty of Pharmacy, Division of Physics for Life Functions, 1-5-30
Shibakoen, Minato-ku, Tokyo 105-8512, Japan

Keywords: Library design, Structure-blinded analysis, Bioactivity space
High through screening is a starting point for drug discovery in many pharmaceutical companies.
To expand collaborative drug discovery between industry and academia, the Drug-discovery
Innovation and Screening Consortium (DISC) of over 20 pharmaceutical companies in Japan has
been established [1]. DISC generated a library that consists of compounds collected from theattending companies, but diversity of compound structures in the library is unknown due to theintellectual property, and therefore, it is necessary to analyze the compound library without
compound structure. For this purpose, we started a project, “Structure-Free Compound-Library
Diversity Analysis”, supported by Japan Agency for Medical Research and Development to develop
a computational system for diversity analysis of large-scale structure-blinded compound library.
In consideration of security, we adopted a secure cloud-based web system for collecting
physicochemical descriptors and fingerprints of the compounds from companies, and Pipeline Pilot
was used as a platform for manipulating and calculating large-scale compound data. An automatedprotocol for converting 2D/3D molecular descriptors of physicochemical property including
principal moment of inertia and lead/drug-likeness score was developed for subsequent analysis ofcompound library diversity. The developed protocol can compare chemical spaces of small-molecule
libraries from approved drugs, candidates, commercial compounds, and patent compounds fromChEMBL database [2]. We also developed a method for analyzing coverage of bioactive space. We
extracted compounds in the ChEMBL database with bioactivity information for over 1,400 targets.
Models for each target were created by several machine learning (ML) methods such as naïve Bayes,
random forest, support vector machine, and deep neural networks using the descriptors andfingerprints. The prediction performance of each ML method was tested by cross-validation and thebest one was used for predicting the bioactivity of compounds from large-scale libraries to evaluate
the distribution of diversity in bioactive space.
[1] http://www.amed.go.jp/program/list/06/03/05.html[2] Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A.,
Light, Y., Mak, L., McGlinchey, S., Nowotka, M., Papadatos, G., Santos, R., and Overington, J.
P., The ChEMBL bioactivity database: an update, Nucleic Acids Research, 42(Databaseissue):D1083-1090, 2014.
This work is conducted as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).

P2-16Developing novel inhibitors of
Heme-copper oxygen reductases
Yuya Nishida 1
nishida@medbio.med.osaka-u.ac.jp

Yasunori Shintani 1yshintani@medbio.med.osaka-u.ac.jp

Hitomi Yuki 2

Teruki Honma 2, 3

hitomi.yuki@riken.jp

honma.teruki@riken.jp

Seiji Takashima 1
takasima@cardiology.med.osaka-u.ac.jp12

3

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, JapanTokyo Institute of Technology, School of Computing, Department of Computer Science,
4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan

Keywords: Heme-copper oxygen reductase, cytochrome c oxidase,
allosteric inhibitor, ligand-based virtual screening
Heme-copper oxygen reductase (HCO) is a family of transmembrane proteins that work as amember of the respiratory electron transport chain. HCO receives electrons from cytochrome c or
ubiquinol and then reduces oxygen to water, which couples with proton transfer across membrane.
The generated transmembrane difference in proton concentration is a major part of proton-motive
force, which is a driving force of ATP synthase. These reactions are crucial in most organisms,
therefore the composing proteins are considered as drug targets. Here we describe inhibitors of two
HCOs: mitochondrial cytochrome c oxidase (mtCcO) and bacterial ubiquinol oxidase (UqO).
Recently, we identified novel allosteric inhibitors of mtCcO, and successfully determined crystal
structures of mtCcO in complex with two inhibitors. The structures showed that both inhibitors
bound to the same position of mtCcO, which is far from the ligand-binding site. This suggests that
mtCcO activity is inhibited by a novel allosteric mechanism. In addition, as comparing mtCcO
with bacterial UqO, the inhibitor binding site in mtCcO has some similarity to the homologous
position in bacterial UqO, though mtCcO is much larger complex than bacterial UqO. These led us
to hypothesize that the novel allosteric site has a potential to be a novel antibiotic target. To
elucidate the inhibitory mechanism by approach of the structure-activity relationship and todevelop a novel inhibitor targeting bacterial UqO, we performed ligand developments with the
inhibitors we found. By using of the ligand-based virtual screening system LAILAPS incommercially available approximately 70,000,000 compounds, 270 compounds were selected andsubsequently, we assessed these inhibitory activities against mtCcO and bacterial UqO. As resultswith mtCcO, 17 compounds had IC50 less than approximately 10 uM, and the most improved
compound had IC50 of 0.1 uM, which is one-order better than that of the query inhibitor. Moreover,
the result of the assay with bacterial UqO from Escherichia coli showed that 13 compounds had
IC50 less than approximately 100 uM, and above all, the most effective compound had IC50 lessthan 10 uM. In conclusion, we succeeded to obtain various novel inhibitors of both mtCcO andbacterial UqO, which can be subsequently applied to the structure-activity relationship analysis
and developing a novel antibiotic.

P2-17The Contribution of Entropy in Drug-Protein Binding
Takeshi Tanaka

Ken Ikeda

tanaka@interprotein.com

ikeda@interprotein.com

Hirotsugu Komatsu

Takao Matsuzaki

komatsu@interprotein.com

tak-matsuzaki-45@cb3.so-net.ne.jp

Interprotein Corporation, SIC-2-205, 5-4-30 Nishihashimoto, Midori-ku, Sagamihara, Kanagawa, 252-0131, Japan

Keywords: in Silico Screening, Protein-Protein Interaction, Small Molecule Inhibitor, Entropy, Drug Design
The entropy change of drug-protein binding can now be observed by Isothermal Titration Calorimetry (ITC).

The

reported values of the entropy change of drug-protein binding range up to 10 kcal/mol, which consists significant portion offree energy change.
binding reaction.

The entropy effect for drug-protein binding can be positive or negative depending on the nature of

In either case, drug-protein binding can not be predicted correctly without considering entropy change.

This seems to be the main reason why conventional docking methods fail in most cases.

We have developed a system

which consider entropy contribution and concentrates compounds with high total energy into higher rank.
successfully applied for 20 PPI targets and 10 enzyme targets.

The details about Runx1-CBFβ are shown.

It has beenP2-18

Evaluation of incorporation of protein flexibility forcomputational structure-based drug design
Noriaki Okimoto 1

Yoshinori Hirano 1

Shigeo Fujita 1

okimoto@riken.jp

hirano@riken.jp

shigeo.fujita@riken.jp

1

Makoto Taiji 1
taiji@riken.jp

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

Keywords: molecular dynamics simulation, molecular docking, binding pocket, ligand binding
A large number of available therapeutic drugs target protein molecules. Proteins fluctuate
dynamically under physiologic conditions, and this flexibility is essential for ligand binding, a key
protein function. Apparent conformational changes upon ligand binding have been observed inseveral pharmacologically relevant proteins. Although the importance of the effects on protein
dynamics and solvent water molecules is widely recognized, many computational methods forstructure-based drug design do not fully consider related effects. Thus far, there have been
considerable efforts to incorporate these effects on protein flexibility and solvent water molecules
into molecular docking. Here, we focus on the effect on the incorporation of protein flexibility formolecular docking. The protein flexibility was represented by the use of the multiple receptor
structures obtained from the computational approaches. The computational approaches including
molecular dynamics (MD) simulations are used to explore the conformations of the receptor
protein before molecular docking.
In this study, tankyrase-ligand systems 1,2 were evaluated for the effect of incorporation ofprotein flexibility. First, we utilized several MD-based sampling techniques to obtain multiple
protein structures. Furthermore, we try to select characteristic druggable binding pocket structures.
Finally, we evaluate performance of molecular docking using these multiple conformations. We
will report the detailed results.

Typical crystal structures of tankyrase protein:
The molecular surface representations indicate the binding pockets.
References
1.

Karlberg, T.; Markova, N.; Johansson, I.; Hammarstrom, M.; Schutz, P.; Weigelt, J.;

Schuler, H. J Med Chem 2010, 53(14), 5352-5355.
2.

Narwal, M.; Venkannagari, H.; Lehtio, L. J Med Chem 2012, 55(3), 1360-1367.

P2-19Computer simulation-based prediction of drug-induced
arrhythmia by evaluating repolarization reserve
Shingo Murakami

Taichiro Tomida

Yoshinori Mikami

shingo.murakami@med.toho-u.ac.jp

tomida@med.toho-u.ac.jp

ymikami@med.toho-u.ac.jp

Masanori Ito

Satomi Adachi-Akahane

itomasanori@med.toho-u.ac.jp

satomiaa@med.toho-u.ac.jp

Department of Physiology, School of Medicine, Toho University, 5 -21-16
Omori-Nishi, Ota-ku, Tokyo 143-8540, Japan

Keywords: Drug induced arrhythmia. Action potential simulation, Repolarization reserve
Some of drugs under development and in clinical use turned out to induce arrhythmia and werewithdrawn from the market. The drug-induced arrhythmia is mainly accounted for by prolongationof action potential duration (APD) due to a block of one type of repolarizing K + currents in cardiac
myocyte called I Kr . Therefore, time-consuming and costly in vitro hERG screenings are conducted
for all candidate compounds. However, it turned out that in vitro hERG screening also excludes
potentially useful drug candidates that may not induce arrhythmia. The risk of drug-induced
arrhythmia differed among various I Kr blockers because normal repolarization of membranepotential in cardiac myocytes is determined by multiple and redundant mechanisms . The
compensation of the repolarizing component by other currents is called repolarization reserve,
which has been used only for a qualitative explanation to account for different risks among drugs.
To assess the risk of drug-induced arrhythmia, we redefined the repolarization reserve andexamined if the quantified repolarization reserve can be used to predict the APD prolongation
caused by various drugs. In the present study, we developed a method for quantification of
repolarization reserve. We redefined repolarization reserve as a source of repolarizing currents thatare activated during prolonged depolarization. By using human ventricular myocyte si mulation
models, we searched a novel quantification method based on this definition. We calculated a newquantitative index of repolarization reserve by simulating action potential (AP) clamp withprolonged AP waveforms and the human ventricular myocyte models.
We verified the effectiveness of this method by using the O’Hara and Rudy model of humanventricular myocytes. The calculated index quantitatively accounted for prolongatio n of APD
under various conditions in cardiac myocytes. We also confirmed that the quantified repola rization
reserve could be used to predict the prolongation of APD by different drugs. The proposed index of
repolarization reserve is expected to contribute to further understanding of APD prolongati on andpredicting the risk of drug-induced arrhythmia.

P2-20

Ligand binding site analysis with protein flexibility forfragment to lead optimization
Masa-aki Ito 1
31343u@ube-ind.co.jp

1

2

3

Takatsugu Hirokawa 2, 3
t-hirokawa@aist.go.jp

Pharmaceuticals Research Laboratory, UBE INDUSTRIES,LTD.,
1978-5,kogushi,Ube,Yamaguchi 755-8633,Japan
Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced
Industrial Science and Technology,
2-4-7 Ao mi, Koto-ku, Tokyo 135-0064, Japan
Division of Biomedical Science, Faculty of Medicine, University of Tsukuba,
1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305-8575, Japan

Keywords: Molecular dynamics, Drug design, Fragment to lead optimization

With the development of increasing number of protein structure determination, pharmacological anddrug discovery research based on the structural biology data have been accelerated. However, someof experimental structural data may be inconvenient to use for drug discovery due to the constraints on
crystallization conditions or undesirable biological state. In silico analysis including molecular
dynamics (MD) simulation can help to bridge the gap between original state of experimental structural
data and drug discovery oriented structural models. In case of ligand biding analysis, a variety of
pocket detection algorithms are available for the analysis of static protein structures. However, sinceproteins are dynamic entities. Static structures represent a serious limitation to account for the
intrinsic plasticity of the binding pocket including pre-existing equilibrium, induced fit model, cryptic
and allosteric sites.
Characterization of cavities taking into account protein conformational
ensembles should be valuable.
In this work, we developed workflow of ligand binding site analysis using MD simulation andchemoinformatics: (1) generating conformational ensemble of the target protein using MD simulation
with 100ns time step based on the input structure of apo-form and fragment-bound form, (2) alphaspheres are detected on different pre-aligned conformation of protein and (3) analysis of the density ofsurrounding alpha spheres and frequency for fragment to lead optimization. For example, we applied
this workflow to evaluate the fragment to lead expansion by fragment growing on DPP4 protein. Twotype of MD simulation were performed, apo-form and fragment-bound form. As the result of MD
simulation based on input structure of apo-form, fragment and lead were overlapped with frequency
map at about 40 and 20%, respectively. We found the additional two grid spaces around fragment at
frequency map at 20% and lead functional group by fragment growing occupied one of them.
These results tell us if ligand design by fragment free approach, three types of lead shape will be
acceptable. These findings should prove useful in lead optimization from fragment by our approach.

P2-21A Genetic Approach to Deep Leaning in Prediction of
Molecular Properties

Yoshinori Wakabayashi 1
wakabayashi@by-hex.com

Tadaaki Mashimo 2,3
mashimo-tadaaki@aist.go.jp

Mitsuhito Wada 2

m-wada@aist.go.jp
1

BY-HEX LLP, 1-19-14 Shimizu Simizu, Suginami-ku, Tokyo 167-0033, Japan
Technology Research Association for Next generation natural products chemistry, 2-3-26 Aomi,
Koto-ku, Tokyo 135-0064, Japan
3
IMSBIO Co., Ltd., 4-21-1 Higashi-ikebukuro, Toshima-ku, Tokyo 170-0013 Japan
2

Keywords: myPresto, RDKit, Descriptor, Regression, Deep Learning, Deep Neural Network,
Genetic Programing, Auto-Building, Auto-Turning, Cloud
Deep learning, a branch of machine learning utilizing multiple processing layers, is rapidly
advancing many areas including image, text and voice recognition, and now being applied to drug
discovery and cheminfomatics. However, it is difficult to design deep neural networks (DNN) andto find optimal hyper-parameters. In this work, we propose an efficient method to automatically set
the number of layers and the initial value of the parameters of neural networks on the basis ofgenetic programming. To demonstrate how the automated method works well on predicting
chemical properties, DNN models were trained for estimating the octanol/water partition
coefficient. We used experimental LogP data retrieved from public database and linked those tomolecular fingerprints, topological properties and physicochemical properties. Cross-validation
experiments showed that the DNN-based models achieved sufficient accuracy in predicting LogP
values.

P2-22
Seed Generation by Integrating Computer-aided Drug
Design with Complementary Experimental Methods
Taiji Oashi 1,2Michihiko Suzuki 2

taiji.oashi@kyowa-kirin.co.jp

michihiko.suzuki@kyowa-kirin.co.jp

Osamu Saku 3

Yuki Takayama 2

osamu.saku@kyowa-kirin.co.jp

yuki.takayama@kyowa-kirin.co.jp

Yuichi Takahashi 2

Hikaru Miyagi 1

yuichi.takahashi@kyowa-kirin.co.jp

hikaru.miyagi@kyowa-kirin.co.jp

Kuniyuki Kishikawa 2
kuniyuki.kishikawa@kyowa-kirin.co.jp

Fumikazu Shinohara 4
fumikazu.shinohara@kyowa-kirin.co.jp

Jun-ichi Saito 5,6
jun.saito@kyowa-kirin.co.jp

1

2

3

4

5

6

Research Core Function Laboratories, Research Functions Unit, R&D Division, Kyowa Hakko
Kirin Co., Ltd 3-6-6 Asahi-machi, Machida-shi, Tokyo, 194-8533, Japan
Chemical Research Laboratories, Research Functions Unit, R&D Division, Kyowa Hakko Kirin
Co., Ltd 1188 Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan
Environment and Safety Section, Ube Plant, Production Division, Kyowa Hakko Kirin Co., Ltd
2547-3 Fujimagari, Ube-shi, Yamaguchi, 755-8501, Japan
Innovative Technology Laboratories, Research Functions Unit, R&D Division, Kyowa Hakko
Kirin Co., Ltd 3-6-6 Asahi-machi, Machida-shi, Tokyo, 194-8533, Japan
R&D Planning Department, R&D Division, Kyowa Hakko Kirin Co., Ltd 1-9-2 Otemachi,
Chiyoda-ku, Tokyo 100-0004, Japan
Open Innovation Department, R&D Division, Kyowa Hakko Kirin Co., Ltd 1-9-2 Otemachi,
Chiyoda-ku, Tokyo 100-0004, Japan

Keywords: Structure-based drug design, 3D-database screening, Druggability evaluation, Oralbioavailability prediction,
Structure-based drug design (SBDD) has been used to obtain the compounds that could fit thetarget site of interest, modulating the disease-associated biological process. Here we will present
how a combination of computational and experimental methods was utilized in the early seed
generation process by two examples; (1) identification of novel small-molecule inhibitor for a
specific target potentially involved in cancer-related disease via 3D-database screening and in vitro
enzyme inhibition assay, (2) discovery of potency-enhancing non-natural nucleotide for siRNA byusing 3D-database screening, NMR-based binding assay, X-ray crystallography, and in vitro siRNAknockdown experiment. For further improving the seed generation process, we will also discuss
key elements in the computational approach, including identification and druggability evaluationof putative ligand-binding site [1] and in silico screening as well as prediction of potential
bioavailability and druglikeness for compounds selected from database screen prior to the final
selection for experimental assay [2] .
[1] Zhong, S., and MacKerell, A.D. Binding response: a descriptor for selecting ligand binding site
on protein surfaces. Journal of Chemical Information and Modeling, 47:2303-2315, 2007.
[2] Oashi, T., Ringer, A.L., Raman, E.P., and Mackerell, A.D. Automated selection of compoundswith physicochemical properties to maximize bioavailability and druglikeness. Journal ofChemical Information and Modeling, 51:148-158, 2011.

P3-01

A Graph Genome Browser
Toshiyuki Yokoyama

Yoshitaka Sakamoto

tfdvrt@gmail.com

yafdc1115@outlook.jp

Yutaka Suzuki
ysuzuki@edu.k.u-tokyo.ac.jp

Masahide Seki
mseki@edu.k.u-tokyo.ac.jp

Masahiro Kasahara
mkasa@edu.k.u-tokyo.ac.jp

Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences,
The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa City, Chiba 277-8583, Japan

Keywords: NGS, Graph Genome, Structural Variations, Visualization, Genome Browser
BackgroundContinuous reduction in sequencing cost and continuous improvement in sequencing throughput
realized population-scale sequence data, which underscores the importance of the development ofgraph genome analysis for handling a large number of genomes with variations of different scales.
Since most part of the genomes is shared between individuals, we need to focus on differences inorder to minimize the computation time. Single-nucleotide variants or small insertions anddeletions (indels) called from those data can be handled more accurately by a graph genome. On
top of that, long-read sequencing enables us to more accurately identify large-scale structuralvariants (SVs) and fusion genes, which include duplications or indels as loops or branching andmerging against a reference genome. Graph genomes have been considered promising especially inhuman variation analysis.
Although graph genome analysis is inextricably linked to the visualization of graph genomes,
means to visualize graph genomes are yet to be developed. Existing genome browsers can visualize
linear genomes but not graph genomes. Existing tools for handling graph genomes at the humangenome scale cannot visualize genomes interactively and intuitively.
Results
We propose a graph genome browser for visualizing genome constructed by combining
mathematical graphs with biological annotations. We define a sequence graph as a bidirected graphcomposed of a set of multiple DNA sequences as nodes and the corresponding end-to-end
connections as edges. With these representations, we aim to visualize cancer genomes including
SVs or fusion genes through comparison with the reference genome using sequencing data
including long reads.
Conclusions
We implement a novel graph genome browser in order to provide an infrastructure for graphgenome analysis. Our graph genome browser enables genome researchers to interactively explore
biologically meaningful features observed as structures on a graph.

P3-02

Fragmentation prediction using neutral loss-fragment pairs

12

Hiroyuki Yamamoto 1

Hiroshi Tsugawa 2h.yama2396@gmail.com

hiroshi.tsugawa@riken.jp

Human Metabolome Technologies, Inc., 246-2 Mizukami Kakuganji, Tsuruoka, Yamagata
997-0052, JapanRIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan

Keywords: MS/MS Fragmentation prediction, Machine learning, Metabolomics
In metabolomics, unknown peaks that chemical structures have not been determined aresometimes selected as metabolomic biomarker candidates in disease associated biomarker
discovery. Recently, some chemoinformatics approaches using MS/MS spectra with the objectivewith structural elucidation of unknown peaks have been reported [1, 2]. In this study, we aimed to
predict fragmentation by using neutral loss-fragment pairs.
For 438 metabolites, we cut of all combination of chemical bonds in two step computations. The
disconnected structure pairs were compared with neutral loss and fragment pairs registered in
MassBank database [3], and matched pairs were assigned as positive sample s and unmatched pairs
as negative samples. For 424 metabolites, PubChem fingerprint for the disconnected structures wascomputed. A fragment feature vector was generated by the similar way as generation of the feature
vector from the compound pair in the prediction of the enzymatic reaction by Kotera et al . [4]. The
fragment feature vector was set as explanatory variable and matched or unmatched information asa binary response variable to make a prediction model of fragmentation. Regularized logistic
regression approaches of ridge logistic regression, elastic net and lasso were performed. A regularizedparameter was optimized by cross-validation, and the prediction accuracy was computed forindependent test set. A maximum value of area under the curve of the receiver observation curve for thetest set was 0.7839 by using logistic regression with lasso. We plan to examine to improve prediction
accuracy by using machine learning approaches such as deep learning in the future.
[1] Tsugawa, H. et al., Hydrogen rearrangement rules: computational MS/MS fragmentation andstructure elucidation using MS-FINDER software, Analytical Chemistry, 88(16):7946-58 (2016).
[2] Duhrkop, K. et al., Searching molecular structure databases with tandem mass spectra using
CSI:FingerID, PNAS, 112(41):12580-5 (2015).
[3] Horai, H. et al., MassBank: a public repository for sharing mass spectral data for life sciences,
Journal of Mass Spectrometry, 45(7):703-14 (2010).
[4] Kotera, M. et al., Supervised de novo reconstruction of metabolic pathways from metabolome -scale
compound sets, Bioinformatics, 29(13):i135-44 (2013).

P3-03

Target Genes and Pathways in Drug Developmentfor Rare Diseases in Clinical Trials
Ryuichi Sakate 1rsakate@nibiohn.go.jp

Akiko Fukagawa 1

a-fukagawa@nibiohn.go.jp

Masami Morita 2

morita-opir@jpma.or.jp

12

Masashi Suzuki 2

suzuki.masashi@me.mt-pharma.co.jp

Akifumi Matsuyama 1

akifumi-matsuyama@nibiohn.go.jp

National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi,
Ibaraki-shi, Osaka 567-0085, Japan
Office of Pharmaceutical Industry Research, Japan Pharmaceutical Manufacturers Association,
Nihonbashi Life Science Bldg., 2-3-11 Nihonbashi-honcho, Chuo-ku, Tokyo 103-0023, Japan

Keywords: Rare disease, Drug development, Clinical trial
Unmet medical needs in drug development for rare diseases are growing worldwide. In order tocontribute to the drug development and related studies, trends in clinical trials for 306 rarediseases (intractable diseases) designated by the Ministry of Health, Labour and Welfare Japan had
been analyzed [1-4]. In this study, further analyses were conducted to clarify the relation between
tested drugs and their target genes and pathways. As a result, it was found that, for 122 rarediseases, 287 genes in 109 pathways (KEGG) were targeted by 338 drugs (DrugBank). Informationof how drugs, genes and targets were shared by diseases was obtained. This information is
expected to facilitate drug repositioning and studies of pathogenic mechanism.
[1] Suzuki, M., Sakate, R., and Fukagawa, A., [Status of implementation of clinical trials on
intractable diseases designated in Japan], The Office of Pharmaceutical Industry Research News,
48:23-27. 2016[2] Suzuki, M., Sakate, R., and Fukagawa, A., [Implementers of clinical trials for the designatedintractable diseases], The Office of Pharmaceutical Industry Research News, 49:29-34. 2016[3] Suzuki, M., Sakate, R., and Fukagawa, A., [Connecting information of the designatedintractable diseases to databases], The Office of Pharmaceutical Industry Research News,
49:35-46. 2016[4] Suzuki, M., Sakate, R., and Fukagawa, A., [Launched drugs used for clinical trials of thedesignated intractable diseases], The Office of Pharmaceutical Industry Research News,
50:39-43. 2017

P3-04

Prediction of natural biosynthetic pathway
by reverse synthetic analysis
Tsubasa Matsumoto
matsumoto.t.ap@m.titech.ac.jp

Miyabi Hishinuma
hishinuma.m.aa@m.titech.ac.jp

Kohei Amano

amano.k.ac@bio.titech.ac.jp

Masaaki Kotera
maskot@bio.titech.ac.jp

School of Life Science and Technology Department of Life Science and Technology, Tokyo
Institute of Technology, 2-12-1, Oookayama, Meguro-ku, Tokyo, 152-8550, Japan

Keywords: Reverse synthetic analysis, Machine learning
The relationship between natural products and enzymes that synthesize them and their genes has been
extensively studied in the fields of synthetic biology, metabolic engineering, phytochemistry and thelike. However, the number of natural products whose metabolic pathways are unknown, and the numberof enzyme genes whose function is unknown are both increasing. So it is desired to develop a method
for exhaustively connecting these two types of information.
Most of the informatics methods on the metabolic pathway are gene expression level and metabolite
quantity for known metabolic pathways such as glycolysis system and Krebs cycle. On the other hand,
Kotera and colleagues have established the methods such as predicting an enzyme gene (or enzyme
protein) that catalyzes a specified reaction [1][2] or selecting a combination that can be a substrate /product pair of an enzyme reaction from tens of thousands of compounds [3][4] .
In this study, we develop a chemical informatics method for predicting the biosynthetic pathway ofvarious natural products using large scale computer system and extracting biosynthetic genes. By
integrating reverse synthetic analysis and machine learning, we propose a framework to predict
chemical transformations that make up the unknown metabolic pathway and support the identificationof enzyme genes and enzyme proteins involved in it.
[1] Tabei Y, Yamanishi Y, Kotera M. Simultaneous prediction of enzyme orthologs from chemicaltransformation patterns for de novo metabolic pathway reconstruction. Bioinformatics, 32, i278-i287,
2016.
[2] Moriya Y, Yamada T, Okuda S, Nakagawa Z, Kotera M, Tokimatsu T, Kanehisa M, Goto S.
Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs. J
Chem Inf Model. 56(3):510-516, 2016.
[3] Yamanishi Y, Tabei Y, Kotera M. Metabolome-scale de novo pathway reconstruction using
regioisomer-sensitive graph alignments. Bioinformatics. 31(12):i161-i170, 2015.
[4] Kotera M, Tabei Y, Yamanishi Y, Tokimatsu T, Goto S. Supervised de novo reconstruction of
metabolic pathways from metabolome-scale compound sets. Bioinformatics, 29(13), i135-i144, 2013.

P3-05Construction of a Transcriptome-based Toxicity Prediction
Model of Adjuvants
Natsuko Kishishita 1,2,4
nkishishita@nibiohn.go.jp

Yoshinobu Igarashi 3
y-igarashi@nibiohn.go.jp

Hiroshi Yamada 3

Ken Ishii 1,2,4h-yamada@nibiohn.go.jp

kenishii@biken.osaka-u.ac.jp

1-3 1

4

Laboratory of Mockup Vaccine, 2 Laboratory of Adjuvant Innovation, and 3 Laboratory of
Toxicogenomics Informatics, National Institutes of Biomedical Innovation, Health andNutrition, Ibaraki, Osaka, 567-0085, Japan
Laboratory of Vaccine Science, Immunology Frontier Research Center, World PremierInternational Research Center, Osaka University, Suita, Osaka 565-0871, Japan

Keywords: Adjuvant, Toxicity, Transcriptome, Biomarker
Research and development of adjuvants is very active today to improve the efficacy of vaccines
against various diseases such as cancers and chronic disorders. Many adjuvants are ligands forpattern-recognition receptors that are important for innate immunity, and they skew immune
responses in a certain way to induce Th1- or Th2-type response, for instance. Many adjuvants aredeveloped over the world by pharmaceutical and venture companies as well as academia, and someof them are in clinical trials. And yet, it is not easy to evaluate the safety and efficacy of adjuvants
accurately in preclinical studies or clinical trials to avoid adverse reactions in humans.
We have constructed the Adjuvant Database (http://adjuvantdb.nibiohn.go.jp/) to evaluate safety
and efficacy of adjuvants with the grant from the Ministry of Health Labour and Welfare and
AMED. Toxicity tests with rats were performed to obtain gene expression profiles of major tissues
such as liver and kidney, and dataset were analyzed collectively with a large toxicogenomics
database developed by our institute (http://toxico.nibiohn.go.jp/). We set drugs and adjuvants withknown toxicity as “positive controls” to construct a toxicity prediction model based on gene
expression data of each tissue. In addition, we analyzed several blood factors including
conventional tissue-damage biomarkers (e.g., AST and ALT) and microRNAs to search for new
biomarkers. As a result, a toxicity prediction model determined reactogenic adjuvants as “toxic”
and those adjuvants that are clinically-approved or under clinical trials as “safe”. In fact, one of
the “toxic” adjuvant was known for liver toxicity, greatly affected a set of genes involved in cell
death and increased multiple tissue-damage biomarkers in the periphery blood. Meanwhile, “safe”
adjuvants did not show toxicity-related features as expected. We further plan to optimize a toxicity
prediction model to generalize molecular signatures of toxicity and aim to identify such toxicity
biomarkers that are applicable to humans.

P3-06Prediction of Biosynthetic Basic Parts ofCompounds with Complicated Structures
Kohei Amano 1

amano.k.ac@m.titech.ac.jp

Miyabi Hishinuma 1

hishinuma.m.aa@m.titech.ac.jp
1

Tsubasa Matsumoto 1

matsumoto.t.ap@m.titech.ac.jp

Masaaki Kotera 1

maskot@bio.titech.ac.jp

School of Life Science and Technology, Tokyo Institute of Technology, M6-2, 2-12-1
Ookayama, Meguro-ku, Tokyo, 152-8550, Japan

Keywords: Biosynthetic pathway analysis, Genetic Algorithm
Many pharmaceutical compounds such as antibiotics are derived from natural products.
Identification of their metabolic pathways is required not only for metabolic engineering but also
for synthetic biology. Natural product chemists estimate reaction pathways or identify enzyme
proteins on the basis of their own knowledge and experiences.
Most of the informatics methods on the metabolic pathway deal with known metabolic pathways
such as glycolysis system and Krebs cycle, and do not focus on identifying previously unknown
metabolism. On the other hand, Kotera and colleagues published the series of methods such aspredicting an enzyme gene or an enzyme protein that catalyzes a specified reaction [1][2] or selecting
a combination that can be a substrate / product pair of an enzyme reaction from tens of thousandsof compounds [3][4] .
We focused on the fact that living things make a lot of natural products from a limited number ofbasic parts. This study provides the method of predicting the biosynthetic basic parts from natural
products that contain complicated structures. We propose a new approach that we clarify the basic
metabolite in its metabolic pathway in advance to avoid combinatorial explosion. The database ofcompounds which can be candidates of the biosynthetic basic parts is prepared. These compounds
are supposed to be detectable by metabolome analysis. We virtually cut any chemical bonds
contained in the interesting compound, and decompose it into partial structures. We make several
evaluation functions with some molecular fingerprints and molecular similarities, and decide
which bonds are to be cut by comparing with the database. It is tested by genetic algorithm toadopt compounds which metabolic pathways are known. We believe our method can support theelucidation of biosynthetic pathway.
[1] Tabei Y, Yamanishi Y, Kotera M. Simultaneous prediction of enzyme orthologs from chemicaltransformation patterns for de novo metabolic pathway reconstruction. Bioinformatics, 32,
i278-i287, 2016.
[2] Moriya Y, Yamada T, Okuda S, Nakagawa Z, Kotera M, Tokimatsu T, Kanehisa M, Goto S.
Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs.
J Chem Inf Model. 56(3):510-516, 2016.
[3] Yamanishi Y, Tabei Y, Kotera M. Metabolome-scale de novo pathway reconstruction using
regioisomer-sensitive graph alignments. Bioinformatics. 31(12):i161-i170, 2015.
[4] Kotera M, Tabei Y, Yamanishi Y, Tokimatsu T, Goto S. Supervised de novo reconstruction of
metabolic pathways from metabolome-scale compound sets. Bioinformatics, 29(13), i135-i144,2013.

P3-07

A system level investigation to unravel mechanisms andpotential direct targets of the osteogenic small molecule
TH
Mochammad Ichsan 1
ichsan@hamfaro.or.id

1
2

3

Kosuke Kanke 2

Hironori Hojo 1,3

kankekosuke@gmail.com

hojo@tetrapod.t.u-tokyo.ac.jp

Ung-il Chung 1,3

Shinsuke Ohba 1,3

tei@bioeng.t.u-tokyo.ac.jp

ohba@m.u-tokyo.ac.jp

Department of Bioengineering, University of Tokyo, Tokyo 113-8656, Japan
Department of Sensory and Motor System Medicine, University of Tokyo, Tokyo 113 -0033,
Japan
Center for Disease Biology and Integrative Medicine, University of Tokyo, Tokyo 113 -8655,
Japan

Keywords: Kinase inhibition, Microarray, Molecular docking, Osteoblast differentiation, Reverse
docking
Direct reprogramming of human somatic cells such as dermal fibroblast s (DFs) into several cell
types without introduction of transcription factors or genetic manipulation has drawn muchattention. Although TH, an osteogenic helioxanthin -derivative, has been shown to act as a robust
small molecule inducing differentiation of several cell types into osteoblasts [1,2,3,4], detailed
mechanisms and direct targets of TH remain unclear. In the present study, we aimed to unravel
them in order to develop novel strategies to chemically reprogram DFs into osteoblasts.
Microarray analysis revealed 1,166 differentially expressed genes during an osteoblast induction
phase in TH-mediated stepwise differentiation of mouse embryonic stem cells (mESCs) into
osteoblasts. We also confirmed the increased expression of osteoblast differentiation-related genes,
such as Col1a1, Ibsp, Runx2 and Sp7, in the dataset. Furthermore, we identified focal adhesion,
ECM-receptor interaction, and PI3K-Akt signaling pathways as the most enriched pathways
modulated by TH. Bioactivity prediction showed that TH potentially acted as a kinase inhibitorwith the highest bioactivity score compared to other 22 kinase inhibitors obtained from
Osteogenics DB, an osteoblast-related molecule database which we newly developed . We thenidentified 145 potential targets of TH by pharmacophore- and structure-based reverse docking and
62 kinases, which were potentially inhibited during the osteoblast induction phase, by a kinase
enrichment analysis. By intersecting all identified potential targets of TH with 344 kinases which
are targeted by osteogenic kinase inhibitors in Osteogenics DB, we identified 42 proteins as themost potential direct targets of TH that fulfilled all criteria used in this study. Lastly, we performed
molecular docking analyses to predict binding affinity and binding specificity of TH against themost potential direct targets. Taken together, these findings suggest that TH achieves its osteogenic
activity at least partially via modulating the focal adhesion, ECM -receptor interaction, andPI3K-Akt signaling pathways and potentially acts as a kinase inhibitor against several kinases.
[1] Ohba S et al. Biochem Biophys Res Commun. 357(4):854 -60, 2007
[2] Nakajima K et al. Biochem Biophys Res Commun. 395(4):502 -8, 2010
[3] Kanke K et al. Stem Cell Reports. 2(6):751–60, 2014[4] Zujur D et al. Sci Adv. 3(5):e1602875, 2017

P3-08Large-scale diseasome analysis of gene expression
signatures toward understanding disease–diseaseassociations and drug discovery
Michio Iwata 1

Ryusuke Sawada 1

Yasuo Tabei 2

m-iwata@bioreg.kyushu-u.ac.jp

rsawada@bioreg.kyushu-u.ac.jp

yasuo.tabei@gmail.com

Yoshihiro Yamanishi 1,3
yamanishi@bioreg.kyushu-u.ac.jp

1

2

3

Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1
Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, JapanRIKEN Center for Advanced Intelligence Project, 2-1 Hirosawa, Wako, Saitama 351-0198,
Japan
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan

Keywords: diseasome, patients, gene expression, drug discovery, machine learning
Diseases are caused by dysfunction of the human biological system consisting of genes,
proteins, and pathways. The pathogenesis is basically considered disease-specific, butcharacteristic molecular features are often similar among patients of different diseases, suggesting
the commonality of the underlying molecular mechanisms. Most drugs are small compounds thatmodulate the activity of biological systems for the treatment of diseases. Thus, the knowledge onmolecular correlations among diseases can be useful for drug discovery. Diseases are characterized
by impaired expression of genes, and drugs modulate the activity of gene expression system. Thus,
the use of transcriptome data for diseases and those for drug candidate compounds is a rational
approach toward drug discovery. However, the integration of disease-specific and drug-induced
transcriptome data has been an open question.
In this study we performed a large-scale diseasome analysis of gene expression signatures for
79 diseases (e.g., adrenoleukodystrophy, leukemia, Alzheimer’s disease, asthma, atopic dermatitis,
breast cancer, cystic fibrosis, dengue and inflammatory bowel disease). We extracted characteristic
gene expression patterns that explain the specificity of each disease and commonality between
different diseases. We also proposed a novel machine learning method for drug discovery from theintegration of disease-specific and drug-induced transcriptome data using multi-layered diseasome
similarities computed from various molecular features (e.g., gene expressions, diagnostic markers,
disease-causing genes, disordered pathways, and environmental factors), and demonstrated theusefulness of the proposed method in terms of accuracy and applicability. The diseasome analysis
is expected to be useful for understanding disease-disease associations and for drug discovery.

P3-09Prediction of novel mitochondrial proteins having
disease-associated mutations toward discovery of drug
targetKenichiro Imai 1
kenichiro.imai@aist.go.jp

1

2

Yoshinori Fukasawa 2
y-fukasawa@aist.go.jp

Kentaro Tomii 2

Paul Horton 2

k-tomii@aist.go.jp

horton-p@aist.go.jp

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
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science andTechnology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan

Keywords: Mitochondria, Disease-associated mutations, Proteome, Transcriptome
Mitochondria provide numerous essential functions for cells and their dysfunction leads to avariety of diseases. Thus, obtaining a complete mitochondrial proteome should be a crucial step
toward understanding the roles of mitochondria in the diseases. Many mitochondrial proteins havebeen identified experimentally but a complete list is not yet available. To fill this gap, methods tocomputationally predict mitochondrial proteins from amino acid sequence have been developed
and are widely used, but unfortunately, their accuracy is far from perfect. We developed MitoFates[1], an improved prediction method for cleavable N-terminal mitochondrial targeting signals(presequences) and their cleavage sites. MitoFates is a Support Vector Machine-based predictor
and considers positively charged amphiphilicity, presequence motifs, position weight matrices
modeling the presequence cleavage sites and classical features such as amino acid composition andphysico-chemical properties. On independent test data, MitoFates attains better performance thanexisting predictors in both detection of presequences and in predicting their cleavage sites . In thisstudy, we used MitoFates to look for novel mitochondrial proteins involved in human diseases. We
obtained 83583 curated disease-associated variants from 6388 human diseases, which are collected
form UniProt, ClinVar [2], and the GWAS Catalog [3] using DisGeNET [4]. Then we analyzed
transcripts possessing the disease-associated variants using MitoFates and found more than 100
transcripts cording novel mitochondrial protein candidates involved in human diseases. We willreport the functional annotation of the candidates. Also we will discuss interpretation of relevance
between the candidates, the mutations and the diseases. The candidate list would be helpful inelucidating the role of mitochondria in health and disease.
[1] Fukasawa et al., MitoFates: improved prediction of mitochondrial targeting sequences and theircleavage sites, Mol Cell Proteomics, 14(4):1113-26, 2015
[2] Landrum M.J. et al., ClinVar: public archive of interpretations of clinically relevant variants.
Nucleic Acids Res., 44:D862–D868, 2016.
[3] Welter D. et al., I The NHGRI GWAS Catalog, a curated resource of SNP -trait associations,
Nucleic Acids Res., 42:D1001–D1006, 2014[4] Piñero J., et al., DisGeNET: a comprehensive platform integrating information on humandisease-associated genes and variants, Nucleic Acids Res., 45(D1):D833-D839, 2017

P3-10

Image-based phenotypic profiling associated with EGF
receptor trafficking and signal transductions reveals
novel mode of action of drugs
Kenji Tanabe
tanabe.kenji@twmu.ac.jp

Medical Research Institute, Tokyo Women’s Medical University, 8-1
Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan

Keywords: High contents screening, Mode of action of drugs, EGF receptor, small-molecule
inhibitors
Small-molecule compounds, including kinase inhibitors, are widely used as anti-cancer drugs.
However, as almost all small-molecule compounds have multiple targets, it may cause an
unexpected side effect and contribute to high attrition rate in clinical trials. Therefore, target
deconvolution of drug candidates is a critical theme for drug development. Although various
approaches are used for the target deconvolution, revealing a functionally unrelated target
molecule, such as non-kinase targets for kinase inhibitors, is difficult. Recently, we established animage-based compound profiling, and found an unexpected non-kinase target (microtubules) for amost uni-specific kinase inhibitor (Tanabe, Sci. Rep. 2016). This study applied the image-based
compound profiling for the target deconvolution of an inhibitor library, which consists of ~400
clinical drugs and various inhibitors. A549 cells, which express GFP-EGF receptor, were exposed
to inhibitors, stimulated by EGF and visualized several major signaling molecules. Cells were
photographed using automated fluorescent microscopy, and analyzed to extract ~500 image
features to quantitatively evaluate their phenotypes. These extracted image features were used toclassify inhibitors using unsupervised machine learning. As result, many clusters with unexpectedcombination of inhibitor were observed. For example, sodium channel inhibitor or transferase
inhibitor were classified with some PI3K-Akt inhibitors. Interestingly, biochemical assay showedthat they share target molecules associated with PI3K-Akt pathway. Similar observations wereobtained in other clusters including cytoskeleton inhibitors. This result indicates that thisimage-based compound profiling is a powerful tool to reveal mode of action of drugs.
[1] Tanabe, K., Image-based compound profiling reveals a dual inhibitor of tyrosine kinase andmicrotubule polymerization, Scientific Reports, 6:25095, 2016.

P3-11Analysis of “uncallable” regions in the human reference
genome associated with gene annotation and segmental
duplication
Shu Saikawa 15944563671@edu.k.u-tokyo.ac.jp
1

Masahiro Kasahara 2mkasa@edu.k.u-tokyo.ac.jp

Department of Computational Biology and Medical Sciences, Graduate School of Frontier
Sciences, The University of Tokyo, Kashiwa 277-8568, Japan.

Keywords: Next generation sequencing, Disease omics analysis
Genome wide association study (GWAS) is one of the most important applications ofsequencing technology and contributed to the detection of gene regions associated with diseases inmany researches[1][2]. However, most of those projects did not result in the detection of highly
correlated SNV and the effectiveness of GWAS study is questioned by researchers [3].
We expect that GWAS study so far does not achieve adequate results because there are someproblems in bioinformatics analysis processes and software. Particularly, we focused on the
problems in mapping procedures. Read mapping software often used in genome sequencing
research such as BWA [4] and Bowtie [5] discard reads or select a certain position randomly frommultiple positions (in this case, map quality becomes zero) when they deal with multi-mapping
reads. In both cases multi-mapping reads are not used in subsequent analysis.
The probability that certain number of reads are uniquely mapped in each genome position
can be modeled by poisson distribution. Therefore, in this research we calculated the probability
that enough number of reads for SV detection are mapped in each genome position using poisson
distribution. Then we focused on the regions where this probability is low (which we call“uncallable region”) and associated with gene annotation and segmental duplication.

[1] Okada, Y., et al., Genetic of rheumatoid arthritis contributes to biology and drug discovery, Nature
506:376-381, 2013.
[2] Imamura, M., et al., Genome-wide association studies in the Japanese population identify seven novelloci for type 2 diabetes, Nature Communications. 7:10531, 2016.
[3] Gallaway, E., New concerns raised over value of genome-wide disease studies, Nature, 546:463, 2017.
[4] Li, H. & Durbin, R., Fast and accurate short read alignment with Burrows-Wheeler transform,
Bioinformatics, 25:1754-1760, 2009.
[ 5 ] L a n g m e a d , B . , T r a p n e l l , C . , P o p , M . , S a l z b e r g , S . L . , U l t r a f a s t a n d me m o r y - e f f i c i e n t a l i g n m e n tof short DNA sequences to the human genome, Genome Biology, 10:R25, 2009.

P3-12

Toward the next step in G protein-coupled receptor
research: a knowledge-driven analysis for the next targets
in drug discovery
Koji Nagata 1

Yukie Katayama 1

Tomomi Sato 2

aknagata@mail.ecc.u-tokyo.ac.jp

ykatayam@ims.u-tokyo.ac.jp

tomomi@post.kek.jp

1

2

3

Yeondae Kwon 1

Takeshi Kawabata 3

ayekwon@mail.ecc.u-tokyo.ac.jp

kawabata@protein.osaka-u.ac.jp

Department of Applied Biological Chemistry, Graduate School of Agricultural and Life
Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Structural Biology Research Center, Photon Factory, Institute of Materials Structure Science,High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801,
Japan
Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan

Keywords: DDSS, OMIM, Orphan GPCR, VaProS
More than 800 G protein-coupled receptor (GPCR) genes have been discovered in the humangenome. Towards the next step in GPCR research, we performed a knowledge-driven analysis oforphan class-A GPCRs that may serve as novel targets in drug discovery [1]. We examined therelationship between 61 orphan class-A GPCR genes and diseases using the OMIM database [2]and the DDSS tool [3]. The OMIM database contains data on disease-related variants of the genes.
Particularly, the variants of GPR101, GPR161, and GPR88 are related to the respective diseases:
growth hormone-secreting pituitary adenoma 2; pituitary stalk interruption syndrome(interpretations conflicting); and childhood-onset chorea with psychomotor retardation. On theother hand, the DDSS tool suggests that 49 out of the 61 orphan receptor genes are related todiseases, judging from their co-occurrences in abstracts of biomedical literature. Notably, GPR50
and GPR3 are related to as many as 25 and 24 disease-associated keywords, respectively. The
aforementioned five orphan GPCRs were characterized genetically, structurally and functionallyusing the structural life science data cloud VaProS [4], so as to evaluate their potential as next
targets in drug discovery.
[1] Nagata, K., Katayama, Y., Sato, T., Kwon, Y., and Kawabata, T., Toward the next step in G
protein-coupled receptor research: a knowledge-driven analysis for the next potential targets in
drug discovery, J. Struct. Funct. Genomics, 17, 111-133, 2017.
[2] Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., and Hamosh, A., OMIM.org:
Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic
disorders, Nucleic Acids Res., 43, D789-798, 2015.
[3] Kwon, Y., Shimizu, S., Sugawara, H., and Miyazaki, S., A novel evaluation measure for
identifying drug targets from the biomedical literature, IPSJ Transactions on Bioinformatics, 7,
16-23, 2014.
[4] Gojobori, T., Ikeo, K., Katayama, Y., Kawabata, T., Kinjo, A. R., Kinoshita, K., Kwon, Y.,
Migita, O., Mizutani, H., Muraoka, M., Nagata, K., Omori, S., Sugawara, H., Yamada, D., andYura, K., VaProS: a database-integration approach for protein/genome information retrieval, J.
Struct. Funct. Genomics, 17, 69-81, 2017.

P3-13

Impact of read trimming on Illumina
paired-end-sequencing samples in the microbiome analysis
using Qiime
Attayeb Mohsen

attayeb@nibiohn.go.jp

1

Jonguk Park

jonguk@nibiohn.go.jp

Yi-An Chen

chenyian@nibiohn.go.jp

Hitoshi Kawashima

Kenji Mizuguchi

hkawashima@nibiohn.go.jp

kenji@nibiohn.go.jp

National institutes of Biomedical Innovation, Health, and Nutrition, 7-6-8, Saito-Asagi, Ibaraki
City, Osaka 567-0085, Japan

Keywords: Quality trimming, Next generation sequencing, Microbiome data analysis.

Paired-end sequences are generated by sequencing the target DNA fro m its both sides producing
partially overlapped two pair-end reads for the target DNA, then both reads are merged to produce
a longer final read. The quality of the resulting final read is affected by the base-specific quality
for the original reads. The Illumina paired-end sequencing technology yielded a good quality
product, and is the most commonly used technology nowadays. However, base-specific quality
scores reduce substantially with machine cycles, leading to low-quality bases as sequencing
approaches to the end of the read, which worsens the merging process and the subsequent analysis
steps in Qiime 1 .
Trimming 2 is used to remove the parts with low quality, and helps improve the outcome of themerging step and overall analysis. In this poster, we e xamined the importance of the trimming
quality threshold using different datasets, and showed the effect of this threshold on the number ofreads preserved after trimming, the number of picked operational taxonomic units ( OTUs) and thediversity measures.
[1] Caporaso J. G., et al., QIIME allows analysis of high-throughput community sequencing data.
Nature Methods 7(5): 335-336, 2010.
[2] Bushnell B., BBTools available at: (http://sourceforge.net/projects/bbtools/).

P3-14

Has Proximity in Human Genome been Conserved through2-Round Whole Genome Duplication ?
Hyogo Kajiyama 1
dbo64283@kwansei.ac.jp
1

	
  	
 

Hiroyuki Toh 1
tohhir@kwansei.ac.jp

Department of Biomedical Chemistry, School of Science and Technology, Kwansei Gakuin
University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan

Keywords: Two-Round Whole Genome Duplication, Hi-C
Recent development of the NGS technology has enabled us to analyze various biological phenomena.
One of the applications of the NGS is “Hi-C”, which can detect spatially adjacent chromosomal regions
in human genome. Then, a model for the structural conformation of the human genome is built basedon the Hi-C data [1, 2] . On the other hand, it is suggested that the vertebrates have undergone thetwo-round whole genome duplication (2RWGD). Associated with the events, four copies of paralogs
were generated in the vertebrate genomes, although the numbers may have changed due to the lineage
specific deletions and/or duplications after the events. In this research, we examined whether theadjacency of chromosomal regions has been conserved after the 2RWGD or not. At first, we obtained adata from NCBI GEO [3] , which includes proximity scores between chromosomal regions generated
from a Hi-C experiment [1] . In the data, each chromosome is divided into non-overlapped regions orbins of 1Mbp in length. We also obtained a whole set of pairs, gene name and protein ID, included in
the human genome (hg19) from UCSC Genome Browser [4] . In addition, the information about thechromosomal locations of human genes was obtained through Galaxy [5] . Then, these data were
integrated to generate a table, in which each row corresponds to a gene and consists of three elements,
gene name, chromosomal location and protein ID of the gene. Next, we obtained the amino acidsequence data encoded by human genes from Uniprot [6] . The sequences thus obtained were sorted sothat the proteins encoded in the same bin constitute one set, to make the sequence comparison
described below easier.	
  Thridly, we obtained a list of the paralogous genes associated with 2RWGD
from OHNOLOGS [7,8] . Then, the bins including the paralogs were identified. The gene products
encoded by the bins spatially adjacent to the different bins containing the paralogs were compared bymaking the alignments for all possible pairs between the adjacent bins. If paralogs are observed among
the adjacent bins, it suggests the conservation of chromosomal adjacency after the 2RWGD. The
conservation of the chromosomal contingency will be discussed based on the analysis.

[1] Lieberman-Aiden, E. et al. (2009). Comprehensive mapping of long-range interactions reveals
folding principles of the human genome. Science, 326(5950), 289-293.
[2] Li, C. et al. (2014). The 3DGD: a database of genome 3D structure. Bioinformatics,
30.11: 1640-1642.
[3] http://www.ncbi.nlm.nih.gov/geo/[4] https://genome.ucsc.edu/[5] https://usegalaxy.org/[6] http://www.uniprot.org/[7] Singh, P. P., Arora, J., & Isambert, H. (2015). Identification of ohnolog genes originating fromwhole genome duplication in early vertebrates, based on synteny comparison across multiple
genomes. PLoS Comput Biol, 11(7), e1004394.
[8] http://ohnologs.curie.fr/

P4-01Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
1. A curated database combining public and newly
acquired experimental data
Hitoshi Kawashima 1

Reiko Watanabe 1

Tsuyoshi Esaki 1

hkawashi@nibiohn.go.jp

reiko-watanabe@nibiohn.go.jp

tsuyoshi-esaki@nibiohn.go.jp

Daisuke Satoh 3

Kazuyoshi Ikeda 3

rikiya-ohashi@nibiohn.go.jp

Rikiya Ohashi 1,2

daisuke.satoh@level-five.jp

ikeda@level-five.jp

Chioko Nagao 1

Yayoi Natsume-Kitatani 1

Kenji Mizuguchi 1

chio@nibiohn.go.jp

natsume@nibiohn.go.jp

kenji@nibiohn.go.jp

1

2

3

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8, Saito Asagi, Ibaraki-shi, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi,
Toda-shi, Saitama 335-8505, Japan
Software Division, Level Five Co., Ltd., 2-25, Kanda Suda-cho, Chiyoda-ku, Tokyo 101-0041,
Japan

Keywords: Pharmacokinetics, Database, Prediction models
To address pharmacokinetic and toxicological issues in drug development, once the main sourceof late attrition of drug candidates, many pharmaceutical companies have now implemented early
ADME or early toxicological studies. However, such approaches are difficult to emulate in theacademic drug discovery environment. Therefore, we began an initiative “Development of a Drug
Discovery Informatics System”, with support from the Japan Agency for Medical Research andDevelopment (AMED) in collaboration with several other research groups. The main aim of thisinitiative is to develop accurate prediction systems for ADME and tox properties primarily targeting
academic scientists. Our group’s focus is to develop a pharmacokinetics database and prediction
models.
Any good prediction system depends on high-volume, high-quality training datasets. We collectedpharmacokinetic and physicochemical parameters from the public bioactivity database, ChEMBL[1]. However, since ChEMBL compiles data obtained in different experimental conditions, wedeveloped a curation workflow to select the data measured in compatible conditions and to reformat
the results as appropriate for our prediction system.
In addition to the public data, we have been acquiring both in vitro and in vivo experimental datausing consistent protocols. The in vitro experiments include physicochemical parameters such assolubility and distribution coefficient, and pharmacokinetic parameters such as metabolic stability,
protein binding in plasma, protein binding in brain homogenate, and blood-to-plasma concentration
ratio. The in vivo data include the drug concentrations in plasma and tissues after oral or intravenous
administration of the drug and pharmacokinetic parameters calculated therefrom.
We stored all the data in a PostgreSQL database and developed a web application to view andanalyze the database content. We plan to integrate this system with the cardiotoxicity andhepatotoxicity databases developed within the AMED initiative described above.
[1] https://www.ebi.ac.uk/chembl/
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-02Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
2. Classification of aqueous solubility measured
using the dried-DMSO method
Tsuyoshi Esaki 1
tsuyoshi-esaki@nibiohn.go.jp

Reiko Watanabe 1reiko-watanabe@nibiohn.go.jp

Hitoshi Kawashima 1
hkawashima@nibiohn.go.jp

Yayoi Natsume-Kitatani 1

Chioko Nagao 1

Rikiya Ohashi 1,2

natsume@nibiohn.go.jp

chio@nibiohn.go.jp

rikiya-ohashi@nibiohn.go.jp

Kenji Mizuguchi 1

kenji@nibiohn.go.jp
1

2

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8, Saito Asagi, Ibaraki-shi, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi,
Toda-shi, Saitama 335-8505, Japan

Keywords: Aqueous solubility, Dried-DMSO, Machine learning
It has been demonstrated that high-throughput in vitro measurements for the evaluation ofphysicochemical, pharmacokinetic, and safety parameters can save time and cost in drug
development and ultimately improve the success rate. Solubility is an essential physicochemical
property having great influences on drug absorption. The oral administration of compounds withlow solubility can not only have low effects, but also be affected by the individual variation.
Therefore, in silico methods to estimate the solubility of compounds are needed and both free andproprietary software programs have been developed . However, solubility is one of the properties
that have been reported to be difficult to predict with high accuracy [1]. In the academic drug
discovery environment, it is unfeasible to collect a large volume of new experimental data andalthough a considerable amount of data are available from public databases, they came from avariety of different experimental conditions. Thus, in this study, we compiled publicly available
solubility data that were measured using a single procedure, the dried-DMSO method, andconstructed computational models to predict solubility and to classify target compounds fromchemical structures alone. The dried-DMSO method is a high-throughput experimental method
widely used in pharmaceutical companies. All the experimental data were collected from the public
bioactivity database, ChEMBL (ver.23) [2], often by checking the original literature and excluding
the data measured using other (or unclear) methods. The collected compounds data were separated
into two groups, training and test sets. Different combinations of chemical descriptors and machinelearning algorithms were examined on the training set and the constructed models were evaluatedon the test set, resulting in an accurate system for predicting compound solubility measured using
the dried-DMSO method.
[1] Hughes L.D., et al., Why are some properties more difficult to predict than others? A study of
QSAR models of solubility, melting point, and LogP, J. Chem. Inf. Model., 48:220-232, 2008.
[2] https://www.ebi.ac.uk/chembl/
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-03Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
3. Prediction of human plasma protein binding
from chemical structures
Reiko Watanabe 1reiko-watanabe@nibiohn.go.jp

Tsuyoshi Esaki 1
tsuyoshi-esaki@nibiohn.go.jp

Hitoshi Kawashima 1
hkawashi@nibiohn.go.jp

Yayoi Natsume-Kitatani 1

Chioko Nagao 1

Rikiya Ohashi 1,2

natsume@nibiohn.go.jp

chio@nibiohn.go.jp

rikiya-ohashi@nibiohn.go.jp

Kenji Mizuguchi 1
kenji@nibiohn.go.jp
1

2

Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8, Saito Asagi, Ibaraki-shi, Osaka 567-0085, Japan
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 2-2-50, Kawagishi,
Toda-shi, Saitama 335-8505, Japan

Keywords: Plasma protein binding, Protein unbound, Machine learning
The fraction of chemicals unbound to plasma proteins is an important determinant of the efficacy ofa drug in pharmacokinetic and pharmacodynamic studies, since the free (unbound) form is available forpharmacological interactions. The unbound fraction affects the membrane permeability, metabolism andglomerular filtration rate, and consequentially it influences the volume of distribution in different tissues
and the total clearance. In addition, unbound drugs exert pharmacological and toxicological effects. In
the early stages of drug discovery, determining the fraction of drug unbound to plasma proteins inhumans and in animal models provides an understanding of the ADME properties to assist candidate
selection. Furthermore, it is important to predict the fraction of unbound drug in the brain for assessing
brain drug delivery. Accordingly, the in silico prediction of unbound drug in plasma is perceived as aneffective tool to mitigate the risk of late-stage attrition during drug development and to optimize further
screening [1]. Human plasma contains several binding proteins such as human serum albumin (HSA),
α-1 acid glycoprotein (AGP), lipoproteins and globulins. Consequently, only a few studies have reported
in silico modeling to predict the binding ratio in intact plasma [2], although previous studies havereported the prediction of protein binding to simple substances such as HSA or AGP.
The aim of this study is to build high precision models to predict the fraction unbound in the intact
human plasma. We employed three machine learning techniques, using topological descriptors to
represent molecular structures, to generate models of human plasma protein binding based on the large
data set of experimental values; these models were subsequently used to estimate the unbound fraction.
For this purpose, multiple prediction models were created; a regression model to predict the protein
binding in human plasma, a classification model to predict the fraction unbound in human plasma anddifferent regression models based on the compound polarity such as acidic, basic and neutral.

[1] Lambrinidis G., et al., In vitro, in silico and integrated strategies for the estimation of plasma
protein binding. A review, Adv. Drug Deliv. Rev., 86:27-45, 2015.
[2] Votano J.R., et al., QSAR modeling of human serum protein binding with several modelingtechniques utilizing structure-information representation, J. Med. Chem., 49:7169-7181, 2006.
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-04Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
4. Development of a program for predicting the sites ofmetabolism by CYP1A2 using a two dimensional template
Junya Ohori 1
oohori.junya@jp.fujitsu.com

1

2

Hiromi Koga 1
koga.hiromi-01@jp.fujitsu.com

Mayumi Matsushita 1

Kenji Mizuguchi 2

matusita.mayumi@jp.fujitsu.com

kenji@nibiohn.go.jp

Life Innovation Department, Healthcare Solutions Unit, Fujitsu Kyushu Systems Limited, 5-13
Higashihie 1-chome, Hakata-ku, Fukuoka 812-0007, Japan
Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health andNutrition, 7-6-8 Saito Asagi, Ibaraki-shi, Osaka 567-0085, Japan

Keywords: In Silico, Drug Metabolism, Regioselectivity, Cytochrome P450
In drug discovery research it is important to predict whether metabolic enzymes metabolize drugs
and what metabolites would they produce. In silico methods for predicting various drug
metabolisms, mainly by cytochrome P450s, have been studied, but their very limited accuracy is
the main reason why they still remain only for supplementary use at the early stages of drug
discovery. In order to predict the safety of drug candidates as early as possible and to accelerate
the development of drugs, a prediction system that can predict metabolism with high accuracy isdesired.
In this study, we developed a program to predict the metabolic site of CYP1A2 based on themethod [1] of superimposing compounds to be predicted on a template representing the relevantbinding and metabolic sites of the enzyme. The template was prepared by overlaying several
known substrates of the metabolic enzyme and assigning additional physicochemical constraints to
it. The program developed to predict regioselectivity of the enzyme only requires the structural
formula of the query compound and does not require any complicated computational chemistry
methods. Here we report the prediction results of the program which were validated and comparedwith the actual experiment results collected from literature.
[1] Yamazoe, Y. et al. , Prediction of regioselectivity and preferred order of metabolisms on
CYP1A2-mediated reactions. Part 1. Focusing on polycyclic arenes and the related chemicals.
Drug Metabolism and Pharmacokinetics. 31(5):363-384, 2016.
This work was conducted as part of “Development of a Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-05Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
5. Prediction of sites of drug metabolism by cytochrome
P450 by molecular simulation
Hiroaki Saito 1 ,Taku Mizukami 2 ,Yoshinori Hirano 1 ,hiroaki.saito@riken.jp,

mizukami@jaist.ac.jp,

hirano@riken.jp,

Takao Otsuka 1 ,Noriaki Okimoto 1 ,Makoto Taiji 1

totsuka.riken.jp,

okimoto@riken.jp,

taiji@riken.jp

1

2

Laboratory of computational molecular design computational biology, computational biology
research core, RIKEN Quantitative Biology Center (QBiC)
Department of Materials Sciences, Japan Institute of Science and Technology (JAIST)

Keywords: Molecular dynamics, Molecular docking, Site of metabolism, Cytochrome P450 (CYP)
Cytochrome P450s (CYPs), a superfamily of haem-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 CYP1A2. 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 stored in the SMARTCypprogram[3] are adopted as the reactivity scores in the method. We carried out a molecular
dynamics (MD) simulation of apo type of CYP1A2 for 10 micro second to sample the receptor(pocket) structures for the ensemble docking simulation. In the molecular docking, DOCK programwith Grid score, which are optimized for CYP system [4], was used. We prepared 42 test set
compounds metabolized by the CYP1A2 and 100,000 receptor snapshots (which correspond to the
10 micro second MD simulation) for the ensemble docking simulation. The top three ranked atoms
by the new score function are defined as possible SOMs for each compound in this study. We
found the success rate of the predicted SOMs was 94 %, showing better result than those by theensemble docking (80%) and SMARTCyp program (88%) 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).

P4-06Development of a pharmacokinetics prediction systemusing multiscale integrated modeling:
6. Assessment of quantum chemical reaction indicesusing Fukui function

1

Takao Otsuka 1

Hiroaki Saito 1

totsuka@riken.jp

hiroaki.saito@riken.jp

Noriaki Okimoto 1

Makoto Taiji 1

okimoto@riken.jp

taiji@riken.jp

RIKEN Quantitative Biology Center (QBiC), QBiC Building B, 6-2-4 Furuedai, Suita, Osaka
565-0874, Japan

Keywords: Electronic structure calculation, Fukui function, chemical reaction indices, frontier
molecular orbital
One of the most popular methodology to predict the potential site of chemical reaction of amolecule by electronic structure calculations is Fukui function or Fukui indices method. The Fukui
indices are described as the response between the chemical potential and the external field of amolecule in the framework of density functional theory exactly. By using some approximations,
the Fukui indices can be evaluated through the electron density or the molecular orbitals,
especially the frontier orbitals, reflecting the electronic states such as electron adding or removingof whole molecular systems 1 . In practical quantum chemical calculations, the Fukui indices can beobtained by so-called population analysis method 2,3 .
In this study we report a study on the numerical assessment of the Fukui indices derived bysome types of population analysis method such as Mulliken, Löwdin, Hirschfeld and so on. We
also apply the Fukui indices method to the metabolic molecular systems, and analyze and evaluate
the reactivity site of metabolic reaction through the Fukui indices.
f + : nucleophilicf - : electrophilic

Reactivity(population)
High

Low
Figure. Quantum chemical reaction indices of 7-Ethoxycoumarinobtained by Fukui function.

[1] Parr, R. G., Wang, W., Density-functional theory of atoms and molecules, Oxford University
Press, New York, Oxford, 1989.
[2] Yang, W., Mortier, W. J., J. Am. Chem. Soc., 108, 5708-5711, 1986.
[3] For example, Contreras, R. R., Fuentealba, P., Galván, M., Pérez, P., Chem. Phys. Lett., 304,
405-413, (1999).
This work is conducted as a part of “Construction of Drug Discovery Informatics System”
supported by Japan Agency for Medical Research and Development (AMED).

P4-07Development of a pharmacokinetics prediction system using
multiscale integrated modeling:
7. Enhancement of the custom SoC LSI functionalities toimprove the system performance
Gentaro Morimoto

Yousuke Ohno

Hao Zhanggentaro.morimoto@riken.jp

ohno@riken.jp

hao.zhang@riken.jp

Itta Ohmura

Keigo Nishida

Teruhisa Komatsu

Yohei M. Koyama

ohmura@riken.jp keigo.nishida.jg@riken.jp teruhisa.komatsu@riken.jp ym.koyama@riken.jp

Aki Hasegawa

ahasegawa@riken.jp

Hiroshi Koyama

hkoyama@riken.jp

Makoto Taiji

taiji@riken.jp

Research and Development Project of Special Purpose Supercomputers for Drug Design,
RIKEN Quantitative Biology Center, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan

Keywords: Molecular dynamics simulations, Ligand binding, Protein modeling, Drug design
We have been developing a series of special purpose computers for molecular dynamics
simulations. The current system is the MDGRAPE-4[1]. The whole system consists of 512 custom
System on Chip(SoC) LSI chips in 3D torus network. The target performance is to make it possible
to simulate typical protein-ligand complex surrounded by water for 10 microseconds per day,which is two orders of magnitude faster than commercially available systems. One of theapplications of long-term MD simulations of proteins is to predict the site of metabolism.
The MDGRAPE-4 hardware development completed in 2014, and the stabilization of operationand software development process succeeded for two years. However, the final performance does
not reach the expected one. In the process of the development, we realized that the classical MD
simulation of the typical system size has already reached the strong scaling limit of parallel
computation and not only accelerating the computationally demanding part but also more
specialized hardware for the type of computation is required to improve the performance.
To achieve the target performance, we have started the development of the improved system thatis named MDGRAPE-4A. The system architecture is basically common with the MDGRAPE-4.
The enhancement of the functionalities are 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 of SIMD functions and special instructions required in the MD simulations in
the general-purpose cores4) reduction of the latency and more flexible routi ng patterns in the network interface5) support of a long-range electrostatic interaction calculation met hod that is suitable for theparallel systems with torus network topology
Currently the development process is under the design and the verification of the revised LSI.
The new system hardware will be completed in 2018. In this presentation, we would like to show
the ideas to improve the performance, progress and simulation results of the new LSI design.
[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).

P4-08Library Design With ChEMBL and Virtual Fragments
Based On Electrostatic and Shape Bioisosterism Approach1
2

Kazuyoshi Ikeda 1

Tomoki Yonezawa 2

ikeda-kz@pha.keio.ac.jp

yonezawa@level-five.jp

Keio University, Faculty of Pharmacy, Division of Physics for Life Functions, 1-5-30
Shibakoen, Minato-ku, Tokyo 105-8512, Japan
Division of Drug Discovery, Software Division, Level Five Co., Ltd., 2-25, Kanda Suda-cho,
Chiyoda-ku, Tokyo 101-0041, Japan

Keywords: Field-based ligand design, Scaffold hopping, Tyrosine-protein kinase inhibitor, WaterSwap

Computational approaches play an important role for efficient ligand design. Our group isanalyzing a compound library of a diverse set of compounds as well as developing a core/focused
library design method by utilizing information from drug discovery data-resources such as
drugs/compounds/bioactivities and the targets.
In this study, we have developed a protocol to generate a focused library using a molecular-field
ligand-based method for a target. The field-based method, which is an improved method of theconventional pharmacophore model, can directly express molecule interactions of ligand to a protein
target including electrostatic and hydrophobic interactions. The method can be also applied for
scaffold/fragment hopping of a given reference-molecule to make the bioisosteric compound dataset[1]. Using the Pipeline Pilot [2], an automatic protocol has been developed to extract frequent rings
and fragments from the ChEMBL database [3] and split based on the frequency of occurrence of thefragments in the databases. We also added the fragment data from historical/recent approved-drugs
in the DrugBank [4] and a novel ring information from the VEHICLe (virtual exploratory
heterocyclic library) to expand the chemical space. This protocol can filter drug-like/lead-likecompounds with pharmaceutically desirable physicochemical properties.
Tyrosine-protein kinase ABL/BTK inhibitor was selected as a test-set in this study and expanded
their known drugs to the diverse set of the bioisosteric compounds with the similar electrostatic
patterns. Additionally, the protein structure information was used for analyzing the residue-level
ligand-protein interaction energetics in the active-site and identifying the substitution point ofligand for scaffold hopping using the WaterSwap method which is recently developed by Woods etal. [implemented in the software 1.]. In this presentation, we will explain and discuss the automatic
protocol of generating a forced library and the applications to the known drug targets.
[1] http://www.cresset-group.com/products[2] http://accelrys.co.jp/products/collaborative-science/biovia-pipeline-pilot[3] Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A.,
Light, Y., Mak, L., McGlinchey, S., Nowotka, M., Papadatos, G., Santos, R., and Overington, J.
P., The ChEMBL bioactivity database: an update, Nucleic Acids Research, 42(Databaseissue):D1083-1090, 2014.
[4] Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M,
Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS. DrugBank
4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014 Jan 1;42(1):D1091-7.
[5] Rapid decomposition and visualisation of protein-ligand binding free energies by residue andby water.
[6] Woods CJ, Malaisree M, Michel J, Long B, McIntosh-Smith S, Mulholland AJ. Faraday Discuss.
2014;169:477-99.

P4-09Development of an informatics system for predicting
cardiotoxicity: 1. Exploring binding modes of hERG
inhibitors based on docking simulation andthe validation with site-directed mutagenesis data
Hitomi Yuki

Tomohiro Satohitomi.yuki@riken.jp

tomohiro.sato@riken.jp

Keiji Ogura

Teruki Honma

keiji.ogura@riken.jp

honma.teruki@riken.jp

RIKEN Center for Life Science Technologies, 1-7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan

Keywords: hERG, Ligand docking, Pharmacophore, Site-directed mutagenesis
ADMET (absorption, distribution, metabolism, excretion and toxicity) studies are indispensable
to lead efficacious and safe drug candidates. Since the 2000s, toxicity has become a major par t of
the cause of development failure, 1 among which the cardiotoxicity accounts for about 40% of thetotal toxicity. 2 hERG (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. Therefore, drug design for avoiding the hERG
inhibition is quite important through the all stages of drug discovery. Although 3D models ofhERG would provide worthy comp ound design ideas for reducing hERG inhibition, there was noexperimentally validated 3D model for several decades. This year, using emerging cryo -electron
microscopy technologies, the molecular structure of hERG has been determined. 3
We performed MD simulation of the hERG cryo-electron microscopy structure and subsequentdocking simulations of known representative hERG inhibitors such as dofetilide to identify the
essential pharmacophores for the hERG inhibition. hERG is a channel and shows high mobility
like cytochrome P450. It makes difficult to determine the appropriate binding mode from diverse
docking poses by only docking scores. Thus, we used the site -directed mutagenesis (SDM) results
for the hERG pore to select and validate the binding mode. SDM re veals which residues areassociated with the inhibition of hERG for each compound. We would like to discuss the analysisof these docking results and the pharmacophores of the hERG inhibition.
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).
[1] Kola, I., and Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug
Discov. 3:711–716, 2004.
[2] Laverty, H., Benson, C., Cartwright, E., Cross, M., Garland, C., Hammond, T., Holloway, C.,
McMahon, N., Milligan, J., Park, B., Pirmohamed, M., Pollard, C., Radford, J., Roome, N.,
Sager, P., Singh, S., Suter, T., Suter, W., Trafford, A., Volders, P., Wallis, R., Weaver, R., York,M., and Valentin, J. How can we improve our understanding of cardiovascular safety liabilities
to develop safer medicines? Br. J. Pharmacol. 163:675–693, 2011[3] 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

P4-10Development of an informatics system for predicting
cardiotoxicity: 2. Update of integrated database for hERG
blocking small molecules considering deviation of assay
results.
Tomohiro Satotomohiro.sato@riken.jp

Keiji Ogura
keiji.ogura@riken.jp

Hitomi Yuki

hitomi.yuki@riken.jp

Teruki Honma
honma.teruki@riken.jp

Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama
City, Kanagawa, 230-0045, Japan

Keywords: hERG, human ether-a-go-go, 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. To
utilizing the recent increase of information about hERG inhibitors in public databases, integrated
database for hERG blocking compounds wa s built and reported in CBI annual meeting 2016. Here,
we updated the database using latest versions of ChEMBL, PubChem, GOSTAR, and hERG
Central [2], and refined the merging procedure to accommodate to compounds for which
contradictory assay results were found across multiple datab ases. Assessment of structural
diversity using Murcko frameworks revealed that the integrated database contains more than 2times as many scaffold for hERG inhibitors as any of the existing databases, and covering 18.2%
of all chemical space occupied by whole compounds in ChEMBL (438,551 frameworks). (Table 1)
The database could provide most comprehensive information about hERG inhibitors and be usefulto predict the inhibitory activity and design safer compounds for drug discovery.
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).
Table 1. Number of hERG inhibitors and inactive compounds in each database.
Database
ChEMBL (v22)
GOSTAR
NCGC
hERGCentral
Integrated DB

Class
Inhibitors
Inactives
Inhibitors
Inactives
Inhibitors
Inactives
Inhibitors
Inactives
Inhibitors
Inactives

Compounds
4,793
5,275
3,260
3,509
232
1,234
4,321
274,536
9,890
281,329

Murcko frameworks
2,474
3,012
1,727
1,692
173
504
2,708
73,4195,516
76,420[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] Du F., Yu H., Zou B., Babcock J., Long S., Li M. hERGCentral: a large database to store, retrieve,
and analyze compound-human Ether-à-go-go related gene channel interactions to facilitate
cardiotoxicity assessment in drug development, Assay Drug Dev Technol. 9(6), 580-588, 2011

P4-11Development of an informatics system for predicting
cardiotoxicity: 3. hERG prediction model based on
integrated database combined with in silico molecular
design for generation of safer compounds
Keiji Ogura
keiji.ogura@riken.jp

Hitomi Yuki
hitomi.yuki@riken.jp

Tomohiro Satotomohiro.sato@riken.jp

Teruki Honma
honma.teruki@riken.jp

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa, 230-0045, Japan

Keywords: hERG, machine learning, in silico molecular design
Inhibition of the human ether a-go-go related gene product (hERG) has been identified as a major
contributor to QT interval prolongation. It can cause critical cardiovascular side effects and was
accounted for the withdrawal of several drugs from the market. Therefore, blockade of hERG has
been extensively investigated since the early 2000s , and in silico models for hERG prediction havebeen continuously reported in recent years [1,2]. Despite the increasing number of hERG-related
information in publicly available database [3], most of the previous researches were performed
based on insufficient number or imbalanced dataset, resulting in limited the predictability and/or
scope of models. In addition to the prediction of hERG blocking activity, suggestion of structuralconversion for safer compounds to hERG is a quite useful tool for drug discovery process.
In this study, in silico model to predict hERG blockade was built using integrated dataset
consisting of hERG information from multiple databases, and combined with fragment replacement
algorithm to suggest structural conversion for safer compounds. This system enables us to not only
predict hERG inhibition but also suggest synthetic candidates with drug-likeness to release thehERG issue. The validation using the test set (87,361 compounds) revealed the superior prediction
performance of the constructed hERG prediction model (accuracy: 0.984, kappa: 0.732,
ROC-AUC: 0.962) compared to commercially available prediction models (accuracy: 0.573-0.905,
kappa: 0.0622-0.291, ROC-AUC: 0.827-0.890). Then, to demonstrate the efficiency of the
combined system to suggest structural conversion, the system was applied to our in house HCK
inhibitor dataset to assess whether the system can generate and predict hERG inactive compounds
from a compound which inhibit hERG.
This work was done as a part of “Construction of Drug Discovery Informatics System” supported
by Japan Agency for Medical Research and Development (AMED).
[1] Braga, R. C., Alves, V. M., Silva, M. F.B., Muratov, E., Fourches, D., Lião, L. M., Tropsha, A.,
and Andrade, C. H., Pred-hERG: A Novel web-Accessible Computational Tool for Predicting
Cardiac Toxicity, Mol. Inf. 10:698-701, 2015.
[2] Villoutreix, B, O., Taboureau, O., Computational investigations of hERG channel blockers:
New insights and current predictive models , Adv Drug Deliv Rev. 86:72-82, 2015.
[3] Wang, S., Li, Y., Xu, L., Li, D, and Hou, Y., Recent Developments in Computational Predictionof hERG Blockage, Curr. Top. Med. Chem. 13:1317-1326, 2013.

P4-12Development of an informatics system for predicting
drug-induced liver injury:
1. An ontological approach for structuring knowledge ofdrug-induced liver toxicity
Yuki Yamagata
y-yamagata@nibiohn.go.jp

Noriyuki Nakatsu
nnakatsu@nibiohn.go.jp

Yoshinobu Igarashi y-igarashi@nibiohn.go.jp
Hiroshi Yamada   h-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

Keywords: Ontology, Drug induced liver injury, Hepatotoxicity, Toxic course, Toxic process
One of the major issues in drug development is the safety assessment of toxicity. The liver
toxicity has been recognized as a primary cause of withdrawal of drug development.
In the poster, we discuss the course of the drug-induced liver toxicity. First, we introduce ourvisualization model of hepatotoxic course map, which shows causal relationships of cellular
processes from early cellular responses to toxicological end points. Next, we propose ourontological model to structuralize the knowledge of liver toxicity. Based on upper ontology such as
YAMATO [1] and BFO [2], we classified concepts involved in hepatotoxic courses as follows:l Occurrent: toxic process (e.g., unfolded protein refolding), toxic course (e.g., ER stress)l Continuant: molecular entity, structure (e.g., organ, cell, organelle), organisml Dependent entity: role (e.g., transcription factor), function
In the course of realization of toxicity, toxic processes are diverse with multiple granularities.
Therefore, in our model, a process can be decomposed into sub-processes represented by “hasPart”
relations. For example, Unfolded protein response (UPR) hasPart three sub-processes, i.e.,translation attenuation, unfolded protein refolding and unfolded protein degradation. Furthermore,
sub-processes can also be decomposed for instance, “unfolded protein refolding” hasPart“Perk-Bip dissociation” at the molecular level. Based on role theory [3], we also define the toxic
process by specifying the roles assigned to participants, focusing on feature genes, e.g., UPR
process hasParticipant Bip/Grp78, and Bip/Grp78 hasRole “chaperone.”
This work provides high-quality data with manual curation from articles/textbooks and covers
granularities of toxic processes from molecule to organ level. Hence, it will contribute tounderstanding knowledge about liver toxicity including bridging a gap between in vivo and in vitro
experiments.
Acknowledgment
This research is supported by the Drug Discovery Support Promotion Project from Japan Agency
for Medical Research and Development, AMED.
[1] Mizoguchi, R., YAMATO: Yet Another More Advanced Top-level Ontology, Proc. of the Sixth
Australasian Ontology Workshop, 1-16, 2010.
[2] Smith, B., and Spear, A. D. Building Ontologies with Basic Formal Ontology, MIT Press, 2015.
[3] Mizoguchi, R., et al., Applied Ontology 2 (2): 159-179, 2007.

P4-13Development of an informatics system for predicting
drug-induced liver injury:
2. Drug-induced liver toxicity prediction system usingtranscriptomics data of human primary hepatocyte
Yoshinobu Igarashi

Yuki Yamagata

y-igarashi@nibiohn.go.jp

y-yamagata@nibiohn.go.jp

Noriyuki Nakatsu
nnakatsu@nibiohn.go.jp

Hiroshi Yamadah-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

Keywords: DILI, Toxicogenomics, Transcriptomics, Human primary hepatocyte
Drug-induced liver injury (DILI) is a primary safety issue in drug development. The DILI oftencauses failures in clinical trials as well as withdrawals from the market. To decrease the failures, it
is important to characterize and predict potential mechanism of liver toxicity of a drug candidate
compound in advance.
We are developing the in silico integrated analysis platform for DILI. This platform consists of
two components. One component is the database of newly obtained toxicogenomics data of DILI
compounds which are defined in FDA/Liver Toxicity Knowledge Base [1]. We also assembled
other public transcriptomics data like NIH/LINCS [2] to complement the database. Anothercomponent is the DILI prediction system using multiple approaches. One of the approaches is the
DILI prediction using transcriptomics data of human primary hepatocyte. We utilize
transcriptomics data of human primary hepatocyte because the animal testing on rodents has
limitations to predict unexpected DILI in human due to the species gap. Each DILI prediction
result is shown up with a statistical probability and with current predicted location in eachhepatotoxicity course map which is constructed based on the ontological model.
This DILI prediction system will assist to prioritize compounds to select further toxicology
testing.
Acknowledgment
This research is supported by the Drug Discovery Support Promotion Project from Japan Agency
for Medical Research and Development, AMED.
[1] Chen M, Vijay V, Shi Q, Liu Z, Fang H, Tong W. FDA-approved drug labeling for the study of
drug-induced liver injury. Drug Discov Today. 2011 16(15-16):697-703[2] http://www.biorxiv.org/content/early/2017/05/10/136168

P4-14Development of an informatics system for predicting
drug-induced liver injury: 3. Bridging for drug-induced
hepatotoxicity using in vivo and in vitro gene expression
data
Yang Zhao 1

Kazuhiko Fukui 1

y-zhao@aist.go.jp

k-fukui@aist.go.jp

Katsuhisa Horimoto 1
k.horimoto@aist.go.jp
1

Molecular Profiling Research Center for Drug Discovery, National Institute for Advanced
Industrial Science and Technology, 2-4-7Aomi, Koto-ku, Tokyo 135-0064 Japan

Keywords: statistical analysis, gene expression, Toxicogenomics, hepatotoxicity
The Japanese Toxicogenomics Project (TGP) [1] measured multiple toxic compounds in liver andhave provided a large amount of gene expression data for rat in vivo/vitro and human in vitro.
Transcriptomic understanding of in vivo/in vitro toxic mechanisms and phenotypic changes whendrugs are exposed is important for risk management in drug discovery. In particular, bridging invivo and in vitro data is a key topic to interpret potential hepatotoxicity of drug candidates [2].
In this work, we developed toxBridge – a statistical interpretation of building relationshipbetween in vivo and in vitro of TGP data. Based on the rat in vivo and human in vitro gene
expression data, fold change and data normalization were taken into account to analyze thesignificance (p-value) of gene signatures and pathways for about 160 medical compounds at four
dose levels and time points, where the compounds are selected from toxicological modelcompounds known to possess hepatotoxicity/nephrotoxicity. These results are compiled thetoxBridge, which provides 1) the significant signatures and pathways depending on dose and timeseries for each compound and 2) bridging rat in vivo and rat in vitro by using basic statisticalanalyses at pathway level. Indeed, at the pathway level, target compounds are likely to takefeasible bridging capabilities between rat in vivo/vitro and human in vitro whose pathway the
Jaccard index is up to 0.4. The results of combined probability and rank correlation analyses
demonstrate that particular compounds with high (combined probability or rank correlation) values
are capable to bridge rat in vivo/vitro and human in vitro.
We further proposed toxRank – rank analyses of drugs using rat in vivo/vitro and human in vitro
gene expression data. Given a signature of query drug, toxRank computes the similarity withreference to expression profiles which are our own developed rank matrix data, and predicts thehepatotoxicity of the query drug.
The interpretation we give of the toxBridge and toxRank make an interesting link with TGP gene
expression data that measure how many medical compounds are translatable from rat in vivo/vitro
to human in vitro. Both of these two studies are expected to be useful for prediction of
drug-induced hepatotoxicity.
Acknowledgment
This research is supported by the Drug Discovery Support Promotion Project from Japan Agency
for Medical Research and Development, AMED.
[1] Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T. and Yamada, H.,
Open TG-GATEs: a large-scale toxicogenomics database, Nucleic Acids Research,
43:D921-D927, 2015.
[2] Hardt, C., Beber, M.E., Rasche, A., Kamburov, A. and Hebels, D.G., ToxDB: pathway-levelinterpretation of drug-treatment data, The journal of biological databases and curation, 2016.

P4-15Development of an informatics system for predicting
drug-induced liver injury:
4. QSAR prediction system for drug-induced liver
toxicities using adverse drug event database
Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

Department of Clinical Pharmaceutics, Meiji Pharmaceutical University
2-522-1 Noshio, Kiyose, Tokyo 204-8588, JAPAN

Keywords: DILI, nuclear receptor, stress response pathway, JADER, FAERS
Adverse drug events and toxicities are one of the most important factors in the dropout of new
drugs during their development period. Currently, in the early stages of drug development, such aslead optimization step for a large number of chemicals, the in silico approach is expected to offer
powerful tools for discriminating among compounds that are toxic and have adverse effects. In thispresentation, the development of the QSAR prediction model for a wide range of compounds thatpossibly result in drug-induced liver toxicities, one of the most frequent toxic events of drugs, wasmentioned. In our strategy for developing the QSAR prediction model, activities related to theadverse outcome pathway (AOP) [1], such as nuclear receptors and stress response pathways
stored in Tox21-AOP database, were first targeted. Then, the predicted activities and molecular
descriptors were applied to develop the prediction model for hepatic disorders in adverse drug
event databases such as PMDA-JADER [2] and FDA-FAERS [3].
Acknowledgment
This research is supported by the Drug Discovery Support Promotion Project from Japan Agency
for Medical Research and Development, AMED.
[1] Ankley GT., Bennett RS., Erickson RJ., Hoff DJ., Hornung MW., Johnson RD., Mount DR.,
Nichols JW., Russom CL., Schmieder PK., Serrrano JA., Tietge JE., Villeneuve DL., Environ.
Toxicol. Chem., 29, 730-741 (2010).
[2] Pharmaceuticals and Medical Devices Agency(http://www.pmda.go.jp/safety/info-services/drugs/adr-info/suspected-adr/0005.html)[3] Questions and Answers on FDA's Adverse Event Reporting System (FAERS)(https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugef
fects/)

P4-16
Enantiomeric Molecular Recognition of Barbitals in
Propofol Binding Site of GABA A Receptor
Tomoyoshi Seto 1 , Ken Koyano 2
tseto@belle.shiga-med.ac.jp
1

Department of Anesthesiology, Shiga University of Medical Science Hospital, 2 Faculty of Medicine,
Shiga University of Medical Science, Seta-Tsukinowa-cho, Otsu 520-2192, Japan

Keywords: Barbital, Enantiomer, Propofol, Molecular Recognition , GABA A Receptor
Stereoisomers of isoflurane, barbitals, etomidate and neuro steroid have been reported to havedifference in anesthetic potency, the minimum alveolar concentration (MAC) [1].
GABA A receptor (GABA A R) is ligand-gated Cl - channel, which exits post-synaptic membrane of
inhibitory neurons of central nerve system, has important role in loss of consciousness inanesthesia [2]. In 2014, 3D structure of GABA A R has resolved [3]. Propofol binding site of
GABA A R has recently specified [4].
We address the questions as to how enantiomeric anesthetics bind to anesthetic target site, andwhy there are differences between the enantiomers in their mode of binding. To clarify themechanisms of enantiomeric molecular recognition and binding to GABA A R, we investigated thebinding mode and energy of amobarbital (amobar), pentobarbital (pentobar) and isobarbital(isobar) using docking simulation.
Method: 4COF ( Protein Data Bank ) was used as GABA A R structure [3]. Amobar, (R)-,(S)-pentobarbital (pentobar) and (R)-, (S)-isobarbital (isobar) bindings were studied. ASEDock(2016.01.07) ( Ryoka system, Japan ) [5] were used for the dockings and energy calculations on
MOE2016.0802 [6]. Ligand’s chirality was kept in flexible ligand docking. Amber10:ETH force
field was used. MOE-ligand Interactions was used to find interactions.
Results and Discussions: (1) amobar bound to (a) M2-M2’ site ( propofol high affinity bindingsite [4] ), (b) agonist binding site, (c) extracellular domain inter-subunit site and (d) M2-M3-M1'
site ( propofol low affinity binding site [4] ) of GABA A R. (2) NH of barbital ring of amobarhydrogen-bonded to Gln224 of M2-M2’ site in the same as propofol binding. (3) (R)-, (S)-pentobar
and isobar bound to M2-M2’ site. N-H of barbital ring hydrogen-bonded to Glu270 of M2-M2’ site.
(4) Of pentobar’s enantiomer methyl group adjacent to chiral carbon directed to opposite way eachother. Steric hindrance of this methyl group determines enantiomer’s fitting. (5) Barbital ringbinding is presumed to be the major binding interaction of either enantiomer. The principal binding
force from the ring is modulated by methyl group hindrance. The steric hindrance produced -2.9
kcal mol -1 molecular discrimination of binding energy.
Conclusions: Amobar, pentobar and isobar bound to propofol high affinity site [4]. The
molecular discrimination of enantiomer turns out to be from modulation of major binding force by
methyl-group hindrance of enantiomer.
[1] Burke, D. and Henderson, D.J., Chirality: a blueprint for the future, Brit J Anaesth 88:563-576,
2002.
[2] Garcia, P.S., Kolesky, S.E. and Jenkins, A., General Anesthetic Actions on GABA A Receptors,
Curr Neuropharm, 8:2-9, 2010.
[3] Miller, P.S. and Radu Aricescu, A., Crystal Structure of a human GABA A receptor, Nature,
512:270-275, 2014.
[4] Franks, N.P., Structural Comparison of Ligand-gated Ion Channels in Open, Closed, andDesensitized States Identify a Novel Propofol-binding Site on Mammalian-Aminobutyric Acid
Type A Receptors, Anesthesiology, 122:787-794, 2015.
[5] Goto , A., Kataoka R., et al., ASEDock-docking based on alpha spheres and excluded volumes , J
Chem Inf Model, 48:583-590, 2008.
[6] Molecular Operating Environment (MOE) 2016.08.02, Chemical Computing Group ULC, 2016.

P4-17

Genome-wide analysis of DNA methylation and gene
expression patterns in human iPS cell-derived hepatocytes
Seiichi Ishida 1

Su-Ryang Kim 1

Shinichiro Horiuchi 1

Yukie Kuroda 1

ishida@nihs.go.jp

Shoko Uchida 1
1

2

Jane Synnergren 2

Yasunari Kanda 1

Division of Pharmacology, National Institute of Health Sciences,
1-18-1 Kamiyoga, Setagaya-ku Tokyo 1588501, Japan
Systems Biology Research Center, School of Bioscience, University of Skövde
Högskolevägen, Box 408, 541 28 Skövde, Sweden

Keywords: iPSC, Hepatocyte, differentiation, Gene expression, Genomic DNA Methylation[Purpose] Human hepatocytes are frequently used for in vitro drug metabolism and toxicity tests
for drug discovery and development. However, since supply of human hepatocytes is limited,
construction of an alternative test system is awaited. Human induced pluripotent stem cells(hiPSCs) are useful because of its proliferative properties and differentiation capabilities into
various cell types, so they are expected as a source of hepatocytes for in vitro testing.
Comprehensive gene expression analysis and genomic DNA methylation analysis were performedon commercially available hiPSC-derived hepatocytes, and the methylation status of genomic DNA
of hiPSC-derived hepatocytes from three vendors was compared with that of human primary
hepatocyte cells.
[Materials and Methods] Three commercially available hiPSC-derived hepatocytes were seeded
and cultured according to the recommended protocol. For one of hiPSC -derived hepatocytes(Vendor A), the changes of gene expression and genomic DNA methylation status during
differentiation from hiPSCs to hepatocytes were analyzed. Human primary hepatocytes derived
from three donors were used as controls. DNA and RNA were prepared from e ach cell culture.
Genome-wide gene expressions were measured by GeneChip Human Gene 1.0 ST Array(Affymetrix, Santa Clara, CA, USA), and Genome DNA methylations were measured by
HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA), according to the
manufacturer’s protocol. GeneSpring GX 12.0 (Agilent) was used for data analysis.
[Results and Discussion] Genes whose expression increased and methylation rate decreased during
differentiation of hiPSCs to hepatocytes were selected and 3,280 genes were obtained.
Hepatocytes-specific genes such as metabolic enzymes, transporters and nuclear receptors were
contained abundantly among them, while genes which show specific expression in other tissues
were also found. Comparison of the methylation status of hiPSC-derived hepatocytes from threevendors and primary hepatocytes showed similar pattern, whereas the genes whose methylation
status was different were also observed.
[Conclusion] The results suggest that the analysis of methylation status of genomic DNA is usefulas an indicator of functional evaluation for hiPSC-derived hepatocytes.

P4-18Integrated analysis for drug toxicities in humanusing multi-label classification.

1

2

Koki Matsuoka1

A. AMMAR GHAIBEH2

c401631029@tokushima-u.ac.jp

ammargh@acm.org

Shiro Omura1
c401203002@tokushima-u.ac.jp

Youichi Sato1

Hiroki Moriguchi2

Aiko Yamauchi1

youichi.sato@tokushima-u.ac.jp

h_moriguchi@ap6.mopera.ne.jp

yamauchi.aiko@tokushima-u.ac.jp

Department of Pharmaceutical Information Science, Graduate School of Biomedical Sciences, Tokushima
University
1-78-1, Sho-machi, Tokushima 770-8505, Japan
Department of Medical Informatics, Graduate School of Biomedical Sciences, Tokushima University
3-18-15, Kuramoto-cho, Tokushima 770-8503, Japan

Keywords: In silico prediction, multi-label classification, Decision tree, Random forest, SVM,
carcinogenicity, teratogenicity, nephrotoxicity, hepatotoxicity
Introduction: In recent years, many studies on drug toxicity prediction using in silico methods have beenreported. Many of them focus on a single toxicity such as carcinogenicity. However, the developed models
may not be practical because drugs often have multiple toxicities. Therefore, we developed models that can
predict carcinogenicity, teratogenicity, nephrotoxicity and hepatotoxicity of drugs in humans at once, using
Support Vector Machine (SVM), decision tree (gini) and random forest.
Methods: We collected 139 drug data that included information regarding carcinogenicity, teratogenicity,nephrotoxicity and hepatotoxicity in human from International Agency for Research on Cancer (IARC),
Drugs in Pregnancy and Lactation 8th and Japanese prescription drug information database. The insilico models were built on the dataset of drugs with 43 descriptors. We evaluated the model performancewith Hamming score, Hamming loss and Accuracy using holdout validation ( train : test = 9 : 1) in Python3.
Results and discussion: We used ‘One vs All’ approach to deal with the multi-label classification problem.
In addition, the methods with classification algorisms corresponding to multi-labels were used in the present
study. The best performances of SVM, decision tree and random forest were shown in Table.1.
Table.1 The performance based on hamming score, hamming loss and accuracy.
Classification

hamming score

hamming loss

accuracy

SVM (rbf)

0.81

0.11

0.64

Decision tree (gini)

0.69

0.16

0.43

Random forest

0.67

0.20

0.29

Conclusion: The model developed was shown to be useful for not only predicting the toxicity of new
chemical entities in advance but also enabling safer pharmacotherapy.

P4-19In silico models for predicting repeated dose toxicity
using machine learning
Tatsuya Ochibe

Kaori Ambe

c142513@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: Applicability domain, Hepatocellular hypertrophy, Machine learning method
It is expected to develop the alternative method for animal experiments to reduce the number ofexperimental animals and improve efficiency of the risk assessment of environmental chemical
substances. Machine learning in silico evaluation method for the repeated dose toxicity (RDT) is
considered to be more useful because RDT has complex and unknown mechanisms. In this study,
using machine learning methods, we tried building in silico prediction models ofchemical-induced hepatocellular hypertrophy which is often observed in RDT studies based onmolecular descriptors [1]. Our original data sets which used to build the models were createdfrom two in vivo toxicological databases. The first was the risk assessment reports of pesticides,
food additives, and veterinary medicine products that were published by Food Safety
Commission of Japan [2], and the second was the Hazard Evaluation Support System Integrated
Platform (HESS) [3] by National Institute of Technology and Evaluation. Then, we developed
prediction models by support vector machine and random forest using descriptors calculated bydragon 6 [4]. Because the prediction results of chemicals inside applicability domain (AD) arehigh reliability [5], we defined AD by the data distance [6]. For both our models, prediction
accuracy of the test set inside the AD was above 74% and AUC was about 0.8. These results
suggested that our in silico models for hepatocellular hypertrophy are reliable and promising
method for RDT prediction.

[1] A. P. Hall., et al., Liver Hypertrophy: A Review of Adaptive (Adverse and Non-adverse)
Changes, Toxicologic Pathology, 40: 971-994, 2012.
[2] http://www.fsc.go.jp/english/index.html[3] http://www.nite.go.jp/en/chem/qsar/hess-e.html[4] http://talete.mi.it/index.htm[5] OECD, ENV/JM/MONO, 2, 2007.
[6] I. V. Tetko., et al., Critical Assessment of QSAR Models of Environmental Toxicity against
Tetrahymena pyriformis: Focusing on Applicability Domain and Ove rfitting by Variable
Selection, J. Chem. Inf. Model. 48 (9): 1733-1746, 2008.

P4-20A prediction model for photolytic half-lives of chemicals
Yumi MATSUYAMA 1

Norihito KAWASHITA 1, 2

u121719h@gmail.com

nkawashita@life.kindai.ac.jp

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

2

3

Kousuke OKAMOTO 3
kokamoto@gen-info.osaka-u.ac.jp

Tatsuya TAKAGI 1
ttakagi@phs.osaka-u.ac.jp

Graduate School of Pharmaceutical Science, Osaka University, 1-6 Yamadaoka, Suita, Osaka
562-0875, Japan
Faculty of Sciences and Engineering, Kindai University, 3-4-1 Kowakae, Higashiosaka, Osaka
577-8502, Japan
Faculty of Pharmaceutical Science, Hokuriku University, 1-1 Taiyogaoka. Kanazawa, 9201154, Japan

Keywords: Statistical analysis, Quantitative Structure-Property Relationships, Evaluation ofenvironmental pollution, Photolysis
It is an important issue to understand the physical properties and dynamics of chemicals in theenvironment. However, experimental surveys on the environmental fates of all compounds which havebeen produced is almost impossible since it takes huge amounts of cost and time. Hence, the QSPR
(Quantitative Structure- Property Relationship) predictions of environmental fates are expected as analternative approach. The QSPR models of photolysis have not been sufficiently developed yet and mostmodels were developed only for some analogous compounds [1]. The purpose of current study is toconstruct a prediction model for the photolytic half -lives and to estimate some physicochemical
properties which are significantly effective for the prediction.
We used Random Forests [2], a commonly used machine learning algorithm for constructing themodel. Descriptors were calculated by MOE [3]. To remove the effects caused by the random split oftraining and test sets, models were constructed multiple times. As a result, energy differences between
the HOMO and LUMO, charge-related descriptors, and some other descriptors were selected to besignificant. The average accuracies of the model were approximately 73.1% and 72.9% for training andtest sets, respectively. Some compounds were always correctly classified, while the others weresometimes not. This result indicated that this model was almost perfect for predicting the photolytic
half-lives of the former compounds, whereas this was not suitable for the latter ones.
Therefore, we further classified the compounds into two groups (correctly predicted compounds andthe others) and constructed distinct models by the two groups. In the result, although some compounds
were classified almost incorrectly (the average accuracy was approximately 55.1% for test sets),
accuracy improved for other compounds (the average accuracy was approximately 81.2% for test sets).
Some compounds which we could not classified previous model became predictable. This result
indicates that dividing dataset is effective to predict photolytic half -lives using the Random Forests
method.

[1] Nolte, T.M. and Ragas, A.M. A review of quantitative structure-property relationships for thefate of ionizable organic chemicals in water matrices and identification of knowledge gaps.
Environmental science. Processes & impacts. 19:221–246, 2017[2] Breiman, L. Random forests. Machine learning. 45:5–32, 2001
[3] Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group ULC, 1010
Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2017.

P4-21

Computational toxicology of cardiomyocyte gel image
Kei Kuramoto 1
kei.kuramoto@daikin.co.jp

Tatsuya Takakuwa 1
tatsuya.takakuwa@daikin.co.jp

Satoshi Tokuno 1
satoshi.tokuno@daikin.co.jp

Hiroshi Matsumoto 1
hiroshi4.matsumoto@daikin.co.jp

Shigehito Sagisaka 1
shigehito.sagisaka@daikin.co.jp

Kaoru Uesugi 2
uesugi@live.mech.eng.osaka-u.ac.jp

Keisuke Morishima 2
morishima@mech.eng.osaka-u.ac.jp12

Technology and Innovation Center, Daikin Industries, Ltd.
1-1, Nishi-Hitotsuya, Settsu, Osaka 566-8585, Japan
Department of Mechanical engineering and Graduate School of Engineering, Osaka University
2-1, Yamada-oka, Suita, Osaka 565-0871, Japan

Keywords: computational toxicology, cardiomyocyte gel, image processing
Alternatives to animal experiments have been official policies all over the world andinternational standards on the welfare of animals used experiments. In this study, we performed
image processing and machine learning with cuda GPGPU [1] and phase-based image processing[2] to analyze the motion and dynamics of the correlation between toxic material andcardiomyocyte gel [3]. The kinetics of myocardial cell gels has periodicity and as each moves
synchronously or asynchronously, we found that region division image processing wasnecessary. And we also found that kinetic analysis could be performed with high accuracy byusing image matching by phase-only correlation method.

[1] http://www.nvidia.co.jp/object/cuda-parallel-computing-platform-jp.html[2] Kuglin, C. D. and Hines, D.C., The phase correlation image alignment method, ProceedingsInternational Conference on Cybernetics and Society, pp. 163-165, 1975.
[3] Hoshino, T., Imagawa, and K., Morishima, K., Cardiomyocyte-Driven Wet Gel Robotics Chemical Modulation of Cardiac Network Pattern Generator-，The 2010 3rd IEEE RAS & EMBSInternational Conference on Biomedical Robotics and Biomechatronics (BioRob 2010) , IEEE
Robotics and Automation Society & IEEE Engineering in Medicine and Biology Society,547-552, 2010.

P4-22A Pneumatic Pressure-Driven Multi-Throughput
Organs-On-A-Chip
Taku Satoh 1

Shinji Sugiura 1

Kazumi Shin 1

taku.satou@aist.go.jp

shinji.sugiura@aist.go.jp

shin-kazumi@aist.go.jp

Toshiyuki Kanamori 1
t.kanamori@aist.go.jp

1

Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial
Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan

Keywords: Microfluidic device, Organs-on-a-chip, Drug discovery, Cell-based assay
Recently,
multi-organsor
body-on-a-chip have been attracting
attention as next-generation research tools
in drug discovery.
In these system,
multiple types of cells were cultured indiscrete chambers connected to each othervia microchannels for medium circulation[1, 2]. In this study, we demonstrate aorgans-on-a-chip system with parallelized
medium circulation by using thepressure-driven
mediumcirculation
system [3].
Figure 1: View of and components of the pneumatic
The culture device in the present systempressure-driven multi-throughput multi-organs-on-ais composed of a chamber plate,chip.
microfluidic plate, lid, and holder (Figure1). The culture unit is composed of two culture chambers, which are connected each other via
microchannels in the microfluidic plate. The sequentially applying pressure to the chambers
induces unidirectional circulation of medium by a similar manner as described previously [3].
Medium circulation was successfully accomplished by sequential applied pressure. The eight setof the two-organ system in the single device can be simultaneously driven by the pneumaticpressure supplied from the external pressure control system vi a four pneumatic pressure lines in
the lid. We have also successfully applied the eight -throughput two-organ system to theliver-cancer connection system to evaluate anticancer prodrug.
[1] I. Maschmeyer et al., “A four-organ-chip for interconnected long-term co-culture of humanintestine, liver, skin and kidney equivalents,” Lab Chip, 15, 2688 -2699, 2015.
[2] C. Oleaga et al., “Multi-Organ toxicity demonstration in a functional human in vitro systemcomposed of four organs,” Sci. Rep. 6, 20030, 2016.
[3] T. Satoh et al., “Pneumatic pressure-driven circulation cell culture microfluidic device forinvestigation of vascular endothelial cells under shear stress,” Lab Chip, 16, 2339 -2348, 2016.

P4-23
On/off targets based toxicity prediction analysis andVerification
Kosuke Negishi 1
k.negishi@elsevier.com

1

Yasunori Okazaki 1
y.okazaki@elsevier.com

Life Science,Elsevier Japan K.K,1-9-15 Higashi-Azabu Minato-ku, Tokyo 106-0044 Japan

Keywords: Toxicology analysis, Medicinal chemistry, On/off target discovery, data integration
SAR(Structure-activity relationship) is well known as one of major evaluation method for drug
discovery and toxicity/biological activity prediction. On the other hand, protein"s" (on/off
target"s") based analysis is not major yet.
In this report, we tried to evaluate the feasibility of the protein"s" based analysis. At first we
focused on drugs which have few side effects and chose phenylalanine derivative of oral
hypoglycemic agent. This type of drug do not have clinical adverse event besides low blood
sugar. We created on/off target"s" list from 2 kind of databases (affinity/bioassay type and Text
mining type). By Using the proteins listed in these databases, we calculated candidates of
toxicity phenotype. Moreover, we searched FDA/EMA documents (database) to verify theconsistency of these predicted toxicities.
[1] Hansch C., Smith N., Engle R., Wood H. Quantitative structure activity relationships of antineoplastic
drugs: Nitrosoureas and triazenoimidazoles Cancer chemotherapy reports 1972 56:4 (443-456)[2] Miller EC, Miller JA. Searches for ultimate chemical carcinogens and their reactions with cellular
macromolecules. Cancer. 1981 May 15;47(10):2327-45.
[3]Hanefeld M, Bouter KP, Dickinson S, Guitard C. Rapid and short-acting mealtime insulin secretion withnateglinide controls both prandial and mean glycemia. Diabetes Care. 2000 Feb;23(2):202-7.
[4] Kawamori R, Kaku K,Hanafusa T , Kashiwabara D, Kageyama S, and Hotta N Efficacy and safety of
repaglinide vs nateglinide for treatment of Japanese patients with type 2 diabetes mellitus J Diabetes
Investig. 2012 Jun 6; 3(3): 302–308.

P5-01

Thermostability Prediction Model for Protein Single Point
Mutations Using Machine Learning
Chie Imamura

Keisuke Kawano

chiem@mosk.tytlabs.co.jp

kawano@mosk.tytlabs.co.jp

Satoshi Koide

Yukihiro Tadokoro

koide@mosk.tytlabs.co.jp

tadokoro@mosk.tytlabs.co.jp

Toyota Central R&D Labs. Inc.,
41-1 Yokomichi, Ngakute, Aichi 480-1192, Japan

Keywords: Prediction of thermostability, Machine learning, SVM, Mutant protein, Protein
structure
The thermostability of proteins has been improved by high-throughput experimental screenings
from randomly mutated proteins so far. However, it is inefficient because it takes a lot of work andtime to select a target. Therefore it is necessary to develop high-performance computational
methods for predicting thermostability upon protein mutations.
In this study, we take an alternative approach to improve protein thermostability using machinelearning. At first, we constructed 2,000 mutants which have single point mutation at various sitesof two proteins. Then we obtained thermostability data by experimental evaluation. Also, we
proposed some features based on relevant information about protein local structure around themutation site.
Using the experimental data and those features as input data, we constructed a thermostability
prediction model using support vector machine. Prediction accuracy of the model was evaluated bycross-validation. In the case which training and test data were selected from the same protein, theaccuracy of predictions and the area under the curve (AUC) showed high values. On the other hand,when training and test data from different proteins were used, AUC values were decreased. Our
results suggest that not only the features from local structure around the mutation site, but featuresfrom whole structure are important to improve prediction accuracy.

P5-02Complex Network Approach for
Characterization of Protein Secondary Structure
Shohei Konno 1
skon@sci.hokudai.ac.jp

Takao Namiki 2
nami@math.sci .hokudai.ac.jp

Koichiro Ishimori 1, 2
koichiro@sci.hokudai.ac.jp

12

Graduate School of Chemical Sciences and Engineering , Hokkaido University, North-13, West8, Kita-ku, Sapporo, 060-8628, Japan
Faculty of Science, Hokkaido University, North-10, West-8, Kita-ku, Sapporo, 060-0810, Japan

Keywords: Protein secondary structure, Complex networks, Amino acid network
One difficulty in protein structure prediction is to construct appropriate coarse-grained models.
An emerging approach is amino acid network (AAN), where the “vertices” represent amino acidresidues in the protein and the “links” correspond to interactions between the residues. In major
AAN studies, the links have been defined by distance between  carbons to provide topologicalinformation with low computational complexity. However, these networks often lose information
about side chains, which remain unsuccessful to categorize protein secondary structures [1]. We
successfully distinguished between all- and all- proteins by using a new definition of AAN,
whose links are determined by different cutoff values depending on the chemical interactions [2][3].
In order to characterize the protein secondary structures by our AAN
approach, we collected high-resolution all- and all- protein structuresfrom Protein Data Bank [4]. To capture the types of interactions, we haveconstructed a network with different cutoff values for hydrophobic
interactions, hydrogen bonds, ion bonds, disulfide bonds and covalentbonds as defined in [3]. The vertices are linked if any two atoms from two
different residues are located within the cutoff distance. We compared our
method with conventional ones in term of the ability of the secondary
structure discrimination and calculated three network parameters: theaverage degree (k), average clustering coefficient (C) and average distance(L). k and C reflect the local structures, while the global structure affects L.
As depicted in Figure A, all- proteins are well separated from all-
proteins in the scatter plots of k versus C. The figure clearly shows that our Figure Scatter plots of kversus C for all- (○) andmethod can distinguish between the all- and all- protein structures, all- (×) proteins by ourwhich was unsuccessful in the conventional method (Figure B). On the introduced network (A)other hand, it was difficult to separate the secondary structures by using L. and previously used 
The parameters describing the local structure (k and C) are more sensitive carbon network (B).
to the difference of the secondary structures than that describing the global structure (L). Suchsuccessful discrimination suggests that AAN we defined here can be an effective coarse-grainedmodel for characterizing the protein structures.
[1] Alves, N. A. and Martinez, A. S., Physica A, 375:336-344, 2007
[2] Tina, K. G., Bhadra, R., Srinivasan, N., Nucleic Acids Research, 35: W473-W476, 2007
[3] http://pic.mbu.iisc.ernet.in[4] http://www.rcsb.org

P5-03

Reconstruction of self-assembly structure of microtubules
Ryuzo Azuma
azuma.r.ac@m.titech.ac.jp

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

Gregory Spence Gutmann
ggutmann13@jcu.edu†

Akihiko Konagaya
kona@c.titech.ac.jp

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

Keywords: Molecular robotics, artificial intelligence related technology
We have been developing a coarse-grained simulation method for analyses of super molecular structureformation processes. In this poster presentation, we demonstrate self-organized structures in terms of studies
on mechanisms of microtubule structure formation. The halmark of our simulation is that it explicitly takes
account of water molecules albeit it is based on a mathematical model employing purely stochastic rules forsemi-microscopic molecular dynamics in a 3 dimensional space. Water molecules turned out to haveversatile roles in governing electrostatic and hydrophobic interactions during the microtuble organization
process.
In our microtuble simulation model, each tubulin monomer (α and β tubulins) is represented by a chain ofcoarse-grained subunits. Strength of associative interactions between those subunits are roughly double thatof thermal fluctuations. For inter-monomer coupling, we assume ~0.2eV strength by taking account ofelectrostatics calculation in a previous study [1].
Using these simulation method and model, we have been successfully observing self-organized structurethat associates well with microtuble triplet structure. Transient dynamics in those simulations reaches aplateau after ~20 milliseconds and thus the structure is stabilized. Diameter of each one in the triplet is close
to that of 13 protofilament microtubles (23-4 nm.) These results support our view that coexistence ofhydrophobic and electrostatic interaction between tubulin dimer drives self-organization of microtubules inpresence of water molecules.
We have also been focusing on atomic-level tubulin α-β dimer structure responsible for interactions during
microtubule assembly formation. In particular, surface charge and molecular dynamics are studied for
scenarios where one of/both c-termini are detached from the surface, as proposed previously [2]. Thesefeatures are going to be utilized to refine our coarse-grained simulation model.
This presentation is based on results obtained from a project commissioned by the New Energy andIndustrial Technology Development Organization (NEDO.)[1] Tuszynsky, J.A., Brown, A., Crawford, E.J., Carpenter, E.J., Molecular Dynamics Simulation of Tubulin
Structure and Calculations of Electrostatic Properties of Microtubules, Mathematical and Computer
Modeling, 41:1055-1070, 2005.
[2] Priel, A., Tuszynski, J.A., Woolf, N.J., Transitions in microtubule C-termini confirmations as a possible
dendritic signaling phenomenon, Eur Biophys J, 35:40-52, 2005.
Fig. 3 Fig. 3

P5-04On Being Scientific
Tomokazu Konishi 1
konishi@akita-pu.ac.jp

1

Graduate School of Bioresource Sciences, Akita Prefectural University, 010-0195, Japan

Keywords: falsifiability, falsification
Our researches often make a crowded lobby; for instance, I wonder how many procedures havebeen introduced to normalize transcriptomics data. Most of them are totally out of use, because
they lack falsifiability, which is an idea introduced by Karl Popper as a touchstone for science (1).
As science has no rigid definition, distinguishing true science and others is controversial; however,this idea has been widely accepted among scientists. Science hates a situation that observation
depends on analytical methodologies, as it is formed by integrating tons of observations reported
by many researchers. So, most scientists do not take ideas like “anything goes” (2, 3). As far as I
know, there has been found only one method that can analyze transcriptomics data fulfilling thisissue (4, 5). There would not be another solution, as it is compatible to thermodynamic property ofa living cell (6).
Another enthusiasm occurred to solve multiplicities of statistical tests (7). This claim
encompassed scientific community a mess, and man y procedures were introduced. However, thecasted problem may not really exist; it could be understood as a stray bullet from war between
schools of statisticians (8).
Rushing into a wrong paradigm is waste of time , not only for analysts, but also for scientists .
How can we avoid such researchers’ bad habit? I believe we can learn from science. Presently, we
introduce a new method by comparing it to existing ones by focusing on properties defined byourselves. I rarely saw articles that defined problems clearly, and estimated the damages caused by
the problem. Also, I rarely saw articles that declare what was assumed, certainty of theassumptions, and how they could be verified. Those should be clarified. I know it is easy to follow
someone’s idea and put some new decorations; however, this is not very scientific attitude.
Additionally, changing data to fulfil benefits of the analyst is prohibited in science, because it
could conflict to ethical rule; it would be recognized as falsification of data.
(1) Popper K. The logic of scientific discovery, Routledge; 1959.
(2) Feyerabend P. Against method: outline of an anarchist theory of knowledge , New Left Books;
1975.
(3) Sober E. Philosophy of Biology, Oxford University Press, 1993 .
(4) Konishi T, Konishi F, Takasaki S, Inoue K, Nakayama K, Konagaya A. Coincidence between
transcriptome analyses on different microarray platforms using a parametric framework. PLoS
ONE. 2008;3(10):e3555.
(5) Konishi T. Parametric analysis of RNA-seq expression data. Genes to Cells.
2016;21(6):639-47.
(6) Konishi T. A thermodynamic model of transcriptome formation. Nucleic Acids Res.
2005;33(20):6587-92.
(7) Dudoit S, Yang Y, Callow M, Speed T. Statistical methods for identifying differentiallyexpressed genes in replicated cDNA microarray experiments. Stat Sin. 2002;12:111–39.
(8) Konishi T. Microarray test results should not be compensated for multiplicity of gene contents.
BMC Systems Biology. 2011;5(Suppl 2):S6-S.

P5-05Using convolutional neural networks plus recurrent neuralnetwork, automatic classification of human iPS
cell-derived cardiomyocytes extracellular potential
waveforms.
Tetsuo Kitamura, Mayumi Obo,kitamura.tetsuo@mh.medience.co.jp

obo.mayumi@ms.medience.co.jp

Tomoharu Osada, Hiroaki Inoue,
osada.tomoharu@mg.medience.co.jp

inoue.hiroaki@mb.medience.co.jp

Yamamoto Yoshinori, Yasuyuki Oonishi,
yamamoto.yoshinori@mg.medience.co.jp

oonishi.yasuyuki@mp.medience.co.jp

Hideaki Hiratsuka
hiratsuka.hideaki@mg.medience.co.jp

Nonclinical Research Center, Drug Development Service Segment, LSI Medience Corp. 14 -1
Sunayama, Kamisu, Ibaraki, 314-0255, Japan

Keywords: convolutional neural networks, recurrent neural network, human iPS cell-derived
cardiomyocytes[Introduction] Cardiac safety evaluation method which uses an extracellular potential of human
iPS stem cell-derived cardiomyocytes is proposed. The evaluated system is expected to predictpro-arrhythmic potentials of compounds with high accuracy by detection of early after
depolarizations waveforms. This detection is done by human, so there is a problem in the labor andprecision. Automatic classification using convolutional neural networks plus recurrent neuralnetworks (CNN+RNN) are reported for movie classification technique. By the machine learningwith training data, CNN+RNN are possible to highly accurate classification. The purpose of thisstudy was to evaluate the accuracy of automatic classification with CNN+RNN for the waveforms
obtained in this evaluation system.
[Methods] Classification targets were waveforms of sequential beats from multi channels.
Framework of neural networks was TensorFlow. Supervised learning was used. Training data andtest data were selected from same experiments. These data were labeled with classification byhuman.
[Results] With the test data, concordance rate of classification result by human and CNN+RNN
was 99.97%.
[Conclusion] CNN+RNN showed high accuracy in cardiomyocytes extracellular potential
waveforms classification.

P5-06
Rational design of orthogonal gene transcription nanodevice on DNA origami
Takeya Masubuchi 1 , Masayuki Endo 2 , Ryo Iizuka 3 , Ayaka Iguchi 4 , Dong Hyun Yoon 4 ,Tetsushi Sekiguchi 5 , Hao Qi 1,6 , Ryosuke Iinuma 1 , Yuya Miyazono 1 , Shuichi Shoji 4 ,Takashi Funatsu 3 , Hiroshi Sugiyama 2,7 , Yoshie Harada 2,8 , Takuya Ueda 1
and Hisashi Tadakuma 1,2,8,
tadakuma@protein.osaka-u.ac.jp
1

Graduate School of Frontier Science, The University of Tokyo, 2 Institute for Integrated Cell-Material
Sciences (WPI-iCeMS), Kyoto University, 3 Graduate School of Pharmaceutical Sciences, The University ofTokyo, 4 Department of Nanoscience and Nanoengineering (ASE Graduate School), Waseda University,
5
Research Organization for Nano & Life Innovation, Waseda University, 6 Department of Chemical
Engineering and Technology, Tianjin University, 7 Department of Chemistry, Kyoto University, 8 Institutefor Protein Research, Osaka University

Keywords: Gene expression, Synthetic Biology, Molecular Robots
In synthetic biology, the design of gene expression requires devices that alter the output dependingon the situation. Inspired by RNA viruses that contain both enzymes and substrate genes, andexpress specific genes through architectural modalities, we integrated an enzyme , T7 RNApolymerase, and multiple target gene substrates onto a DNA origami -based nano-chip. This genenano-chip orthogonally transcribes its own genes, and using this system, we succeeded in arational design of gene expression by controlling the inter-molecular distances between the
enzyme and the substrate genes. Our approach of component integration on a nano -chips may
provide the basis for an integrated gene expression circuit.
[1] Tadakuma H, Masubuchi T, Ueda T. RNA Study Using DNA Nanotechnology.
Prog Mol Biol Transl Sci., 139:121-163 (2016).

(review),

P5-07Graph Neural Networks for Density Functional Theory
Masashi Tsubaki 1
tsubaki.masashi@go.aist.jp

1

National Institute of Advanced Industrial Science and Technology (AIST)
Artificial Intelligence Research Center, Machine Learning Research TeamTokyo Waterfront BIO-IT Research Building, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan

Keywords: Graph Neural Networks, End-to-End Learning, Density Functional Theory
In this poster, we propose the use of machine learning technique, graph neural networks (GNNs),for predicting various electronic ground-state properties molecules based on density functional(DFT) theory. GNN is a neural network, which takes a graph-structured data (i.e., molecular graph)
as an input and outputs a low-dimensional real-valued vector representation (i.e., molecular
embedding). In addition, our GNN uses an end-to-end learning technique to obtain the molecular
embeddings, which does not require any molecular features (e.g., descriptors and fingerprints) andknowledge of chemistry and physics. The end-to-end GNN allows us to automatically learn allparameters including input features in the model. We experiment with thirteen electronic
ground-state properties of 117k distinct organic molecules and show that our end-to-end GNN canbe more accurate than hybrid DFT.

P5-08ChemiSpadon: an SNS-based platform for human-assisted
chemical space exploration

1
2
3
4

5

Kazuki Z Yamamoto​1

Ryuichi Kubo​2

kayamamoto-tky@umin.net

ryuichi.kubo@dena.com yamasakih@pharm.kitasato-u.ac.jp

Hiroyuki Yamasaki​3

Masahito Ohue​4

Yusuke Yamada​5ohue@c.titech.ac.jp

yamayu0127@gmail.com

Isotope Science Center, University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
DeNA Life Science, Inc., Shibuya Hikarie, 2-21-1 Shibuya, Shibuya-ku, Tokyo 150-8510 Japan
Kitasato University School of Pharmacy, 5-9-1 Shirokane, Minato-ku, Tokyo 108-8641 Japan
School of Computing, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550,
Japan
Mishima.syk, Mishima-PostOffice-Kyokudome, Minami-Tamachi, Mishima-shi, Shizuoka 411-8799,
Japan

Keywords​: Chemical space, Drug design, Human computation, SNS, Mastodon
Exploration of undeveloped chemical space is essential for discovery of novel drugs. Conventionally,‘druglikeness’ and ‘druggablity’ have been mainly considered for small molecules whose molecular weights
are less than 500-600[1, 2]. However, to cope with novel drug targets previously considered ‘undruggable’due to absence of druggable cavities, medium-sized molecules like natural products, macrocycles,
peptidomimetics or others are needed[3]. Although medium-sized molecules constitute attractive chemical
space, the size of such space is too large to explore comprehensively[4]. To facilitate exploration of such vast
undeveloped chemical space, here we present “ChemiSpadon”, an SNS-based platform for human-assisted
chemical space exploration. ChemiSpadon is based on the open-source SNS system called Mastodon[5] andequipped with chemical structure posting system, structure evaluation system, and structure generating bots.
Taking advantage of human knowledge and instinct, we expect efficient exploration of undeveloped but
feasible chemical space for drug discovery. Participants are very welcome. Please visit: www.chemical.space[1] ​Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J., Experimental and computational
approaches to estimate solubility and permeability in drug discovery and development settings. ​Advanced
drug delivery reviews​, 23:3-25, 1997.
[2] ​Cheng, A. C., Coleman, R. G., Smyth, K. T., Cao, Q., Soulard, P., Caffrey, D. R., ... & Huang, E. S.,
Structure-based maximal affinity model predicts small-molecule druggability. ​Nature biotechnology​,
25(1):71, 2007.
[3] ​Lipinski, C. A., Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand
chemistry structure and drug discovery project decisions, ​Advanced drug delivery reviews​, 101:34-41,
2016.
[4] ​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. ​Journal of chemical information andmodeling​, 52(11):2864-2875, 2012.
[5] Mastodon ​https://joinmastodon.org/