P1-01
An elucidation of STING phosphorylation mechanism
by a combination of molecular dynamics simulations
and molecular biological analyses
Yuko Tsuchiya 1,2

Nao Jounai 1

tsuchiya@protein.osaka-u.ac.jp

n-jonai@nibiohn.go.jp

Fumihiko Takeshita 1

Ken J Ishii 1

takeshita.fumihiko.aw@daiichisankyo.co.jp

kenishii@nibiohn.go.jp

Kenji Mizuguchi 1
kenji@nibiohn.go.jp

1

2

National Institutes of Biomedical Innovation, Health and Nutrition,
7-6-8 Saito-Asagi, Ibaraki, Osaka 567-0085, Japan
Institute for Protein Research, Osaka University,
3-2 Yamadaoka, Suita, Osaka 565-0871, Japan

Keywords: STING phosphorylation, molecular dynamics simulations, vaccine adjuvants
STING (stimulator of interferon gene) is an essential adaptor protein in innate immunity. It senses
cytosolic DNA and induces IFN-β production when a cell is infected. A better understanding of thedownstream signaling mechanism, triggered by ligand binding, is essential for the development ofanti-cancer drugs or vaccine adjuvants.
In this study, we adopted an interdisciplinary approach and predicted the dynamics of STING byin silico simulations, followed by the characterization of candidate residues by molecularbiological techniques. Based on the results, we propose a putative mechanism of STING
phosphorylation upon binding of a potent ligand.
[1] Tsuchiya , Y., et al., Ligand-induced ordering of the C-terminal tail primes STING forphosphorylation by TBK1, EbioMedicine, 9:87-96, 2016.

P1-02
Molecular Dynamics Simulation of Shiga Toxin , IV
Kazumi Omata 1

Hisashi Okumura 2,3

komata@hosp.ncgm.go.jp

hokumura@ims.ac.jp

Yoshiharu Mori 2

Kiyotaka Nishikawa 4

ymori@ims.ac.jp

1

2
3

4

knishika@mail.doshisha.ac.jp

Department of Data Science, National Center for Global Health and Medicine, 1-21-1 Toyama,
Shinjuku, Tokyo 162-0052, Japan
Institute for Molecular Science, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, JapanThe Graduate University for Advanced Studies, 38 Nishigonaka, Myodaiji, Okazaki, Aichi
444-8585, Japan
Faculty of Life and Medical Sciences, Doshisha University, 1-3 Miyakotani, Tatara, Kyotanabe,
Kyoto 610-0394, Japan

Keywords: Shiga toxin, Ligand docking, Molecular dynamics, O157
To develop a drug against infection by Shiga toxin (Stx) -producing Escherichia coli O157:H7 [1,2],
this study investigates docking of Stx B subunit (Stx B) and a peptide neutralizer, using molecular
dynamics simulations [3].
In the previous CBI meetings, we reported the binding of Stx1 B and MMA-tet: Stx1 in the former
is a family of Stx, and the latter is a neutralizer developed by Nishikawa and collaborators with the
peptide library technique [2]. The MMA-tet is a branched peptide oligomer with four arms; theamino-acid sequence of each arm is MMARRRR. We have shown that (1) the MMA-tet recognizedand docked on the three classes of binding sites in Stx1 B, in agreement with the deduction basedon the experiments; (2) the binding of Stx1 B and MMA-tet occurs inhomogeneously on the Stx1 B
surface, implying that in fact more than one neutralizer bind to one Stx1 B in actual situations; (3)it remained unclear why the neutralizer binds strongly one of the three binding-site classes which
consists of arginine or tryptophan residues.
In this 2016 meeting, in addition to further analysis of the binding of Stx1 B and MMA-tet, we willreport the results of PPP-tet, in which the amino-acid sequence of its arm is PPPRRRR. The
biological experiments have shown that the PPP-tet binds to Stx2 B (B subunit of another family ofStx) but not to Stx1 B [1]. This study demonstrates that our MD simulations can reproduce thisfeature and therefore it can be an effective method for developing drugs against toxic proteins. .

[1] Nishikawa, K., Watanabe, M., Kita, E., Igai, K., Omata, K., Yaffe, M.B., and Natori, Y., A
multivalent peptide library approach identifies a novel Shiga toxin inhibitor that inducesaberrant cellular transport of the toxin. The FASEB Journal, 20:2597-9, 2006.
[2] Tsutsuki, K., Watanabe-Takahashi, M., Takenaka, Y., Kita, E., and Nishikawa, K., Shiga Toxins
1 and 2 by Targeting Specific Neutralizer That Potently Inhibits Both Identification. Infect.
Immun. 81: 2133-2138, 2013).
[3] Okumura, H., Temperature and pressure denaturation of chignolin: Folding and unfolding
simulation by multibaric-multithermal molecular dynamics method. Proteins, 80: 2397-2416,
2012.

P1-03
3D reconstruction program for coherent diffraction
patterns obtained by XFEL
Miki Nakano 1
miki.nakano@riken.jp

Slavica Jonic 2
Slavica.Jonic@impmc.upmc.fr

1

2

3

Osamu Miyashita 1
osamu.miyashita@riken.jp

Atsushi Tokuhisa 1
tokuhisa@riken.jp

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

Advanced Institute for Computational Science, RIKEN, 6 -7-1, Minatojima-minamimachi,
Chuo-ku, Kobe, 650-0047, Japan
IMPMC, Sorbonne Universités – CNRS UMR 7590, UPMC Univ Paris 6, MNHN, IRD UMR
206, 75005 Paris, France
Department of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, 464-8602, Japan

Keywords: X-ray free electron laser, Single particle ana lysis
3D structural analysis for single particles using X-ray free electron laser (XFEL) is a new
structural biology technique, which enables us to observe the molecules that are hard crystallized
such as biological molecules and living tissue in the state close to nature 1,2 . In order to restore the3D real structure of the molecule from the diffraction patterns obtained by XFEL experiments,
computational algorithms are necessary as one needs to estimate the beam incidence angles to themolecule and retrieve the phase information in Fourier space 3,4 .
We are developing a program package for XFEL analysis based on XMIPP 5 , which is commonlyused for image processing of single-particle 3D cryo electron microscopy. Our program successfully
restored 3D structure of the molecule.
[1] Hirata, K., Shinzawa-Itoh, K., Yano, N., Takemura, S., Kato, K., Hatanaka, M., Muramoto, K.,
Kawahara, T., Tsukihara, T., Yamashita, E., et al. Determination of Damage-Free Crystal
Structure of an X-Ray-Sensitive Protein Using an XFEL. Nat. methods, 11:734–736, 2014
[2] Chapman, H. N., Barty, A., Bogan, M. J., Boutet, S., Frank, M., Hau-Riege, S. P., Marchesini,
S., Woods, B. W., Bajt, S., Benner, W. H., et al. Femtosecond Diffractive Imaging with a Soft X-Ray Free-Electron Laser. Nat. Phys., 2:839–843, 2006
[3] Ekeberg, T., Svenda, M., Abergel, C., Maia, F. R. N. C., Seltzer, V., Claverie, J.-M., Hantke,
M., Jönsson, O., Nettelblad, C., van der Schot, G., et al. Three-Dimensional Reconstruction of
the Giant Mimivirus Particle with an X-Ray Free-Electron Laser. Phys. Rev. Lett., 114:098102,
2015[4] Tokuhisa, A., Jonic, S., Tama, F., Miyashita, O. Hybrid Approach for Structural Modeling ofBiological Systems from X-Ray Free Electron Laser Diffraction Patterns. J. Struct. Biol.,
194:325–336, 2016[5] Scheres, S. H., Núñez-Ramírez, R., Sorzano, C. O., Carazo, J. M., Marabini, R. Image
Processing for Electron Microscopy Single-Particle Analysis Using XMIPP. Nat. Protoc,
3:977–990, 2008

P1-04

Interaction analysis of MDM2 inhibitor by fragment
molecular orbital method
Naoto Motoyama 1

Notihito Kawashita 1,2

u449357i@ecs.osaka-u.ac.jp

kawasita@phs.osaka-u.ac.jp

Hirotomo Moriwaki 1

Yu-Shi Tian 3

Tatsuya Takagi 1,2philopon.dependence@gmail.com

yushi-tian@ist.osaka-u.ac.jp

ttakagi@phs.osaka-u.ac.jp

12
3

Graduate School of Pharmaceutical Science, Osaka University, Suita, Osaka, Japan
Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan

Keywords: MDM2, p53, Protein-protein interaction inhibitor, FMO, IFIE
The p53 protein is a transcription factor that controls the cellular resp onse to DNA damage orother stress stimuli through the induction of cell -cycle arrest, DNA repair, apoptosis, andsenescence. Since it is well known that lack of p53, which is inhibited by MDM2, can cause cancer,
the MDM2-p53 interaction was proposed to be a promising therapeutic strategy for cancers. A
number of inhibitors have been reported by several groups [1-5]. In order to obtain more
information for designing novel inhibitors using the fragment molecular orbital (FMO) method [6],we computed inter-fragment interaction energies (IFIE) of co-crystal structures of MDM2 with its
inhibitors, and evaluated the relationship between interaction energies and the reported
experimental IC50 values. 13 structures were adopted in current study. These structures wereprepared by the following steps before the FMO calculations using software package MOE2014:
modifying PDB structures with structure preparation module, adding hydrogen atoms using
protonate 3D module, and optimizing these hydrogen positions. Subsequently, the FMO
calculations were carried out by ABINIT-MP6.0+ and the calculation level was set to MP2/6-31G*.
Finally, the correlation coefficients between the interaction energies and the reported pIC 50 were
calculated. As a result, we obtained a good correlation between IFIE-SUM and experimental values(R = 0.777). Importantly, an amino residue, Phe55, played vital roles in interacting with inhibitors.
Acknowledgement
This work is a part of Construction of platform of FMO -based drug discovery using HPCI systemand this research used computational resources of the K computer provided by the RIKEN
Advanced Institute for Computational Science through the HPCI System Research project (Project
ID: hp150160).
[1] M. Miyazaki, et al., Bioorganic & Medicinal Chemistry Letters, 23:728-732, 2013
[2] X. Huang, et al., Journal of Medicinal Chemistry, 56:4053-4070, 2013
[3] A.Z. Gonzalez, et al., Journal of Medicinal Chemistry, 57:2472-2488, 2013[4] P. Holzer, et al., Journal of Medicinal Chemistry, 58:6348-6358, 2015
[5] F. Gassier, et al., Bioorganic & Medicinal Chemistry Letters, 25:3621-3625, 2015
[6] K. Kitaura, et al., Chemical Physics Letters, 313:701-706, 1999

P1-05
Hydration Thermodynamics Study of Activation of
Hydrolytic Water in Acyl-Trypsin Intermediates
Yosuke Masuda 1

masuday@pharm.kitasato-u.ac.jp

Noriyuki Yamaotsu 1

yamaotsun@pharm.kitasato-u.ac.jp

Shuichi Hirono 1

hironos@pharm.kitasato-u.ac.jp

1

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

Keywords: WaterMap, Catalytic rate constant, Hydrolytic water, Gibbs free energy[Backgrounds]
The force driving the acyl intermediate to the tetrahedral intermediate in the deacylation step ofserine proteases is not well understood. In the deacylation step, a hydrolytic water attacks the
carboxyl carbon of the acyl group, which results in a tetrahedral intermediate. We investigated thehydration thermodynamics of activation of the hydrolytic water in acyl-trypsin intermediates.

[Materials and Methods]
First, representative solution structures of five different acyl-trypsin intermediates (see [1] andother related reports) were obtained from the trajectories of molecular dynamics simulations using
Desmond 4.3 (Schrödinger). Then, hydration thermodynamics analysis was performed using
WaterMap TM 2.4 (Schrödinger) [2]. Among the predicted water sites, the hydrolytic water site was
identified in each structure, and thermodynamic properties of the hydrolytic water were analyzed.
[Results and Discussions]
All calculated Gibbs free energy (ΔG) values of hydrolytic water exhibited positive values, andlogarithms of experimental catalytic rate constant values and calculated ΔG values were positively
correlated with a remarkably high correlation coefficient (R 2 = 0.925). The result suggested thatunfavorable ΔG of the hydrolytic water may contribute to the deaylation in the catalysis.
Calculated enthalpy values and number of hydrogen bonds between acyl-trypsin and thehydrolytic water per frame showed a negative correlation. The hydrolytic waters seemed to bestabilized through hydrogen bonding to surrounding residues. Also, calculated entropy values andoccupancy values were negatively correlated. The occupancy seemed to increase when surrounding
residue prevented a water molecule escaping from the hydrolytic water site.
Consequently, we suggest that the environment of serine proteases leads to unfavorable ΔG ofhydrolytic water in cooperation with substrates, and when this hydrolytic water turns to tetrahedral
intermediate hydroxyl group, deacylation may be accelerated in proportion to the ΔG value. In the
future, the methodology developed in our study will accelerate researches on hydrolytic enzymes.
[1] Masuda, Y., Nitanai, Y., Mizutani, R., and Noguchi, S., Crystal structure of 6guanidinohexanoyl trypsin near the optimum pH reveals the acyl-enzyme intermediate to bedeacylated, Proteins Structure, Function and Bioinfomatics, 81:526-530, 2013.
[2] Young, T., Abel, R., Kim, B., Berne, B.J., Friesner, R.A., Motifs for molecular recognition
exploiting hydrophobic enclosure in protein–ligand binding, Proc. Natl. Acad. Sci. U. S. A., 104:808-813, 2007

P1-06
Approach to the quantitative prediction of thebinding affinity in biomolecular system
Yoshiaki Tanida

tanida.yoshiaki@jp.fujitsu.com

Azuma Matsuura
matsuura.azuma@jp.fujitsu.com

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

Keywords: binding affinity, free energy, metadynamics, alchemical transformation, bootstrap
Current “state-of-the-art” free energy calculations based on alchemical transformation for ligand
binding have been widely accepted in computational ligand design and optimization. Here, theprediction of the preferred orientation of the ligand bound to the target receptor is necessary. In
this work, to construct the free energy surface for ligand binding pose, metadynamics is applied.
Consequently, we propose the following workflow based on the free energy calculations coupledwith exploring metastable structures.
1. Search several metastable ligand-receptor complexes using the well-tempered metadynamics
in a suitable collective variables space.
2. Extract the equilibrium geometries using conventional molecular dynamics procedure.
3. Run equilibrium simulation at each intermediate state along the alchemical transformation
pathway.
4. Calculate free energies from the collected uncorrelated samples using the multiple trajectories,
multistate Bennett acceptance ratio.
5. Estimate statistical uncertainty with the bootstrap method.
We believe that our workflow described above will become a promising approach in innovative
drug design.

P1-07
Theoretical Comparison of the Conformational Affinity
between hOGG1 DNA Repair Protein and DNA Holding
Different Numbers of 8-OxoG
Ai Suzuki 1
ai@aki.niche.tohoku.ac.jp

Patrick Bonnaud 1
patrick@aki.niche.tohoku.ac.jp

Akira Sassa

2a-sassa@nihs.go.jp

Manabu Yasui 2

Akira Miyamoto 1

Masamitsu Honma 2m-yasui@nihs.go.jp

miyamoto@aki.niche.tohoku.ac.jp

honma@nihs.go.jp

1

2

New Industry Creation Hatchery Center, Tohoku University, 6-6-10 Aoba Aramaki, Aoba-ku,
Sendai, Miyagi 980-8579, Japan
Division of Genetics and Mutagenesis, National Institute of Health Sciences , Kamiyoga
1-18-1, Setagaya-ku,Tokyo 158-8501, Japan

Keywords: 8-oxoG, hOGG1, Accelerated Quantum Chemical Molecular Dynamics

Introduc tion: The human repair enzyme 8-oxoguanine (8-oxoG) DNA glycosylase (hOGG1) is known
to particularly remove the 8-oxoG and ignores undamaged bases, and the numbers or the position of
8-oxoG in DNA changed the frequency of mutation induction 1,2 . To elucidate the mechanism of thephenomenon, the conformational affinity of various DNAs to hOGG1 was investigated.
Theoretical Methods and Models: The Protein Data Bank ID 2I5w 3 was applied to model the initialinterrogation stage of hOGG1. Energy minimization for the DNA/hOGG1 complex was conducted for20000 steps with 0.1 fs by the accelerated quantum chemical molecular dynamics method , which
reduces computational load with maintaining accuracy to solve Shrödinger equation, HC = SCε, withthe first-principle parametrization for ionization potential, H and overlap integral, S.
Results: Conformational affinity of the DNA/hOGG1 complex (Fig. 1a) composed from double
stranded 10-base pair-DNA, 5’-CCACGAGGCT. (ca. 640 atoms) and hOGG1 (ca. 4800 atoms) was
compared with 8oxoG-DNA/hOGG1 including 5’-CCAC[8-oxoG]AGGCT carrying single 8-oxoG (Fig.
1b), and 2 ×8oxoG-DNA/hOGG1 including 5’-CCAC[8-oxoG]A[8-oxoG]GCT carrying tandem 8-oxoG(Fig. 1c) in the transcribed strand. Catalytic Lys249 for the rotation or cleavage of 8-oxoG-ribose
interatomic bond came closer to the 8-oxoG in single and tandem 8-oxoG containing DNA than no
adduct DNA. Gly245 acted repulsively at carbonyl oxygen of and kept away about 10 Å in case of
2×8oxoG/hOGG1 complex, which is farther than 8 Å in case of 8oxoG/hOGG1 complex.

(a) DNA/hOGG1(b) 8-oxoG-DNA/hOGG1 (c) 2×8oxoG-DNA/hOGG1
Fig. 1 (a) DNA/hOGG1, (b) 8oxoG-DNA/hOGG1, (c)2×8oxoG-DNA/hOGG1 complexes.
Numericals in these structures are inter-atomic distances (Å).
[1] Sassa, A., Kamoshita, N., Kanemaru, Y., Honma, M., Yasui, M., Xeroderma Pigmentosum Group A
Supresses Mutagenesis Caused by Clustered Oxidative DNA Adducts in the Human Genome, PLoS
ONE, 10(11), e0142218, 2015.
[2] Yasui, M., Kanemaru, Y., Kamoshita N., Suzuki, T., Arakawa, T., Honma , M., Tracing the fates ofsite-specifically introduced DNA adducts in the human genome, DNA Repair, 15, 11-20, 2014.
[3] Banerjee, A, Verdine, G. L., PNAS, A nucleobase lesion remodels the interaction of its normalneighbor in a DNA glycosylase complex, 103:15020-15025, 2006.
1

P1-08
Fragment Molecular Orbital Calculations on
the Interaction and Binding Energies of
Human Protease Renin and its Inhibitors
Yoichiro Yagi 1

Takatomo Kimura 2

yagi@ee.ous.ac.jp

kimura@konankako.co.jp

Chiduru Watanabe 3

Yoshio Okiyama 3

chiduru.watanabe@riken.jp

yoshio.okiyama@riken.jp

Shigenori Tanaka 4

Teruki Honma 3

Kaori Fukuzawa 5tanaka2@kobe-u.ac.jp

honma.teruki@riken.jp

fukuzawa.kaori@nihon-u.ac.jp

1

2
3

4

5

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 of Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
Department of Computational Science, Graduate School of System Informatics,
Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
School of Dentistry at Matsudo, Nihon University, 2-870-1 Sakaecho-Nishi, Matsudo,
Chiba 271-8571, Japan

Keywords: Fragment Molecular Orbital (FMO) Calculation, Renin, Inhibitor, Interaction energy,
Binding energy, Pair interaction energy decomposition analysis (PIEDA)
We have focused our attention on the Fragment Molecular Orbital (FMO) simulations of thecomplexes of proteins with ligands to elucidate the function of proteins by analyzing of theinteraction between protein and ligand [1,2]. A human protease, renin, is the first component of therenin-angiotensin system in hypertension. This presentation describes the FMO calculations on theinteractions of renin and its inhibitors. The PDB structures of 22 different inhibitor-bound renincomplexes were optimized respectively by an energy minimization with explicit water molecules
such as TIP3P model using AMBER9. After energy minimization, the surrounding water molecules
were removed and for the resulting renin complexes FMO calculations withABINIT-MP/BioStation program were carried out at FMO2-MP2/6-31G* level on the K computer.
We computed the inter-fragment interaction energy (IFIE) between inhibitor and the amino acidresidues in renin, and estimated the sum of IFIEs as binding energy between inhibitor and renin.
FMO computations indicated that the calculated binding energy can be correlated to the activity
value on 50% inhibitory concentration (IC 50 ) of the inhibitors. In addition, we performed pairinteraction energy decomposition analysis (PIEDA) calculation to examin e 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: hp150160 and hp160103). PIEDA calculation was
done by using MIZUHO/BioStation software package.
[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., Tanaka, T., Imagawa, A., Moriya, Y., Mori, Y., Kimura, T., Kamezawa, M., andNaoshima, Y., Large-Scale Biomolecular Chemical Computations toward the Prediction of
Burkholderia cepacia Lipase Enantioselectivity, Journal of Advanced Simulation in Science andEngineering, 1:141-160, 2014.

P1-09Construction of a platform for protein-ligand binding
affinity prediction: Application to colony-stimulating
factor-1 receptor (CSF-1R)
Kentaro Takai 1 , Mutsuyo Wada 1 , Hajime Sugiyama 1 , Takashi Mitsui 1 ,Yoshiaki Tanida 2 , Hiroyuki Sato 2 , Azuma Matsuura 2 , Akihiko Ueda 1 , and Shunji Matsumoto 1
takai.kentaro@jp.fujitsu.com

1

2

BIO-IT R&D Office, Research & Development Division, Healthcare Systems Unit, Fujitsu
Limited, 1-17-25 Shin-kamata, Ohta-ku, Tokyo 226-8503, Japan
Fujitsu Laboratories Limited, 10-1 Morinosato-Wakamiya, Atsugi, Kanagawa 243-0197, Japan

Keywords: free energy, binding affinity prediction, alchemical transformation, IT-Soyaku
Aiming to make a breakthrough in drug discovery, we have developed technology of IT-based
drug design (IT-Soyaku)[1]. One of the key technologies is the protein -ligand binding affinity
prediction using the alchemical free energy calculation[2]. The protocol has been implemented in
the latest version of MAPLE CAFEE TM (Massively Parallel Computation of Absolute Binding Free
Energy)[1]. For the execution of this scheme, we have to perform hundreds of molecul ar dynamics(MD) simulations on the large-scale computer systems (hundreds or thousands of cores). If systemfaults occur in some machines, we cannot execute calculations on schedule. Then, we haveconstructed a computing platform where all of the calculations are monitored under a program
specialized for control, failure of the machine is automatically detected and simulation iscontinually executed on other machines. In order to build the robust system, we adapted the double
surveillance function; each MD simulation is controlled by individual monitor processes, and all ofmonitor processes are supervised by other process.
On the constructed platform, we performed absolute free energy calculations for three co-crystal
structures of CSF-1R[3,4,5], and obtained the results within 1 day using 2240 cores per a
compound even if some troubles took place. Here, the protocols we used in these calculations arebriefly described. The harmonic restraint between a protein and a ligand has been used to define
the volume of sampling for the standard state correction[6,7]. We also applied the multistate
Bennett’s acceptance ratio to evaluate free energies[8]. Our obtained values agree well with theexperimental ones. We expect that our constructed platform drives technology of IT-Soyaku.
[1] W. Mitchell, et al., Current Opinion in Chemical Biology, 15:553-559, 2011.
[2] Y. Tanida, アンサンブル, vol. 17, No. 2, April 2015.
[3] C. Schubert, et al., J. Biol. Chem., 282:4094-4101, 2006.
[4] M. J. Meyers, et al., Bioorg. Med. Chem. Lett., 20:1543-1547, 2010.
[5] C. R. Illig, et al., J. Med. Chem., 54:7860-7883, 2011.
[6] J. Hermans, et al., Ist. J. Chem., 27:225-227, 1986.
[7] G. Jayachandran, et al., J. Chem. Phys., 125:084901, 2006.
[8] M. R. Shirts, et al., J. Chem. Phys., 129, 124105, 2008.

P1-10Estimating the qualitative and quantitative capability of
the MM/3D-RISM method
Masatake Sugita 1

1

2

Takeshi Hasegawa 1

m-sugita@fc.ritsumei.ac.jp

sj0017hf@ed.ritsumei.ac.jp

Takeshi Kikuchi 1

Fumio Hirata 2

tkikuchi@sk.ritsumei.ac.jp

hirata@toyotariken.jp

Department of Bioinformatics, College of Life Science, Ritsumeikan University, 1-1-1,
Noji-higashi, Kusatsu, Shiga, 525-8577, Japan
Toyota Physical & Chemical Research Institute, Nagakute, Aichi 480-1192, Japan

Keywords: 3D-RISM, molecular dynamics, binding free energy
In-silico screening of drugs requires the prediction of a free energy difference between bound andunbound states of host and guest molecules in aqueous solutions. The binding free energy consistsof three contributions: the interaction energy of the solute molecules, structural entropy change ofthe solute molecules, and the desolvation free energy. However, the estimation of the structural
entropy change and the desolvation free energy is a difficult task, since these quantities can be
estimated by exploring the vast extent of the configuration space of the solvent molecules in
addition with the solute molecules.
Considering such a situation, we have started to apply the MM/3D-RISM method[1], which is the
combined approach based on the molecular dynamics simulation and the 3D-RISM theory[2], tothe binding free energy prediction. Since MM/3D-RISM method needs only the structuralensemble of the solute molecules for estimating the desolvation free energy, this method enables toreduce the computational cost for sampling of the solvent molecules. However, the quantitative
capability of the MM/3D-RISM method have been ambiguous so far since this method is somehow
novel and have been applied to a few systems.
In this study, we applied the MM/3D-RISM method to a relatively simple system, inclusion
process of a cyclodextrin, and typical protein-ligand system, Pim-1 kinase and its inhibitors andtried to assess the quantitative capability of the MM/3D-RISM method. As a result, our procedure
reproduces reasonable correlation of the experimental and calculated binding free energy both ofinclusion process of the cyclodextrin[3] and Pim-1 kinase – inhibitor system. We conclude that the
MM/3D-RISM method have an ability to distinguish the tightly bind compounds from thecandidate molecules.

[1]

S. Genheden et al., “An MM/3D-RISM Approach for Ligand Binding Affinities,” J. Phys.
Chem. B, 114: 8505–8516, 2010.

[2]

A. Kovalenko and F. Hirata, “Three-dimensional density profiles of water in contact with asolute of arbitrary shape: a RISM approach,” Chem. Phys. Lett., 290: 237–244, 1998.

[3]

M. Sugita and F. Hirata, “Predicting the binding free energy of the inclusion process of
2-hydroxypropyl-β-cyclodextrin and small molecules by means of the MM/3D-RISM
method,” J. Phys.: Condens. Matter, 28: 384002, 2016.

P1-11
Computational analysis of interaction between
HLA-B*13:01 with Dapson responsible for drug-inducing
hypersensitivity syndrome
Hiroaki Gouda 1

Yurie Watanabe 1

godah@pharm.showa-u.ac.jp

ywatanabe@pharm.showa-u.ac.jp

Daisuke Kuroda 1
dkuroda@pharm.showa-u.ac.jp

1
2

Yoshio Kusakabe 2
yoshio@pharm.teikyo-u.ac.jp

Naoko Utsunomiya-Tate 2tate@pharm.teikyo-u.ac.jp

School of Pharmacy, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
Faculty of Pharma Sciences, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan

Keywords: Homology modeling, Docking, Molecular dynamics simulation, HLA, Dapson
Drug-induced hypersensitivity syndrome (DIHS) is a severe drug eruption marked by fever,redness, liver dysfunction, and high mortality rate (10-20 %) [1]. Dapson is one of medicines
causing DIHS. Recent study reports that human leukocyte antigen (HLA) allele, HLA-B*13:01 was
identified as a marker of susceptible individual for DIHS related to Dapson [2]. Interestingly,
HLA-B*13:02 having high sequence identity with HLA-B*13:01 over 99 %, is not reported to haverelated with DIHS. The different amino acid residues between HLA-B*13:01 and HLA-B*13:02
were only three. Ile118, Ile119, and Arg121 of HLA-B*13:01 are mutated to Thr, Trp, and Thr,
respectively, in HLA-B*13:02. In order to investigate the reason why only HLA-B*13:01 is
involved in the onset of DIHS from a structural point of view, we performed computational
analysis of Dapson-HLA-B*13:01 and Dapson-HLA-B*13:02 interactions using homology
modelling, molecular docking, and binding free energy calculation combined with molecular
dynamics (MD) simulation and Molecular mechanics Poisson-Boltzman surface area
(MM-PBSA) methods .
As a results of homology modelling, we identified a crucial structural difference in the antigen
binding sites between HLA-B*13:01 and HLA-B*13:02. We observed an additional smallhydrophobic pocket in HLA-B*13:01, as Trp119 of HLA-B*13:02 was substituted by less bulky
Ile in HLA-B*13:01. All of three representative docking poses of Dapson for HLA-B*13:01 used
this unique small pocket, indicating that it seems to be suitable for Dapson-binding. On the other
hand, Dapson seems to bind to HLA-B*13:02 at several different locations. Finally, the bind freeenergy calculations suggested that the binding affinity of Dapson with HLA-B*13:01 is muchgreater than that with HLA-B*13:02. This result indicated that Dapson would give more influence
to the antigen-recognition site of HLA-B*13:01 than that of HLA-B*13:02, which might beassociated with the incidence of DIHS.
[1] Criado PR, Criado RF, Avancini JM, Santi CG, An Bras Dermatol. 87:435-449, 2012.
[2] Watanabe H. J Dermatol.. 38:229-235, 2011.

P1-12
An approach for analyzing protein-ligand binding pockets
using probe molecules
Hiroyuki Sato 1
shryk@jp.fujitsu.com

1

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

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

Keywords: Binding pocket, Probe molecule, Water, Fluctuation of pocket
We present a novel and efficient approach for analyzing protein-ligand binding pockets. Firstly, alarge number of probe molecules were randomly distributed in the unit cell of protein, andconstrained molecular dynamics (MD) simulation was carried out using NVT ensemble. Thenwater molecules were distributed to fill the vacuum space of the unit cell, and partly-constrained
MD simulation was carried out using NPT ensemble. We applied this approach to humancoagulation factor Xa (fXa)[1]. As a result, we found that the fluctuation of the pocket has animportant role for identifying the appropriate orientation of a probe molecule in the pocket. Tosummarize, partly-constrained MD with a large number of probe molecules provides useful
information for analyzing protein-ligand binding pockets.
[1] Maignan, S., et al., V. J. Med. Chem. 2003, 46, 685-690.

P1-13
Fragment ER : Efficient and Accurate Binding Free
Energy Calculation Method for Protein-ligand Complex
Tomohide Masuda 1

Ryuji Tanimura 1

Tomohide_Masuda@nts.toray.co.jp

Ryuji_Tanimura@nts.toray.co.jp

Nobuyuki Matubayasi 2

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

12

Pharmaceutical Research Laboratories, Toray Industries, Inc., 6-10-1, Tebiro,
Kamakura, Kanagawa 248-8555, Japan
Division of Chemical Engineering, Graduate School of Engineering Science,
Osaka University, 1-3, Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan

Keywords: Molecular dynamics simulation, Binding free energy, Energy representation
The accurate prediction of binding affinities (K d ) of small molecules to the target protein is a
crucial interest issue in the drug discovery process from early hit finding phase through final
optimization of drug candidates, as the logarithm of an experimental binding affinity is exactly
proportional to the Gibbs binding free energy.
Although the various methodologies for computing binding free energy with atomistic molecularmodels have been developed, most of them have trade-off relation between accuracy andcomputational efficiency, i.e., rough though quick or accurate though time-consuming.
On the other hand, one of us (Matubayasi) has developed a theory of solution in the energy
representation (ER) [1] in which the solvation free energy can be obtained from relatively shortmolecular simulations performed at two end-points, the pure solvent and solution system, and that
does not involve any adjustable parameters. Since the theory does not take into account theintermediate states in the solute insertion process, the computation time to determine the solvation
free energy is considerably reduced compared to the exact methods such as FEP that requiresconsiderable samplings of a large number of intermediate states.
We have developed the “Fragment ER” method by applying the ER theory to “binding” freeenergy calculations of small molecules bound to the target protein [2]. Fragment ER calculates therelative binding free energy efficiently and accurately between two similar molecules having thecommon scaffold and differing in the substructure (“fragment”) such as substituents. The results
and comparison with FEP and experimental data will be reported in this meeting.
[1] N.Matubayasi et al., JCP 113, 6070 (2000); 117, 3605 (2002); 119, 9686 (2003).
[2] T.Masuda et al., The 29th meeting of the Molecular Simulation Society of Japan (2015).

P1-14
Toward the rapid in silico screening of mutations toimprove the thermal stability of proteins
Kanji Oshima 1

Shinichi Yoshida 1

Kanji.Oshima@kaneka.co.jp

Shinichi.Yoshida@kaneka.co.jp

Yuji Okubo 1
Yuji.Okubo@kaenka.co.jp
1

KANEKA CORPORATION 1-8, Miyamae-cho, Takasago-cho, Takasago Hyogo 676-8688
Japan

Keywords: Mutation, In silico screening, Protein modeling, thermal stability
The accurate prediction of the effect of mutations on the thermal stability of proteins is importantin protein engineering, and is very useful for the rational design of modified proteins, keeping
stable and active in unusual conditions such as at high temperature. The mutation effect on thethermal stability of proteins is associated with the difference of a melting temperature Tm, i.e. thetemperature at which the proteins unfold. Since it is very complicated to predict the effect ofmutations through Tm, a standard folding free energy dG is alternatively used to evaluate themutation effect on the thermal stability. To estimate the precise value of dG, molecular dynamics
simulation such as free energy perturbation method is generally used, which is very
time-consuming. Here, we propose the rapid in silico screening method of mutations to improve
the thermal stability of proteins using a short time molecular dynamics simulation with implicit
solvent model. In this method, we use the difference in the potential energy (dE) between folded
structure and unfolded structure as a measure of the thermal stability of proteins. The initialstructure of the modified protein with mutations is mo deled by using the crystallographic structure
as a template with a modeling tool for protein side chain prediction. The unfolded structure ismade by extending the conformation of the protein so that the shape is an almost straight bar. We
apply this method to evaluate the thermal stability of protein G beta1 domain [1-2] and compared
the calculated values of dE with experimental values of Tm. We succeeded in getting thecorrelation coefficient of the calculated and the experimental data of ~0.7.
[1] Su, A., Mayo, L. S., Coupling backbone flexibility and amino acid sequence selection inprotein design , Protein Science, 6:1701-1707, 1997
[2] Malakauskas, S.M., Mayo, S.L., Design, structure and stability of a hyperthermophilic protein
variant, Nature Structural & Molecular Biology, 5:470-475, 1998

P1-15
Estimation of binding free energy for the Pim1
kinase-ligand system based on the MM/3D-RISM method
Takeshi Hasegawa 1

sj0017hf@ed.ritsumei.ac.jp

Takeshi Kikuchi 1

tkikuchi@sk.ritsumei.ac.jp

12

Masatake Sugita 1

m-sugita@fc.ritsumei.ac.jp

Fumio Hirata 2

hirata@toyotariken.jp

Department of Bioinformatics, College of Life Science, Ritsumeikan University, 1-1-1
Noji-higashi, Kusatsu, Siga 525-8577, Japan
Toyota Physical & Chemical Research Institute, Nagakute, Aichi, 480-1192, Japan

Keywords: 3D-RISM, binding free energy
To seek drug candidate molecules, it is necessary to predict the binding free energies between aprotein and its ligands. Although many computational methods for predicting the binding freeenergy have been developed, some of those methods have disadvantages for evaluating thecontribution from the solvent molecules. On the other hand, 3D-RISM theory[1], a statisticalmechanical theory for the molecular liquid, analytically calculates configuration integral aboutsolvation, and can assess the solvation structure and solvation free energy along the protein
structure in a reasonable computational cost. In this regards, the combination of the 3D-RISM
theory and MD simulations, called MM/3D-RISM method[2], can reduce the computational costfor the sampling of the solvent molecules since this combination of the methods reduces the
sampling space to only of the conformational space of solute molecules. However, the quantitative
capability of the MM/3D-RISM method has been ambiguous since this method has been applied toa few system.
In this study, we apply the method to estimate binding free energies between Pim1 kinase andits inhibitors. Pim1 kinase is expected as a target protein of therapeutic agents for cancer, such as
leukemia and lymphoma[3]. As a result, our procedure reproduces reasonable correlation between
the experimental and calculated binding free energies. Furthermore, we also analyse thedifferences in the characteristics of the tightly bound ligands and unstable ligands.
[1] A.kovalenko and F.Hirata, “Three-dimensional density profiles of water in contact with soluteof arbitrary shape: a RISm approach,” Chem. Phys. Lett., 290:237-244, 1998
[2] S.Genhenden et al., “An MM/3D-RISM Approach for Ligand Binding Affinities,” J.phys.
Chem.B, 114:8505-8516, 2010
[3] Ron Grey, Albert C. Pierce, Guy W. Bemis, Marc D. Jacobs, Cameron Stuver Moody, Rahul
Jajoo,
NarinderMohal,
JeremyGreen,“Structure-based
designof
3-aryl-6-amino-triazolo[4,3-b]pyridazine inhibitors of Pim-1 kinase,” Bioorganic & Medicinal
Chemistry Letters 19 (2009) 3019–3022.

P1-16
Inhibition Mechanisms of Carbapenem Antibiotics on
Acylpeptide Hydrolase: Docking Simulation Study
Hiroki Otaki 1,2

otaki@nagasaki-u.ac.jp

Satoshi Mizuta 1,2

s-mizuta@nagasaki-u.ac.jp

1

2

Takeshi Ishikawa 1

t-ishi@nagasaki-u.ac.jp

Kaname Ohyama 1

k-ohyama@nagasaki-u.ac.jp

Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki
852-8523, Japan
Center for Bioinformatics and Molecular Medicine, Graduate School of Biomedical Sciences,
Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan

Keywords: Protein modeling, Docking simulation, Drug-drug interaction
Valproic acid (VPA) has a broad spectrum of anticonvulsant activity and is the most commonlyused antiepileptic drug. VPA is often applied in combination with other anticonvulsant agents.
Numerous types of drug-drug interaction (DDI) between concomitantly administrated drugs were
known. Among interactions, it was first reported in 1997 that serum level of VPA was drasticallydecreased by carbapenems (CBPMs) [1], such as panipenem and meropenem, which have a broad
spectrum of antibacterial activity with moderate activity against Gram-positive bacteria andexcellent activity against Gram-negative aerobics and anaerobics. This DDI results in insufficientconcentration of VPA to prevent epileptic fit and increase of the risk of recurrence of epileptic
seizures. After the first report by Nagai et al, several reports outlined a significant interaction
between VPA and CBPMs with a marked decrease in VPA concentration and neurological adverse
events [2].
Although several mechanisms are proposed to explain it, the mechanism of the DDI has yet to besolved. Among the proposed mechanisms, inhibition of deconjugation is thought to be the most
reasonably explain the DDI. Suzuki et al. systematically investigated the DDI based on inhibitionof deconjugation of VPA-glucuronide (VPA-G) by CBPMs and identified the key enzyme as
acylpeptide hydrolase (APEH), in which hydrolysis of VPA-G to VPA is inhibited by CBPMs [3].
However, molecular mechanism of APEH inhibition has not been elucidated. To address thisproblem, we employed computational method to obtain structural information and inhibition
mechanism of APEH by CBPMs. The structure model of APEH were constructed by homology
modeling and interactions between APEH and several compounds, including VPA-G, meropenem
and panipenem, were investigated by docking simulation. The results of the calculations andmolecular mechanism of the DDI mediated by APEH will be discussed in this presentation.
[1] Nagai, K., Shimizu, T., Togo, A., Takeya, M., Yokomizo, Y., Sakata, Y., Matsuishi, T., Kato,
H., Decrease in serum levels of valproic acid during treatment with a new carbapenem,
panipenem/betamipron, Journal of Antimicrobial Chemotherapy, 39:295-296, 1997.
[2] Clause, D., Decleire, P.-Y., Vanbinst, R., Soyer, A., Hantson, P., Pharmacokinetic interaction
between valproic acid and meropenem, Intensive Care Medicine, 31:1293-1294, 2005.
[3] Suzuki, E., Yamamura, N., Ogura, Y., Nakai, D., Kubota, K., Kobayashi, N., Miura, S.,
Okazaki, O., Identification of valproic acid glucuronide hydrase as a key enzyme for theinteraction of valproic acid with carbapenem antibiotics, Drug Metabolism and Disposition,
38:1538-1544, 2010.

P1-17Prediction of peptide binding to a major histocompatibility
complex class I molecule based on docking simulation
Takeshi Ishikawa 1
t-ishi@nagasaki-u.ac.jp

1

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

Keywords: MHC class I molecule; docking simulation; structure-based method.

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic
epitopes is one of the most important processes for cell-mediated immunity. Computational
prediction of amino acid sequences of MHC class I binding peptides from a given sequence maylead to important biomedical advances. In this study, an efficient structure -based method forpredicting peptide binding to MHC class I molecules was developed [1], in which the binding freeenergy of the peptide was evaluated by two individual docking simulations. An original penalty
function and restriction of degrees of freedom were determined by analysis of 361 published X-ray
structures of the complex and were then introduced into the docking simulations. To validate themethod, calculations using a 50-amino acid sequence as a prediction target were performed. In 27
calculations, the binding free energy of the known peptide was within the top five of 166 peptides
generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole
sequence of a protein as a prediction target were performed. These data clearly demonstrate highpotential of this method for predicting peptide binding to MHC class I molecules.
[1] Ishikawa, T. Prediction of peptide binding to a major histocompatibility complex class I
molecule based on docking simulation. J. Comput. Aid. Mol. Des. , in press (DOI;
10.1007/s10822-016-9967-3)

P1-18
Molecular Simulation Analysisof RNA Aptamer to Human Immunoglobulin G
Hisae Yoshida 1

Toshiki Fukaya 1

Masahiro Sekiguchi 1

cehi15013@g.nihon-u.ac.jp

ceto13138@g.nihon-u.ac.jp

cema16020@g.nihon-u.ac.jp

Emire Inomata 2

Kazumasa Akita 2

Takeshi Ishikawa 3

e.inomata@ribomic.com

k.akita@ribomic.com

t-ishi@nagasaki-u.ac.jp

Taiichi Sakamoto 4
taiichi.sakamoto@p.chibakoudai.jp

Kenji Yamagishi 1
yamagisi@chem.ce.nihon-u.ac.jp

1

College of Engineering, Nihon University, 1 Nakagawara, Tokusada, Tamura, Koriyama,
Fukushima 963-8642, Japan
2
Ribomic Inc., 3-16-13 Shirokanedai, Minato-ku, Tokyo 108-0071, Japan
3
Graduate School of Biomedical Sciences, Nagasaki Unive rsity, 1-12-4 Sakamoto, Nagasaki,
Nagasaki 852-8523, Japan
4
Faculty of Advanced Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino,
Chiba 275-0016, Japan

Keywords: RNA aptamer, Fragment molecular orbital, Molecular dynamics
RNA aptamers are short single-stranded nucleic acids with high affinity and specificity for
their target molecules, which can be nucleic acids, proteins, or small organic compounds.
Aptamers, therefore, have many potential applications in medicine and tec hnology.
In order to be used in biological applications, the sugar moiety of the RNA aptamer has to besuitably modified to make it resistant to nuclease digestion. Recently, an optimized 23 -nucleotide
aptamer was designed, 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]. However, the structural
basis of the interaction of the RNA aptamer with hFc1 is poorly
understood.
In this study aimed at elucidating the specific binding
mechanism, we carried out ab initio fragment molecular orbital(FMO) calculations for the aptamer/hFc1 complex (Figure). We
analyzed the energy of interaction between each nucleotide andeach residue by using the inter-fragment interaction energy (IFIE)
analysis based on FMO calculations. Furthermore, we performed
molecular dynamics (MD) calculations for some aptamers inorder to elucidate the conformational behaviors and dynamical
features of aptamers.
The final goal of this study was to evaluate the affinity ofchemically modified aptamers for the target protein using thisFigure. The crystal structureof the aptamer/hFc1 complex
method.
[1] S. Miyakawa, et al., RNA, 14:1154-1163, 2008.
[2] Y. Nomura, et al., Nucleic Acids Research, 38:7822-7829, 2010.

P1-19Analysis of Intermolecular Interactions between Measles Virus
Hemagglutinin and Receptors by the Fragment Molecular Orbital
MethodFengqi Xu1*, Shigenori Tanaka1*, Hirofumi Watanabe2, Yasuhiro Shimane3,
Misako Iwasawa4, Kazue Ohishi5, Tadashi Maruyama3
1
2
3
4
5

Department of Computational Science, Graduate School of System Informatics, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
Center for Collaborative Research and Technology Development, Kobe
University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501, Japan
Center for Marine Biosciences, Japan Agency for Marine-Earth Science andTechnology, 2-15, Natsushima, Yokosuka, Kanagawa 237-0061, Japan
Center for Earth Information Science and Technology, Japan Agency for Marine-Earth
Science and Technology, 2-15, Natsushima, Yokosuka, Kanagawa 237-0061,Japan
Faculty of Engineering, Tokyo Polytechnic University, 1583, Iiyama,
Atsugi, Kanagawa 243-0297, Japan*email: xu@eniac.scitec.kobe-u.ac.jp, tanaka2@kobe-u.ac.jp

Keywords: Measles virus, Receptor, Interaction energy analysis, Fragment Molecular Orbital (FMO) method,
IFIE (Inter-Fragment Interaction Energy)

Measles virus has been one of the major causes of childhood morbidity and mortality worldwide. Threemeasles virus receptors (SLAM, CD46, nectin-4) have been identified so far, and in this study, we use the
Fragment Molecular Orbital (FMO) method [1] to investigate the interaction energy between the hemagglutininof measles virus (MV-H) and the three respective receptors based on the PDB conformation of the complexes[2-4]. The calculation was performed at FMO-MP2/6-31G* level. On the basis of the calculated IFIE [5,6](Inter-Fragment Interaction Energy) results between the MV-H and the three receptors, we found the stronglyinteracting amino acid residues in hemagglutinin which play an essential role in binding to receptors. Then we
carried out FMO calculations also for different structures which were obtained by mutating some importantamino acid residues in the conformation of hemagglutinin of measles virus. For example, by comparing the
IFIEs, we observed that Arg533 in MV-H is important in binding to SLAM (the predominant receptor for wildtype), which was consistent with the earlier virological studies [7-9]. The present study has shown that the FMO
method is a powerful tool to comprehensively search for those amino acid residues contributing to interactions
with receptor molecules. It is also useful for the drug design of inhibitor and for a construction of engineered
measles virus for cancer therapy.
References[1] K. Kitaura, E. Ikeo, T. Asada, T. Nakano, M. Uebayasi, Chem. Phys. Lett., 313: 701, 1999.
[2] C. Santiago et al., Nature Structural & Molecular Biology, 17: 124, 2010.
[3] T. Hashiguchi et al., Nature Structural & Molecular Biology, 18: 135, 2011.
[4] X. Zhang et al., Nature Structural & Molecular Biology, 20: 67, 2013.
[5] T. Nakano, T. Kaminuma, T. Sato, Y. Akiyama, M. Uebayasi, K. Kitaura, Chem, Phys. Lett., 318: 614, 1999.
[6] S. Tanaka, Y. Mochizuki, Y. Komeiji, Y. Okiyama, K. Fukuzawa, Phys. Chem. Chem. Phys., 16: 10310, 2014.
[7] N. Masse et al., Journal of Virology, 78: 9051, 2004.
[8] T. Nakamura et al., Nature Biotechnology, 22: 331, 2004.
[9] S. Vongpunsawad et al., Journal of Virology, 78: 302, 2004.

P1-20Re-docking by Analyzing the Profile
of Protein-protein Interaction
Yosuke Amano 1
13rb006@ms.dendai.ac.jp

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

Nobuyuki Uchikoga 2uchikoga@phys.chuo-u.ac.jp

12

Department of Science and Engineering , Tokyo Denki University, Ishizaka, Hatoyama-machi,
Hiki-gun, Saitama 350-0394, Japan
Department of Physics, Chuo University, 1-13-27 kasuga, Bunkyo-ku, Tokyo 112-8551, Japan

Keywords: rigid-body docking, post-docking analysis
Various proteins exert molecular functions through forming a protein complex. Elucidation of acomplex structure is essential to understand its molecular functions. At present, the number ofcomplex structures in PDB is not enough for us to understand the information coded on theprotein-protein interaction network. In silico analyses, docking simulation has been applied toaugment the lack of complex structure information. In general, software for docking simulation
rotates a protein (ligand) around its interaction partner protein (receptor) to generate many
complex structure candidates. Hereafter, these candidates are referred to as decoys. Similarcomplex structures to the native structure (near-native structures) are expected to exist among the
decoys. However, in some protein pair cases, there is no near-native structure in a set of decoys
because of the shortage of docking space, that is one of the important problems to be solved.
This problem was addressed by expanding the docking space based on re-docking strategy [1]. In
general, re-docking is a second round docking step after the initial docking. Besides, analyses ofInteraction FingerPrints (IFPs) [2] have been proved to obtain near-native structures efficiently byfocusing on the docking space corresponding to a decoy. To our knowledge, however, there is notool for re-docking by analyzing IFPs.
Hence, we developed a software named Pftkool that is a tool for re-docking by calculating andclassifying IFPs. Firstly, Pftkool performs the initial docking by MEGADOCK [3] to generate a setof 2,000 decoys. Secondly, IFP of a receptor (r-IFP) is calculated as an N-dimensional vector foreach decoy. N represents the number of residues of the receptor. Each element of a vector is thenumber of residues of a ligand interacting with a receptor residue. Thirdly, decoys are classified
into groups based on the distance calculated from r-IFPs. Then, decoys in each group have similarinteracting residues of the receptor. Fourth, r-IFPs of the decoys in a group are assembled into an
assembled r-IFP. Fifth, Pftkool performs re-docking by MEGADOCK to generate another set of
2,000 decoys by using the assembled r-IFP in a group. In this step, interacting residues shared in agroup and their neighboring residues are exclusively used for re-docking. Thus, Pftkool enables us
to perform initial docking, re-docking, and several other data processing. We intend to provide
Pftkool free of charge.
[1] Uchikoga, N., Matsuzaki, Y., Ohue, M., Hirokawa, T., Akiyama, Y., PLOS ONE, 8(7):e69365, 2013.
[2] Uchikoga, N., Hirokawa, T., BMC bioinformatics 2010, 11:236-245, 2010.
[3] Masahito, O., Takehiro, S., Shuji, S., Yuri, M., Takashi, I., Yutaka, A., Bioinformatics, 30(22):3281-3283, 2014.

P1-21
Application of FMO-LCMO method to Protein-Ligand
system1

2

Takao Otsuka 1

Noriaki Okimoto 1

totsuka@riken.jp

okimoto@riken.jp

Makoto Taiji 1

Shinji Tsuneyuki 2

taiji@riken.jp

stsune@phys.s.u-tokyo.ac.jp

RIKEN Quantitative Biology Center (QBiC), QBiC Building B, 6-2-4 Furuedai, Suita, Osaka
565-0874, Japan
Department of Physics, The University of Tokyo, 7 -3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan

Keywords: Electronic structure calculation, fragment molecular orbital method, one-electron
orbital
One of the most popular strategy to obtain the protein-ligand binding energy by electronic
structure calculations is to use the fragment molecular orbital (FMO) method. The FMO method
was developed as a way of calculating the QM electronic states of whole systems through thefragment based type of strategy 1 . Recently, we proposed an efficient scheme for protein -ligand
binding energy by using the FMO method and have shown the validity of our scheme 2 .
On the other hand, we also have focused on the molecular orbitals of protein-ligand system.
Recently, we have developed the FMO-linear combination of molecular orbitals (LCMO), which isan efficient post-process calculation of one-electron orbitals of the whole system after the FMO
total energy calculation 3,4 . In this method, the Hamiltonian of the whole system is constructed fromthe Hamiltonian submatrices with respect to the MOs of each fragment as the basis set. The
Hamiltonian submatrix elements are calculated by using the fragment orbital energies, fragment
MO coefficients, and fragment overlap matrices during the FMO step. The one-electron orbitals
and orbital energies of the whole system can be obtained by (one-shot) diagonalizing the whole
Hamiltonian matrix.
In this study we report further study on the application of FMO-LCMO method to theprotein-ligand systems and demonstrate the one-electron orbitals of protein-ligand system.
[1] Kitaura, K., Ikeo, E., Asada, T., Nakano, T., Uebayasi, M., Chem. Phys. Lett., 313, 701, 1999.
[2] Otsuka, T., Okimoto, N., Taiji, M., J. Comput. Chem., 36, 2209, 2015.
[3] Tsuneyuki, S., Kobori T., Akagi K., Sodeyama K., Terakura K., Fukuyama H., Chem. Phys. Lett.
476, 104, 2009.
[4] Kobori T., Sodeyama K., Otsuka T., Tateyama Y., Tsuneyuki S., J. Chem. Phys. 139, 094113,
2013.

P1-22The molecular simulation for predicting RNA duplex
dimerization free-energy changes upon mutations and three
dimensional protein-RNA complex structures
Shun Sakuraba 1
sakuraba@cbms.k.u-tokyo.ac.jp

1

2

3

Junichi Iwakiri1
iwakiri@cb.k.u-tokyo.ac.jp

Michiaki Hamada2
mhamada@waseda.jp

Kiyoshi Asai 1,3

Tomoshi Kameda3

asai@k.u-tokyo.ac.jp

kameda-tomoshi@aist.go.jp

Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa-shi, Chiba 277-8561, Japan
Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering,
Waseda University, 55N-06-10, 3-4-1, Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science andTechnology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan

Keywords: RNA, protein, docking simulation, molecular dynamics simulation, thermodynamics
We are studying RNA with the method of molecular simulations. In this poster, we show following
two results.
First, we show a prediction of RNA duplex dimerization free-energy changes upon mutations using
molecular dynamics simulations, and its comparison to the experimental data. 1 A linear regression for
nine pairs of double-stranded RNA sequences, six base pairs each, yielded a mean absolute deviation of
0.55 kcal/mol and an R 2 value of 0.97, indicating a quantitative agreement between simulations andexperimental data. The observed accuracy indicates that the molecular dynamics simulation with thecurrent molecular force field is capable of estimating the thermodynamic properties of RNA molecules.
Second, we show a prediction of RNA–protein three dimensional structures. 2 It is often difficult todetermine the tertiary structure of these complexes experimentally, and thus an accurate rigid bodydocking for RNA–protein complexes is needed. In general, the rigid body docking process is divided
into two steps: generating candidate structures from the individual RNA and protein structures and thennarrowing down the candidates. In this study, we focus on the former problem to improve theprediction accuracy in RNA–protein docking. Our method is based on the integration ofphysicochemical information about RNA into ZDOCK, which is known as one of the most successful
computer programs for protein–protein docking. Because recent studies showed the current force field
for molecular dynamics simulation of protein and nucleic acids is quite accurate, 1 we modeled thephysicochemical information about RNA by force fields such as AMBER and CHARMM. A
comprehensive benchmark of RNA–protein docking, using three recently developed data sets, reveals aremarkable prediction accuracy of the proposed method compared to existing programs for docking.
The highest success rate is 34.7% for the predicted structure of the RNA–protein complex with the bestscore and 79.2% for 3,600 predicted ones, which may be the world record.

[1] Shun Sakuraba, Kiyoshi Asai and Tomoshi Kameda, Predicting RNA Duplex Dimerization
Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations, J. Phys. Chem.
Lett. 2015, 6, 4348−4351.
[2] Junichi Iwakiri, Michiaki Hamada, Kiyoshi Asai, and Tomoshi Kameda., Improved Accuracy in
RNA–Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics
Simulation into the Scoring Function, J. Chem. Theory Comput. (in press).

P1-23
Molecular dynamics simulation of binding free energy
between protein and ligand

Teruhisa S. Komatsu 1

Noriaki Okimoto1

teruhisa.komatsu@riken.jp

okimoto@riken.jp

Makoto Taiji1
taiji@riken.jp

1

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

Keywords: molecular dynamics simulation, protein-ligand binding affinity
For rational drug design, the computational estimation of the precise protein-ligand binding free energies
is essential. Aiming at the selection of compounds in hit-to-lead and lead optimization process, rather highprecision (around the order of thermal energy kBT=2.5kJ/mol) for prediction of the binding free energies
should be established. Atomistic (classical mechanical) force fields optimized for organic molecules havebeen developed[1-2], and methods to calculate binding free energies by molecular dynamics simulations
are proposed[3-7]. Nevertheless, it seems that prediction of binding free energy by the molecular dynamics
simulation is not yet guaranteed for use in practical drug development. There is a room for further
investigations to clarify the difficulties and for developing more efficient schemes.
Here we applied a variant of replica exchange methods [8] to predict the binding free energy between
protein and ligands. Although we do not yet obtain conclusive results on two issues, (1) how precise
estimation can be obtained not depending on the initial condition and (2) how long simulations must be
done to obtain the reliable results, we will discuss the present preliminary results on the day.
[1] S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D.A. Case, An All Atom Force Field for Simulations ofProteins and Nucleic Acids. J. Comp. Chem. 7, 230-252 (1986).
[2] J.W. Ponder and D.A. Case. Force fields for protein simulations. Adv. Prot. Chem. 66, 27-85 (2003).
[3] Y. Deng and B. Roux, Computations of Standard Binding Free Energies with Molecular Dynamics
Simulations, J. Phys. Chem. B 113, 2234–2246 (2009).
[4 ] J. Wang, Y. Deng, and B. Roux, Absolute Binding Free Energy Calculations Using Molecular
Dynamics Simulations with Restraining Potentials, Biophys. J. 91, 2798-2814 (2006).
[5] H. J. Woo and B. Roux, Calculation of absolute protein–ligand binding free energy from computer
simulations, PNAS 102, 6825-6830 (2005).
[6] D. L. Mobley, J. D. Chodera, and K. A. Dill, Confine-and-Release Method: Obtaining Correct Binding
Free Energies in the Presence of Protein Conformational Change, J. Chem. Theory Comput. 3,
1231-1235 (2007).
[7] H. Fujitani, Y. Tanida, and A. Matsuura, Massively parallel computation of absolute binding free energy
with well-equilibrated states, Phys. Rev. E 79, 021914:1-12 (2009).
[8] Y. Sugita and Y. Okamoto, Replica-exchange molecular dynamics method for protein folding, Chem.
Phys. Lett. 314, 141 (1999).

P1-24
Flexible Fitting to Cryo-EM Density Map using Ensemble
Molecular Dynamics Simulations
Osamu Miyashita 1

Chigusa Kobayashi 1

osamu.miyashita@riken.jp ckobayashi@riken.jp

1

2

3
4

5

Takaharu Mori 2,3t.mori@riken.jp

Yuji Sugita 1,2,3,4

Florence Tama 1,5

sugita@riken.jp

florence.tama@riken.jp

RIKEN Advanced Institute for Computational Science, 6-7-1, Minatojima-minami-machi,
Chuo-ku, Kobe, Hyogo, 650-0047, JapanRIKEN Theoretical Molecular Science Laboratory, 2-1 Hirosawa, Wako-shi, Saitama 351-0198,
Japan
RIKEN iTHES, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, JapanRIKEN Quantitative Biology Center, 6-7-1, Minatojima-minami-machi, Chuo-ku, Kobe,
Hyogo, 650-0047, Japan
Department of Physics, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, Aichi 464-8602, Japan

Keywords: Cryo-EM single particle analysis, Flexible fitting, Replica exchange molecular
dynamics simulation
Cryo-EM single particle analysis provides structural information on biologically important
molecular complexes. However, resolution is still not sufficiently high, and thus, high-resolution
structures from other experiments are often used to create an atomic model from such
low-resolution structural information.
Flexible fitting is a computational algorithm to derive a new conformational model, from aknown structure, that conforms to low resolution experimental data to obtain conformationalmodels in new functional states. However, the conventional flexible fitting algorithms cannot
derive correct structures in some cases. In this study, we show the importance of examining
conformational ensemble in the refinement process by performing multiple fitting trials using
different force constants. Application to simulated maps of Ca 2+ ATPase and diphtheria toxin as
well as experimental data of release factor 2 revealed that for these systems, multiple
conformations with similar agreement with the density map exist and a large number of fitting
trials are necessary to generate good models. In addition, we show that an automatic adjustment of
the biasing force constants during the fitting process, implemented as replica exchange scheme,
can improve the success rate.

P2-01
Discovery of allosteric non-peptide ERK2 inhibitors
through in silico screening and competitive binding assay
Takayoshi Kinoshita 1
kinotk@b.s.osakafu-u.ac.jp

Yurika Mori 1
moriyurika14@b.s.osakafu-u.ac.jp

1

2

Hajime Sugiyama 2sugiyama.hajime@jp.fujitsu.com

Atsushi Tomonaga 2
atomonaga@jp.fujitsu.com

Graduate School of Science, Osaka Prefecture University, 1-1, Gakuen-cho, Naka-ku, Sakai,
Osaka 599-8531, Japan
Bio-IT R&D Office, Fujitsu Limited, 17-25, Shinkamata 1-Chome, Ota-ku, Tokyo 144-8588,
Japan

Keywords: ERK2, allosteric inhibitor, in silico screening, crystal structure, binding assay
Extracellular signal-regulated kinase 2 (ERK2) is a drug target for type 2 diabetes mellitus. A
peptide-type ERK2 inhibitor (PEP) was discovered in the previous study through theknowledge-based method and showed physiological effects on the db/db mice model of type II
diabetes [1]. The crystal structure at 2.98 Å resolution revealed that PEP bound to the allostericsite without the interruption of the ATP competitive inhibitor binding to ERK2. An in silico
biased-screening in regard to the allosteric site, using the focused library eliminating the ATPbinders such as FR180204 previously discovered [2], rendered three compounds with inhibitory
activity of IC 50 < 100M by ELISA-based enzymatic assay. Among them, two compounds
displayed the concentration-dependent efficacy by a competitive binding assay against PEP and
could be lead compounds for anti-diabetic medicine through the optimization process by
structure-based approaches.

[1] Kinoshita, T., Doi, K., Sugiyama, H., Kinoshita, S., Wada, M., Naruto, S., and Tomonaga, A.,
Knowledge-based identification of the ERK2/STAT3 signal pathway as a therapeutic target for
type 2 diabetes and drug discovery, Chem. Biol. Drug Des., 78: 471-476, 2011.
[2] Ohori, M., Kinoshita, T., Okubo, M., Sato, Yamaszaki, A., Arakawa, H., Nishimura, S.,
Inamura, T., Nakajima, H., Neya, M., K., Miyake, H., and Fujii, T., Identification of a selective
ERK2 inhibitor and structural determination of the inhibitor-ERK2 complex, Biochem. Biophys.
Res. Commun., 336: 357-363, 2005.
[3] Kinoshita, T., Sugiyama, H., Takahashi, N., and Tomonaga A., Identification of allosteric
ERK2 inhibitors through in silico biased screening and competitive binding assay, Bioorg. Med.
Chem. Lett., 26:955-958, 2016.

P2-02In Silico Prediction of Drug Inhibitory Activities to
Monoamine Transporters using ADMEWORKS (R)Kazuo Hirota 1

Takashi Yoshinaga 1

k2-hirota@hhc.eisai.co.jp

Kohei Sawada 1

Tetsushi Dodo 2Jose Ciloy 3

1

2

3

Global CV Assessment, Biopharmaceutical Assessment unit, Medicine Development Center,
Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba-shi Ibaraki, 300-2635, Japan
Planning and Coordination group, Biopharmaceutical Assessment unit, Medicine Development
Center, Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba-shi Ibaraki, 300-2635, Japan
Life Innovation, Healthcare Solution Division, Fujitsu Kyushu Systems Limited , Higashihie
Business CenterⅡ , 1-5-13 Higashihie, Hakata-ku, Fukuoka, 812-0007, Japan

Keywords: in silico prediction model, monoamine transporters, off-target
The monoamine transporters for norepinephrine (NET), dopamine (DAT) and serotonin (SERT)
are localized to presynaptic neuron and responsible for the reuptake of their associatedmonoamines. In drug discovery, the reduction of the inhibitory activities to monoaminetransporters is very important to avoid serious side effects related to the cardiovascular andcentral nervous systems. The in silico model with high accuracy for inhibitory activity of
monoamine transporters is considered to be a powerful tool for finding good drug candidates in
the lead optimization process.
We tried to build in silico models to predict the inhibitory activities to NET, DAT and SERT
using in-house data of about 3,000 compounds and a commercially available platform,
ADMEWORKS/ModelBuilder v7.0/Predictor v7.0 developed by Fujitsu Kyushu Systems Limited .
ADMEWORKS allows the building of predictive models based on empirical data using advanced
machine learning algorithms.
In this study, the usefulness of binary classification models (qualitative model) was examined.
Data for NET, DAT and SERT were randomly selected to create corresponding training sets andvalidation sets. About 20 to 78 descriptors out of 1010 descriptors generated by the software wereselected using a genetic algorithm and particle swarm optimization. Algorithms used for building thein silico models were AdaBoost (ADA), k-nearest neighbor (KNN), neural network (NN), support
vector machine (SVM) and discriminant function (TILSQ). Each algorithm was applied to the selected
descriptors to build several in silico models for comparison. Validation test results showed that the bestmodels with high accuracy and a good balance of sensitivity, specificity and number of descriptors forprediction of NET, DAT and SERT inhibition were as follows: sensitivity: 76.6, 84.6 and 80.0%,
specificity: 77.7, 87.6 and 87.0％, accuracy: 77.1, 86.1 and 83.5%, number of descriptors: 35, 63 and60, respectively. The algorithms used in the best models were ADA and SVM.
Based on the above results, we established good in silico models in order to predict the inhibitory
activities of monoamine transporters using ADMEWORKS.

P2-03

Interaction Analysis of FABP4 Inhibitors by X -ray
Crystallography and Fragment Molecular Orbital Analysis

1

Uno Tagami 1

Kazutoshi Takahashi 1

uno_tagami@ajinomoto.com

kazutoshi_takahashi@ajinomoto.com

Masayuki Sugiki 1

Toshimi Mizukoshi 1

masayuki_sugiki@ajinomoto.com

toshimi_mizukoshi@ajinomoto.com

Institute for Innovation, Ajinomoto Co., Inc., 1 -1 Suzuki-cho, Kawasaki 210-8681, Japan

Keywords: FABP, aP2, inhibitor, X-ray crystal structure, FMO
X-ray crystal structural determination of FABP4 in complex with four inhibitors revealed thecomplex binding modes, and the resulting observations led to improvement of the inhibitory
potency of FABP4 inhibitors. However, the detailed structure-activity relationship (SAR) could not
be explained from these structural observations. Fo r a more detailed understanding of theinteractions between FABP4 and inhibitors, fragment molecular orbital analyses were performed.
These analyses revealed that the total interfragmen t interaction energies of FABP4 and each
inhibitor correlated with the ranking of the Ki value for the four inhibitors. Furthermore,interactions between each inhibitor and amino acid residues in FABP4 were identified. The oxygen
atom of Lys58 in FABP4 was found to be very important for strong interactions with FABP4. These
results might provide useful information for the development of novel potent FABP4 inhibitors.

[1] Tagami, U.; Takahashi, K.; Igarashi, S.; Ejima, C.; Yoshida,T.; Takeshita, S.; Miyanaga, W.;
Sugiki, M.; Tokumasu, M.; Hatanaka, T.; Kashiwagi, T.; Ishikawa, K.; Miyano, H.; Mizukoshi, T.
ACS Med. Chem. Lett., 7:435–439, 2016.
[2] Furuhashi, M.; Hotamisligil, G. S. Nature reviews, drug discovery. 7:489–503, 2008.
[3] Miyanaga, W.; Sugiki, M.; Ejima, C.; Tokumasu, M.; Yoshida, T.; Takeshita, S. patent number
WO2014003158, 2014.
[4] Kitaura, K.; Ikeo, E.; Asada, T.; Nakano, T.; Uebayasi, M. Chem. Phys. Lett. 313:701–706,
1999.

P2-04
Exploring Structural Diversity and Ligand Binding
Property Consensus from Protein Structure Database
Akitoshi Okada 1 , Yoshirou Kimura 1 , Kinya Toda 1 , Ryoichi Kataoka 1 , Atsushi Kanou 1
okada@rsi.co.jp

1

Computational Science Department, Science & Technology Systems Division, Ryoka Systems Inc.,Tokyo Skytree East Tower, 1-1-2 Oshiage, Sumida-ku, Tokyo 131-0045, Japan

Keywords: Protein Structures, Pharmacophore elucidation, Ligand binding mode
There are more than 120,000 protein structures deposited in PDB a s of August, 2016. A number ofsimilar sequence data are deposited due to the fact that the structures will form different
conformations according to their states (e.g., active, inactive, ligand-bound, etc.).
Extracting ‘information’ such as structural diversity and ligand binding property accordingly tothese states can be somewhat time-consuming. As a starting point, one will need to search therelevant data from the PDB database and verify if it is the optimal dataset needed. One may alsoneed to perform a necessary preparation, such as sequence alignment and structure superposition
before even looking at the dataset to extract the information. Then finally comes to look at eachdata and extract the necessary information. In any steps, human errors or biases can cause
unreproducible results. It may also be desirable to go through the in -house data as well as the PDB
database in extracting the information.
We therefore made two programs in order to reduce the necessity of going through the steps andgoing directly to the information from the query protein sequence or structure. Programs werecoded by Scientific Vector Language of MOE 1 and also made use of PSILO 2 , which can manage
both the in-house data and the public PDB data. These programs will search, pre-calculate, and 1)
show the structural diversity or 2) output the consensus ligand binding property that can beelucidated (namely, PSILO diverse search and PSILO pharmacophore consensus respectively) fromthe whole protein database.
We will demonstrate these two programs using a kinase sequence as an example. Given just theamino acid sequence as a query, PSILO diverse search clusters the structures automatically with
exploring the structural diversity of the similar kinases (such as active and inactive state
conformations, Fig. 1a). Then, PSILO pharmacophore consensus can be used for, but not limited to,each representative kinase and quickly elucidate the conserved ligand binding mode (Fig. 1b).
a)b)

Figure 1. Structural information ofc-Abl tyrosine kinase derived by
PSILO diverse search and PSILO
phamacophore consensusa) Structural diversity of c-Abl
kinase (diverse region circled
in black)
PDB ID: 1IEP, 1M52b) Consensus
ligandbinding
property of active states c-abl
kinase. The spheres represent
theconserved
binding
propertiesPDB ID: 1IEP

[1] Molecular Operating Environment (MOE), 2015.10; Chemical Computing Group Inc., 1010
Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2016.
[2] Protein SILO (PSILO), 2015.08; Chemical Computing Group Inc., 1010 Sherbrooke St. West,
Suite #910, Montreal, QC, Canada, H3A 2R7, 2016.

P2-05
Reactivity evaluation of donepezil in the oxidation
by CYP3A4 based on QM calculation
Atsuko Sato 1,2
atsuko.sato@kyowa-kirin.co.jp

Hitomi Yuki 3
hitomi.yuki@riken.jp

Chiduru Watanabe 3
chiduru.watanabe@riken.jp

Jun-ichi Saito 4
jun.saito@kyowa-kirin.co.jp

Akihiko Konagaya 2

Teruki Honma 2,3

kona@c.titech.ac.jp

honma.teruki@riken.jp

1

Kyowa Hakko Kirin Co .,Ltd. , Chemical Research Laboratories, Research functions Unit, R &D
Division, 1188, Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan
2
Tokyo Institute of Technology, School of Computing, Department of Computer Science, 4259
Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan
3
RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa, 230-0045, Japan
4
Kyowa Hakko Kirin Co. ,Ltd. , R&D Planning Department, R&D Division, 1-9-2 Otemachi,
Chiyoda-ku, Tokyo, 100-0004, Japan

Keywords: Cytochrome P450 (CYP), donepezil, reactivity, radical intermediate, sites ofmetabolism (SOM), density functional theory (DFT), potential energy
CYP3A4 contributes to more than 50% of clinical drug metabolism. Therefore, optimization of drugcandidates considering the CYP3A4 metabolism is critically important. Our purpose of this research is
to establish methods for metabolically stable drug design. Generally, accessibility of each carbon atom
to heme iron and oxidative reactivity of the atom in the substrate are important to predict the site of
metabolism. In our previous study, we have investigated the mechanism of oxidation on carbamazepine(CBZ) using MM and QM calculation [1].
In this presentation, we focused the reactivity prediction on donepezil. We have evaluated thereactivity of the compound by the potential energy concerned with the radical intermediate using
quantum mechanics by DFT calculations. We have compared a fast simple method such as SMARTCyp[2] with our QM calculations using the compound conformation in the CYP3A4 complex. In thisevaluation process, we will discuss the protonated state of donepezil in the complex of CYP3A4.

[1] Hata, M.; Tanaka, Y.; Kyoda, N.; Osakabe, T.; Yuki, H.; Ishii, I.; Kitada, M.; Neya, S.; Hoshino, T.
Bioorg. Med. Chem. 2008, 16, 5134–5148.
[2] Liu, R.; Liu, J.; Tawa, G.; Wallqvist, A. J. Chem. Inf. Model. 2012, 52, 1698–1712.

P2-06
Molecular dynamic approach to identify significant PTP1B
inhibitors to combat diabetes
Shabana Bibi
dazzling.grin@gmail.com

Katsumi Sakata
ksakata@maebashi-it.ac.jp

Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-machi,
Maebashi, Gunma 371-0816, Japan
Keywords: Diabetes mellitus; virtual screening, PTP1B inhibitors; ZINC database; Pharmacophore modelling

Abstract: Diabetes is a metabolic disorder of the pancreas, in which the pancreas loses its functionality to
produce insulin hormone properly in the body. Protein tyrosine phosphatase 1B (PTP1B) interrupts
insulin signaling by dephosphorylating both the insulin receptor and insulin receptor substrate. PTP1B is
considered to be a negative regulator of insulin receptor signaling. PTP1B is a promising drug target for
the treatment of type 2 diabetes, obesity, as well as cancer. In this research virtual screening was carriedout on drug like compounds of Zinc database using several criteria. The 3-D coordinates in the X-ray
crystal structure of PTP1B enzyme complex (PDB code: 3EAX) was selected as the initial model for
structure based virtual screening to find active drug like PTP-1B compounds. The PTP-1B focused library(1625 compounds) was downloaded and filtered by Lipinski rule of five and OSIRIS server for ADME
properties. 70 selected compounds were docked against the PTP-1B by CLC and MOE to identify lowenergy binding modes within the active site of a macromolecule. Interactions like hydrogen bonds, ionicinteraction, hydrophobic interactions and metallic bonds were analyzed. Two different docking
algorithms were used to validate the docking results. For the characterization of novel and potent
inhibitors for PTP1B, we explored selective features of our docked complex shown in Figure 1 bystructure based pharmacophore modeling and finally select the features of two top scored models andsubjected as pharmacophore query for screening of Zinc database of 4 million compounds. Significant
hits were identified by pharmacophore based virtual screening of Zinc database. Hits with similar binding
poses and top scores were selected for subsequent refinement and optimization by molecular dynamic
approach. Structural features relevant to the interactions of the newly identified inhibitors with the activesite residues of PTP1B are discussed in detail. This study might be helpful in designing active lead forclinical studies that may serve as antidiabetic drugs in future.

Figure 1: Schematic representation of the top scored PTP1B hits (A) ZINC13674403 and (B)ZINC40431189 in the binding Pocket (PDB ID: 3EAX) exhibits ideal binding mode and interactions to
key residues.

P2-07
Bridging the dimensions: Seamless integration of
3D structure-based design and 2D structure-activity
relationships to guide medicinal chemistry
Marina Takahashi 3

Sumie Tajima 3

m_takahashi@hulinks.co.jp

Marcus Gastreich 2
marcus.gastreich@biosolveit.com

Peter Hunt 1

tajima@hulinks.co.jp

Edmund Champness 1
ed@optibrium.com

Tamsin Mansley 1

peter@optibrium.com tamsin@optibrium.com
1
2
3

James Chisholm 1
james@optibrium.com

Carsten Detering 2

carsten.detering@biosoveit.com

Christian Lemmen 2

Matt Segall 1

christian.lemmen@biosolveit.com matt@optibrium.com

Optibrium Ltd., 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL, UK
BioSolveIT GmbH, An der Ziegelei 79, 53757 Sankt Augustin, Germany
Hulinks Inc., 5-14 Nihonbashi Hakozaki-cho, Chuo-ku, Tokyo 103-0015, Japan

Keywords: quantitative structure activity relationships, structure-based design, visualization
The effective use of software can have a major impact on timelines and innovation in dru g discovery. However, the traditional split between computational model lers and synthetic chemists has
been blurred and software must be accessible across disciplines to quickly understand and predict
structure-activity relationships (SAR). There has been a similar divide between tools forthree-dimensional (3D) structure-based design and those for analysis of SAR based on a
two-dimensional (2D) compound structure. Seamless integration of these approaches enables all of
the structural knowledge to be used to guide the efficient design of high quality, active compounds.
In this poster, we will illustrate how information from 2D models of key physicochemical andabsorption, distribution, metabolism, elimination and toxicity (ADMET) properties can be linked
with 3D views of protein-ligand complexes. The influence of each atom or functional group onthese properties can be highlighted [1] and combined with visualization of the key interactions
contributing to binding affinity [2], enabling development of optimization strategies that balance
potency with the ADMET properties required in a safe and efficacious drug [3].
Furthermore, 2D analyses, such as activity cliff detection and matched molecular pair analyses,
are commonly used to explore compound data sets and quickly identify important SAR within achemical series or library [4]. We will demonstrate how a seamless, highly visual link between the
results of these analyses and related 3D structural information helps to understand and rationalize
this SAR. This enables the efficient design of compounds with improved target affinity in a truly
multi-parameter optimization environment.
[1] Segall M.D., Champness E., Obrezanova O., Leeding C., Beyond Profiling: Using ADMETmodels to guide decisions, Chemistry & Biodiversity 6: 2144-2151, 2009[2] Reulecke, I. et al., Towards an Integrated Description of Hydrogen Bonding and Dehydration:
Decreasing False Positives in Virtual Screening with the HYDE Scoring Function , ChemMedChem
3:885-897, 2008[3] Segall, M.D., Multi-Parameter Optimization: Identifying high quality compounds with a ba lance of properties, Curr. Pharm. Des. 18: 1292-1310, 2012[4] Segall M.D., et al., Breaking free from chemical spreadsheets, Drug Dicov. Today, 20:1093-1103, 2015

P2-08Predicting inhibitory and activatory effects of drugcandidate compounds from chemically-induced
transcriptome data by a multitask learning with gene
perturbation similarity
Ryusuke Sawada 1
rsawada@bioreg.kyushu-u.ac.jp

Yasuo Tabei 2
tabei.y.aa@m.titech.ac.jp123Michio Iwata 1
m-iwata@bioreg.kyushu-u.ac.jp

Yoshihiro Yamanishi 1,2,3
yamanishi@bioreg.kyushu-u.ac.jp

Division of System Cohort, Medical Institute of Bioreg-ulation, Kyushu University, 3-1-1
Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
Institute for Advanced Study, Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka,
Fukuoka 812-8581, Japan

Keywords: multitask learning, drug target, transcriptome, gene knock-down, gene over-expression,
chemical treatment
Genome-wide identification of inhibitory and activatory effects of drug candidate
compounds is an important issue in drug discovery. It is also necessary to appropriately evaluate
the compound activities not only on a single target protein but also on other proteins (off-targets).
Deciphering inhibitor and activator actions derived from the interactions with the primary target
and off-targets can help to elucidate potential therapeutic effects and side effects of the compounds.
In recent years, a variety of computational methods have been proposed for drug target predictionin the framework of chemogenomic approach [1] and phenotypic approach [2], but they cannot
distinguish the difference between inhibitors and activators, and the performance is poor when thenumber of known ligands for a target protein of interest decreases. Genome-wide expression
profiling of transcriptional responses to chemical and genetic perturbations on human cell lines is
a promising unbiased approach for exploring the mode-of-action of bioactive compounds.
In this study, we propose a novel method to simultaneously predict inhibitory effects andactivatory effects of drug candidate compounds on multiple target proteins fromchemically-induced transcriptome data by a multitask learning with gene knock-down similarityand gene over-expression similarity. Transcriptome data were obtained from the Library ofIntegrated Cellular Signatures (LINCS: http://www.lincsproject.org/). For chemically-induced data,
we analyzed transcriptomic changes in global patterns of gene expression profiles in response to
the chemical treatments of 20,122 compounds (including approved drugs) on 71 human cell lines.
For genetically-perturbed data, we analyzed transcriptomic changes in global patterns of gene
expression profiles in response to the knock-down of 4,331 human genes and the over-expressionof 2,946 human genes. The proposed method can predict inhibitory effects and activatory effects
separately for drug candidate compounds from the integration of chemically-induced andgenetically-perturbed transcriptome data. We demonstrated the usefulness of the method in termsof high accuracy and high coverage, and show advantages over the previous methods. The
proposed method is expected to be useful for identification of potential drug actions that theprevious methods failed to detect in various pharmaceutical applications.
[1] Yamanishi, Y., et al., Prediction of drug-target interaction networks from the integration ofchemical and genomic spaces. Bioinformatics, 24, i232–i240, 2008.
[2] Campillos, M., et al., Drug target identification using side-effect similarity. Science, 321,
263–266, 2008.

P2-09Apo- and antagonist-binding structuresof vitamin D receptor ligand-binding domain in solution
studied by MD and SAXS hybrid approach
Yasuaki Anami 1

Nobutaka Shimizu 2
nobutaka.shimizu@kek.jp

Toshimasa Itoh 1

Toru Ekimoto 3

ekimoto@tsurumi.yokohama-cu.ac.jp

Mitsunori Ikeguchi 3

ike@tsurumi.yokohama-cu.ac.jp123

Daichi Egawa 1

Keiko Yamamoto 1
yamamoto@ac.shoyaku.ac.jp

Laboratory of Drug Design and Medicinal Chemistry, Showa Pharmaceutical Un iversity,
3-3165 Higashi-Tamagawagakuen, Machida, Tokyo 194-8543, Japan
Photon Factory, Institute of Materials Structure Science, High Energy Accelerator Research
Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801, Japan
Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan

Keywords: Vitamin D receptor, Molecular dynamics simulation, Small -angle X-ray scattering
Vitamin D receptor (VDR) is a ligand-dependent transcriptional factor regulating the expressionof genes related to calcium homeostasis, immunomodulation, and cell differentiation andproliferation. Since functions of VDR and other nuclear hormone receptors (NRs) are involved inhuman diseases, their regulation by ligand binding is a typical strategy for pharmaceutical drug
design. According to X-ray crystal structures of NRs and other experimental results, theligand-binding domain (LBD) undergoes conformational change upon ligand binding, and a local
conformational change around helix 12 is key to regulating agonism/antagonism.
The mechanism of agonism/antagonism of VDR is not understood clearly on the basis of X-ray
crystallography. All the crystal structures of VDR-LBD are almost identical, regardless ofagonist/antagonist binding. Since the conformation of helix 12 is key, the crystal structures of the
VDR-LDB/antagonist complex do not provide structural insight into the mechanism of antagonism.
In addition, no crystal structures of apo VDR-LBD has been reported, and the exact conformationof the apo form at atomic resolution remains unknown.
In order to reveal the apo and antagonist binding forms of VDR -LBD, we analyze them by ahybrid approach of small-angle X-ray scattering (SAXS) and molecular dynamics simulation (M D).
SAXS analysis can reveal an overall shape of molecular structures in solution, and can capture
both flexible structure in the apo form and the conformational change in response to antagonist
binding. We obtained SAXS profiles of apo and antagonist bind ing rat VDR-LBDs, however, theprofiles were different from the theoretical profiles calculated from crystal structures. To clarify
the solution structures at atomic resolution, we conducted MD and collected each structuralensemble. Comparing the SAXS profiles and the MD results, we report a reliable structure for apo
and antagonist binding forms of VDR-LBD. In apo form, helix 12 is partially unraveled andfluctuates around the canonical position. In addition, helix 11 bends outward by a kink -centeredhinge-bending motion, resulting in opening of the entrance into the ligand -binding pocket. In
antagonist-binding form, the position of helix 12 is unsuitable for creating canonical active
position, thus preventing co-activator binding for transactivation. On t he basis of these results, we
propose a mechanism for the agonism/antagonism of VDR by ligand binding that is an alte rnative
to the mouse-trap model.
[1] Anami, Y., Shimizu, N., Ekimoto, T., Egawa, D., Itoh, T., Ikeguchi, M., Yamamoto, K., Journalof Medicinal Chemistry, 2016 in press.

P2-10Efficient Similarity Search Using Multiple Reference
Molecules on PG-Strom architecture

1

2

Kohei Kaigai 1

Atsushi Yoshimori 2

kaigai@ak.jp.nec.com

yoshimori@itmol.com

NEC Business Creation Division, NEC Corporation, 1753, Shimonumabe, Nakahara-ku,
Kawasaki, 211-8666, Japan
Institute for Theoretical Medicine, Inc., 4259-3 Nagatsuta-cho, Midori-ku, Yokohama
226-8510, Japan

Keywords: k-Nearest Neighbors (k-NN), GPU acceleration, Query acceleration engine
PG-Strom is an extension of PostgreSQL, designed to off -load several CPU intensive query
workloads to GPGPU automatically or manually [1, 2]. It works as a query acceleration engine,
and provides cost-effective solution for processing massive amount of records. Its characteristics
well fits usual workloads of data manipulation in chemoinformatics.
Similarity searches using multiple reference molecules are one of data fusion methods, which arewidely used in virtual screening. Various methods have been introduced to utilize multiplereference molecules, such as centroid fingerprints [3], k-nearest neighbors (k-NN) [3], andSVM-based ranking [4]. Data fusion methods generally improve virtual screening performance.
In this study, we attempted to implement
the k-NN based similarity search system
on PG-Strom architecture. It allows
running the heaviest portion of thesimilarity search onto GPGPU which has
more than thousands cores, then we could
observesignificantly
accelerated
similarity search results.
On the other hands, the entire workload is
processed inside of the PostgreSQL
database, thus, we can utilize flexibilityof SQL for both of pre-process andpost-process of the similarity search.
We will discuss about relationshipbetween the SQL queries, adoption of
GPGPU computing power inside of
database management system, and its
applications to chemoinformatics.
[1] Kaigai K., PG-Strom-GPGPU meets PostgreSQL-, PGconf 2015.
[2] https://github.com/pg-strom/devel[3] Schuffenhauer A., Floersheim P., Acklin P., Jacoby E., Similarity metrics for ligands
reflecting the similarity of the target protein, Journal of Chemical Information andComputer Sciences , 43:391–405, 2003.
[4] Geppert
H.,
HorváthT.,
GärtnerT.,
WrobelS.,
Bajorath
J.,
Support-vector-machine-based ranking significantly improves the effectiveness ofsimilarity searching using 2D fingerprints and multiple re ference compounds, Journalof Chemical Information and Modeling , 48:742-746 2008.

P2-11
Fragment-based Drug Design based on SZMAP Water
Mapping in Protein Active Site
Hideyuki Sato 1

hsato@eyesopen.com

Gregory Warren 2

greg@eyesopen.com

Gunther Stahl 3

gunther.stahl@eyesopen.com

1
2
3

OpenEye Japan Co.,Ltd., Sankaido Building 8F, 1-9-13 Akasaka, Minato-ku, Tokyo, 107-0052, Japan
OpenEye Scientific Software Inc., 9 Bisbee Ct., Santa Fe, NM 87503, USA
OpenEye Scientific Software Limited, Josephstr. 29, 50678 Cologne, Germany

Keywords: Fragment-based Ligand Design, Water Mapping, Protein Structure
Fragment-based drug design (FBDD) is widely used in modern drug discovery for designing
novel ligands. Although it requires fragment mapping in an active site of a target protein
structure, it is sometimes difficult to find appropriate fragments and their stable locations in theproteins from X-rays analyses. Therefore, alternative approaches to find appropriate fragments
are required in order to apply this useful method against a lot of drug discovery targets.
SZMAP predicts the role of water molecules in molecular interactions around an active site of aprotein using semi-continuum approach[1,2].
This approach combines one explicit watermolecule with robust continuum solvent methods[3]. SZMAP uses classical statistical mechanics
and thorough sampling of water molecules to analyze specific water orientations. GAMEPLAN,
which is an application of SZMAP, analyzes environment on each atom of a bound ligand in a
protein and suggests ways to modify the bound ligand per one or two atom(s) using water structurein the immediate environment of the ligand. This method helps to find reasonable directions byadding polar or non-polar fragments in dependence upon a protein active site environment. One
complication with the fragments generated by GAMEPLAN is chemical synthesizability. This
problem is solved by using our fragment replacement tool BROOD which searches a large
fragment database generated from ChEMBL20 using shape and color similarity. Since BROOD
does not require a readily synthesizable chemical structure for the query when searching thedatabase, fragments suggested by GAMEPLAN can be easily applied as a BROOD query. The
result is novel synthesizable fragment-sized R-groups that are added to the template of interest
with chemistry suggested from solvent mapping.
In this poster, we will discuss how we can design novel potent ligands from a smaller compoundby combining SZMAP/GAMEPLAN water mapping and BROOD fragment replacement
approaches.
[1] Calculation of the thermodynamic properties of aqueous electrolytes to 1000.degree.C and5000 bar from a semicontinuum model for ion hydration Tanger, J.C., Pitzer, K.S., J. Phys.
Chem., 93:4941-4951, 1989.
[2] Continuum based calculations of hydration entropies and the hydrophobic effect Rashin, A.A.,
Bukatin, M.A., J. Phys. Chem., 95:2942-2944, 1991.
[3] A Smooth Permittivity Function for Poisson-Boltzmann Solvation Methods Grant, J.A.,
Pickup, B., Nicholls, A., J. Comp. Chem., 22:608-640, 2001.

P2-12
Application of deep learning for large scale data withstandard performance workstation(s)Tetsuro KITAJIMA 1

Kei TANEISHI 2

Hiroaki IWATA 3

Hitoshi KAMIJIMA 1

kitajima@isp.co.jp

kei.taneishi@riken.jp

iwata@fbri.org

kamijima@isp.co.jp

Shinji YAMAMOTO 1
ymt@isp.co.jp

1

2

3

4

Kazunori INARI 1
inari@isp.co.jp

Yasushi OKUNO 4

okuno.yasushi.4c@kyoto-u.ac.jp

Research Institute of Systems Planning Inc., Kasuya Bldg. 2 -9 Sakuragaoka, Shibuya, Tokyo
150-0031, Japan
Advanced Institute for Computational Science, RIKEN, 7 -1-28, Minatojima-minami-machi,
Chuo-ku, Kobe, Hyogo 650-0047 JapanFoundation for Biomedical Research and Innovation, 1 -6-5, Minatojima-minami-machi,
Chuo-ku, Kobe, Hyogo 650-0047 Japan
Department of Clinical System Onco -Informatics, Kyoto University Graduate School of
Medicine, Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan

Keywords: Deep Learning, Big data, cheminformatics, in-silico drug discovery
Deep Learning (DL) [1], a widely known method of machine learning, is used in various fields,such as drug development, image recognition and autonomous driving technology. DL is suitablefor dealing with large scale data; therefore we have been studying application of DL for in-silico
drug discovery and large scale biochemical assay screening.
There are powerful libraries/frameworks for DL, for example, Caffe [2], Chainer [3] andTensorFlow [4]. When applying DL to large scale (i.e. larger than the memory size) data, it ispossible to use LMDB key-value storage with Caffe. However, Chainer and TensorFlow have nosuch database storage support, making them unsuitable for DL with large scale data.
In this presentation we will introduce our new framework that allows us to carry out DL with large
scale data using Chainer. Our framework requires a workstation with GPU for training andapplying the DL model. If the storage provided by a single workstation is not enough, multiple
workstations interconnected by wide-bandwidth network such as 1Gbit Ethernet can be used. We
have used our framework for applying DL to PCBA bioassay data which has 3072 columns and1.1M rows (prepared by Dr. Okuno and his team). It took 7.3x10^2 seconds to train 1 epoch using asingle workstation equipped GeForce GTX 980 Ti GPU and 6.7x10^2 seconds with the same GPU
workstation plus 3 storage workstations.
PaaS providers (Amazon, Microsoft, Google…) are likely to support powerful GPU instances in
the near future. Our framework has good compatibility for PaaS infrastructure and will be able to
make use of them allowing us to carry out high performance DL .
This study is supported by Fujitsu Limited.
[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015):
436-444.
[2] Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick,Sergio Guadarrama, and Trevor Darrell, Caffe: Convolutional Architecture for Fast Feature Embedding,arXiv: 1408.5093, 2014.
[3] Tokui Seiya., Kenta Oono, Shohei Hido, and Justin Clayton, Chainer: a next-generation open source
framework for deep learning, Workshop on Machine Learning Systems at Neural Information
Processing Systems (NIPS), 2015.
[4] Martín Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
Systems, arXiv: 1603.04467, 2016.

P2-13
Hydrogen bond basicity prediction for common fragments
in high-activity compounds
Yoshio Okiyama 1

yoshio.okiyama@riken.jp

Daisuke Takaya 1

daisuke.takaya@riken.jp

Shunpei Nagase 1

shunpei.nagase@riken.jp

1

Kikuko Kamisaka 1

kikuko.kamisaka@riken.jp

Teruki Honma 1

honma.teruki@riken.jp

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

Keywords: Hydrogen bond acceptor, Lone pair, Molecular electrostatic potential, QM, ADMETHydrogen bond (HB) is strongly contributing to binding affinity of a drug molecule with target
proteins or solubility in water and prediction of the strength in advance is very useful for drug
design process. HB acidity and basicity, which are defined as the capabilities to make an HB for its
donor and acceptor, respectively, play key roles in determination of hydrogen bonding strength
between them.
Recently, Kenny et al. proposed a nonlinear regression model to predict HB basicity by using
molecular electrostatic potential (MEP) minima near each lone pair as a descriptor [1]; for each
HB acceptor type, MEPs of small molecules only with equivalent HB acceptors were quantum
mechanically (QM) calculated and fitted to measured HB basicity values to determine theparameters of a regression equation, which are used to predict HB basicity for molecules withmultiple, nonequivalent HB acceptors, whose basicity values cannot be measured individually.
In accordance with this methodology, we newly developed an automated protocol to evaluate HB
basicity for multiple compounds and applied to a dataset where consists of frequently appearing
fragments common to highly potent compounds registered in the ChEMBL database (see the figure
below). Because the frequently used fragments are likely to be pharmacologically meaningful inrational drug design, predicted HB basicity data would provide useful information to enhance
activity or improve physicochemical and ADMET properties from the bioisosterism viewpoint for
medicinal chemists.

[1] Kenny, P. W., Montanari, C. A., Prokopczyk, I. M., J. Ribeiro, F. R., Sartori, G. R., Hydrogen
Bond Basicity Prediction for Medicinal Chemistry Design, Journal of Medicinal Chemistry,
59:4278-4288, 2016.

P2-14
Human insight in drug discovery: docking pose selection inprotein-ligand docking and hit-compounds selection in
structure-based virtual screening
Kazuki Yamamoto1

Yoshitaka Moriwaki2

Keita Oda3

kayamamoto-tky@umin.net

virgospica93@gmail.com

ainsophyao@gmail.com

Masahito Ohue4

ohue@c.titech.ac.jp

Itsuo Nakane5

ituwo.nakane@gmail.com

Ryunosuke Yoshino4, 6

yoshino.r.aa@m.titech.ac.jp

Hayase Hakariya7

Tadaaki Mashimo8,9Mitsuhito Wada8, 10

hakariya.hayase.46n@st.kyoto-u.ac.jp

mashimo.tadaaki@imsbio.co.jp

m-wada@aist.go.jp

Fukunishi Yoshifumi8, 11
y-fukunishi@aist.go.jp

Isotope Science Center, The University of Tokyo, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
Research Center of Integrative Molecular Systems, Institute for Molecular Science, National Institutes of
Natural Sciences, 38 Nishigo-Naka, Myodaiji, Okazaki City, Aichi 444-8585, Japan
3
Stray
4
School of Computing, Tokyo Institute of Technology, W8-76, 2-12-1, Ookayama, Meguro-ku, Tokyo 1528550, Japan
5
Okazaki City Hall Environmental Preservation Division, 15-1, Dezaki, Rokku-cho, Okazaki City, Aichi
444-0072, Japan
6
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku,
Tokyo 113-8657, Japan
7
Kyoto University Graduate School of Medicine, Institute for Integrated Cell-Material Sciences(iCeMS)/Institute for Chemical Research (ICR), Training Program of Leaders for Integrated Medical
System (LIMS), Yoshida Ushinomiya-cho, Sakyo-ku, Kyoto City, Kyoto 606-8501, Japan
8
Technology Research Association for Next-Generation Natural Products Chemistry, 2-3-26, Aomi, Kotoku, Tokyo 135-0064, Japan
9
IMSBIO Co., Ltd., Owl tower 6F, 4-21-1, Higashi-ikebukuro, Toshima-ku, Tokyo 170-0013, Japan
10
Biochemical Information Project, Fujitsu Limited, Japan
11
Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced
Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
1
2

Keywords: Drug discovery, Structure-based virtual screening, Visual inspection, Human computation,
Crowd sourcing
Implicit knowledge still remains important in drug discovery process, while physicochemical simulationand machine learning methods are rigorously utilized in computational drug design. Such drug discoveryrelated knowledge includes chemical alert structure, synthetic accessibility, feasibility of ligand binding
pose, etc. To take advantage of human knowledge, visual inspection of chemical structure and docking poseis commonly performed by researchers. However, the criteria of visual inspection-based filtering have notbeen systematized and the effectiveness of such heuristic filtering is unclear. Here, we examined the validityof visual inspection-based filtering, such as docking pose selection in protein-ligand docking and hitcompounds selection in structure-based virtual screening. Visual judgment by examinees was collected via
vote system. Our human-assisted screening workflow was tested by participating in the IPAB 3rd ComputerAided Drug Discovery Contest 1. Based on the analysis of the vote result, we will discuss the feasibility ofhuman-computer synergy in drug discovery.
(The author also presents this subject in the IPAB Focused Session FS6-1.)[1] IPAB 3rd Computer-Aided Drug Discovery Contest (2016) www.ipab.org/eventschedule/contest/contest3

P2-15Structure-Based Rational Design of Novel Oral Bioactive
LXR Modulators
Tomohiro Shimada 1

Shinji Kakuda 1

Jun Hirata 1

Emi Kada 1

t.shimada@teijin.co.jp

s.kakuda@teijin.co.jp

j.hirata@teijin.co.jp

e.kada@teijin.co.jp

1

Teijin Institute for Bio-medical Research, TEIJIN PHARMA LIMITED, 4-3-2 Asahigaoka,
Hino, Tokyo 191-8512, Japan

Keywords: LXR, nuclear receptor, corepressor selectivity, rational design, SBDD, LBDD
To achieve good balance between activities and drug-like properties is difficult in medicinal
chemistry. Especially, like nuclear receptors, the targets which consist of hydrophobic binding sitetend to prefer poor developable high lipophilic compounds.
Liver X receptors (LXRs), which have been of interest as targets for the treatment of
atherosclerosis, inflammation, Alzheimer’s disease, dermatological conditions, hepatic steatosis
and cancer, are no exception to this rule. Although over a decade of efforts, no LXR m odulator has
successfully progressed beyond clinical trials [1][2] .
Compared with LXR agonists, such as T0901317 and GW3965, GSK9772 shows potent
anti-inflammatory activity without inducing lipogenic side -effects in vitro through stabilizing
repressed state of LXR. But no additional work on this series including in vivo efficacy has beenreported. It could be due to its low bioavailability derived from high lipophilicity (AlogP = 7.3) [3] .
In this study, we have discovered novel oral bioactive LXR modulators which stabilize repressedstate. Structure-based rational drug design made it possible not only to find novel oral bioavailable
structure while maintaining desired activities and cofactor profi le, but to reduce number ofcompounds being synthesized and project cycle time. Our SBDD approaches and compound
profiles including in vivo activities will be discussed.

[1] Reviews: J. Med. Chem.,57:7182−7205, 2014.; Expert Opin. Ther. Patents,23:1317-3, 2013.;
Expert Opin. Ther. Patents,20:535-562, 2010.
[2] FASEB J., 30:2570-9, 2016.; ACS Med. Chem. Lett., 6:902−907, 2015.; Bioorg. Med. Chem.
Lett., 25, 372–377, 2015.; Molecular Metabolism, 4:353e357, 2015.
[3] J. Med. Chem., 51:5758–5765, 2008.

P2-16Prediction of sites of metabolism of compounds for
CYP1A2 by combined molecular simulations
hiroaki.saito@riken.jp,

Hiroaki Saito 1 ,mizukami@jaist.ac.jp,

Taku Mizukami 2 ,Yoshinori Hirano 1 ,Takao Otsuka 1 ,Noriaki Okimoto 1 ,Makoto Taiji 1

totsuka.riken.jp,

12

okimoto@riken.jp,

hirano@riken.jp,

taiji@riken.jp

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 heme-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 to predict the SOMs of compounds
metabolized by CYP1A2. First, a long time molecular dynamics (MD) simulation of the solvated
CYP1A2 system was carried out to observe the inherent dynamical multiple receptor (pocket)
structures in physiological conditions, and then those sampled receptor structures were used for themolecular docking using multiple receptor structures (ensemble docking). Next, from theinformation on the docked compound poses obtained from ensemble docking, we evaluated thepossibility on access of the compound to the active site of CYP1A2 at atomic level (accessibility).
In the molecular docking, docking program GOLD with ChemScore parameters, which are
optimized for CYP system [3], was used.
We prepared 19 test set compounds metabolized by the CYP1A2 and 10,000 receptor snapshots(which correspond to the one micro second MD simulation) for the ensemble docking simulation.
The top three ranked atoms by the accessibility analysis are defined as possible SOMs for eachcompound in this study, and we found the success rate of the predicted SOMs was 78.9 %. For
more accurate SOMs prediction, we then evaluated a new score function utilizing the accessibility
score by the ensemble docking and the reactivity score obtained from the SMARTCyp software [4].
The success rate of the predicted SOMs using the new score function was 89.5%, indicating thatthe use of the new score function improves about 10 % as compared with the SOMs prediction
from the ensemble docking alone.
[1] Wienkers LC, heath TG., Nature Rev. Drug. Discov. 4 (2005), 825-833.
[2] Johannes Kirchmair et al., J. Chem. Inf. Model. 52 (2012), 617−648.
[3] PROTEINS: Structure, Function, and Bioinformatics 58 (2005), 836–844.
[4] P. Rydberg et al., ACS Med. Chem. Lett., 1 (2010), 96-100

P2-17Prediction of Tankyrase2-ligand Binding Affinities Using
Molecular

Mechanics

andQuantum

Mechanics

Calculations
Noriaki Okimoto 1
okimoto@riken.jp

Takao Otsuka 1

Yoshinori Hirano 1

totsuka@riken.jp

hirano@riken.jp

Makoto Taiji 1
taiji@riken.jp

1

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

Keywords: affinity prediction, molecular docking, molecular mechanics calculation, quantum
chemical calculation
Accurate computation of the protein-ligand binding affinity is of significant importance for lead
optimization in drug development research. Various computational methods have been proposed to
predict the protein-ligand binding affinity. There are relatively accurate free energy calculation
techniques based on molecular dynamics (MD) simulations. These include linear interactionenergy methods, the molecular mechanics/Poisson-Boltzmann and surface area (MM-PB/SA)
method, alchemical free energy calculations, and so on. However, these methods employing MD
simulations are limited by the use of classical molecular mechanics (MM) force fields, because thepreparation of appropriate parameters for organic compounds is difficult.
Protein-ligand systems exhibit various non-bonded interactions, such as π-stacking, chargetransfer, polarization effects, and dispersion, as well as the usual and weak hydrogen bonds, asevidenced by structural studies 1,2 . These non-bonded interactions cannot be understood with
sufficient accuracy by using classical MM force fields. On the other hand, application of quantum
mechanical (QM) calculations considering such non-bonded interactions can significantly improve
the accuracy of the MM force fields.
In this study, we evaluated prediction of binding affinities for tankyrase2-ligand systems by using
combined approaches of MM and QM calculations. As our approach for reducing computational
cost of QM calculation, ONIOM method and multilayer fragment molecular orbital method 3,4 wereused. We will report the detailed results.
References
1.

Raha, K.; Peters, M. B.; Wang, B.; Yu, N.; Wollacott, A. M.; Westerhoff, L. M.; Merz, K.

M., Jr. Drug Discov Today 2007, 12(17-18), 725-731.
2.

van der Vaart, A.; Merz, K. M. J Am Chem Soc 1999, 121(39), 9182-9190.

3.

Otsuka, T.; Okimoto, N.; Taiji, M. J Comput Chem 2015, 36(30), 2209-2218.

4.

Fedorov, D. G.; Ishida, T.; Kitaura, K. J Phys Chem A 2005, 109(11), 2638-2646.

P2-18
Towards Desktop laboratory from MD, docking, andvirtual screening to bio assay: Computation performance
Tadaaki Mashimo 1,2

Mitsuhito Wada 1,3

mashimo-tadaaki@aist.go.jp

m-wada@aist.go.jp

Yoshinori Wakabayashi 4

Yoshifumi Fukunishi 1,5

wakabayashi@by-hex.com

y-fukunishi@aist.go.jp

Haruki Nakamura 6
harukin@protein.osaka-u.ac.jp
1

Technology Research Association for Next generation Natural Products Chemistry 2 -3-26 Aomi,
Koto-ku, Tokyo, 135-0064, Japan2
IMSBIO Co., Ltd. Owl tower 6F, 4-21-1, Higashi-ikebukuro, Toshima-ku, Tokyo, 170-0013,
Japan
3
Fujitsu Ltd. 1-5-2, Higashi-shinbashi, Minato-ku, Tokyo, 105-7123, Japan
4
BY-HEX LLP 1-19-14, Shimizu, Suginami-ku, Tokyo, 167-0033, Japan
5
National Institute of Advanced Industrial Scie nce and Technology (AIST) 2-3-26 Aomi,
Koto-ku, Tokyo, 135-0064, Japan
6
Institute for Protein Research, Osaka University 3 -2 Yamadaoka, Suita-shi, Osaka, 565-0871,
Japan

Keywords: myPresto, Cloud computing, Virtual screening, Molecular dynamics, Performance“myPresto” [1] is a free computer software suite for drug design, including more than 20 programs
and a compound database: molecular dynamics (MD) [2] simulation, protein pocket search,
protein-ligand docking [3] , structure-/ligand-based virtual screening (VS) [4] , molecular editor,
solubility prediction, and synthetic accessibility. Several commercial programs with the graphicaluser interfaces enable us to easily operate these computational methods, to dramatically reduce
cost of cloud configuration (installation of software environment of cloud computer ) for these
programs, and to help ordering chemical compounds and using an assay service.
We evaluated the VS and MD simulations on a workstation and cloud computing resources, where
VS and MD simulations were examples of parallel and sequential computation application s. As aresult, the required cost of the VS and MD simulations showed unique trends depending on the
resources (processors, storages and network). The VS of 2-million compounds on an ordinary
workstation costed 10 hours. On the contrary, it costed from 1 hour/100 nodes to 10 hour/10 nodes
on a cloud computer. On average, the VS costed about 3,000 JPY on the cloud.
We will discuss the cost-efficient modeling of computations for cloud computing within short
CPU time.
[1] Y. Fukunishi, Y. Mikami, and H. Nakamura. J. Physical Chemistry B. 107 (47), 13201 -13210(2003).
[2] T. Mashimo, Y. Fukunishi, N. Kamiya, Y. Takano, I. Fukuda, and H. Nakamura. Journal ofChemical Theory and Computation. 9 (12) 5599 -5609 (2013).
[3] Y. Fukunishi, Y. Mikami, H. Nakamura. Journal of Molecular Graphics and Modelling. 24 (1),34-45 (2005).
[4] Y. Fukunishi, Y. Mikami, S. Kubota, and H. Nakamura. Journal of Molecular Graphics andModelling. 25 (1), 61-70 (2006).

P2-19ESPRESSO: An ultrafast compound pre-screening methodwith segmented compounds
Keisuke Yanagisawa 1,3

Shunta Komine 2,3yanagisawa@bi.cs.titech.ac.jp

komine@bi.cs.titech.ac.jp

Shogo D. Suzuki 2,3

Masahito Ohue 1,4

s_suzuki@bi.cs.titech.ac.jp

ohue@c.titech.ac.jp

Takashi Ishida 1,3,4
ishida@c.titech.ac.jp

1

2

3

4

Yutaka Akiyama 1,3,4
akiyama@c.titech.ac.jp

Department of Computer Science, School of Computing, Tokyo Institute of Technology, W8-76,
2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Department of Computer Science, Graduate School of Information Science and Engineering,Tokyo Institute of Technology, W8-76, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology,
J3-141, 4259, Nagatsutacho, Midori-ku, Yokohama City, Kanagawa 226-8501, Japan
Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research,
Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama City, Kanagawa
226-8501, Japan

Keywords: Virtual screening, Protein-ligand docking, Compound pre-screening, High-speed
computation
Recently, the number of available protein tertiary structures and compounds has increased.
However, structure-based virtual screening is computationally expensive due to docking
simulations. Thus, methods that filter out obviously unnecessary compounds prior tocomputationally expensive docking simulations have been proposed. However, the calculation
speed of these methods is not fast enough to evaluate more than 10 million compounds. In thispresentation, we propose a novel, docking-based pre-screening protocol named ESPRESSO(Extremely Speedy PRE-Screening method with Segmented cOmpounds) [1] . Partial structures(fragments) are often common among several compounds; therefore, the number of fragment
variations needed for evaluation is smaller than that of compounds. Our method increased
calculation speeds ~200-fold compared to conventional methods. ESPRESSO is written in C++ andPython, and it is available as an open-source code (http://www.bi.cs.titech.ac.jp/espresso/) under
the GPLv3 license.
[1] Yanagisawa K., et al., “ESPRESSO: An ultrafast compound pre-screening method based oncompound decomposition”, Proc. GIW2016. (accepted)

P2-20Singular Value Decomposition in the FMO-IFIE Analysis for Ligand
Screening
Keiya Maruyama1*, Yinglei Sheng1, Hirofumi Watanabe2, Kaori Fukuzawa3, Shigenori Tanaka1*
1
2
3

Department of Computational Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo
657-8501, Japan
Center for Collaborative Research and Technology Development, Kobe University, 1-1, Rokkodai, Nada-ku, Kobe, Hyogo 657-8501,
Japan
School of Dentistry at Matsudo, Nihon University, 2-870-1, Sakaecho-nishi, Matsudo, Chiba, 271-0061, Japan*email: maruyama@eniac.scitec.kobe-u.ac.jp, tanaka2@kobe-u.ac.jp

Keywords: p38 MAP kinase, Interaction energy analysis, Ligand binding, Fragment Molecular Orbital (FMO) method, IFIE (InterFragment Interaction Energy), Singular Value Decomposition (SVD)

In recent years, “In silico drug discovery” using high performance computing (HPC) technology such as large-scale molecularsimulations attracts much attention. However, computer simulations have a number of difficulties in accuracy as compared withexperiments. In this study, we evaluated the binding affinity between p38 MAP kinase[1] and various inhibitors by the fragment
molecular orbital (FMO)[2] method at MP2/6-31G* level in comparison with experimental values of IC50. Kinase p38 is a proteinthat plays an important role in the functional expression of various cellular processes that cause cell aging and autoimmune disease,and is activated by phosphorylation with external stress such as heat, osmotic pressure and ultraviolet rays.
It has been found that the calculated results of the FMO-IFIE[3, 4] (inter-fragment interaction energy) for 95 complex structures
registered on Protein Data Bank were not well correlated with the activity data of IC50. Therefore, we performed the singular value
decomposition (SVD)[5] for the calculated results of the IFIE matrix (amino acid residues × ligands) to find the reason why there
was not the good correlation and the direction for improving the correlation.
As a result, we succeeded to improve the correlation by rejecting some singular vectors that impair the correlation. Furthermore,
we analyzed the rejected singular vectors and found an overestimation of hydrogen bond between GLU71 and the ligand.
This study was conducted as one of the activities of “FMO Drug Design Consortium”, under the task of K computer “hp150160”
and “hp160103”. We thank the members of the FMO Drug Design Consortium for their providing the IFIE data on p38.

References[1] K.P. Wilson, P.G. McCaffrey, K. Hsiao, S. Pazhanisamy, V. Galullo, G.W. Bemis, M.J. Fitzgibbon, P.R. Caron, M.A. Murcko,
M.S. Su, The structural basis for the specificity of pyridinylimidazole inhibitors of p38 MAP kinase, Chem. Biol., 4: 423, 1997.
[2] K. Kitaura, E. Ikeo, T. Asada, T. Nakano, M. Uebayasi, Fragment molecular orbital method: an approximate computational
method for large molecules, Chem. Phys. Lett., 313: 701, 1999.
[3] S. Tanaka, Y. Mochizuki, Y. Komeiji, Y. Okiyama, K. Fukuzawa, Electron-Correlated Fragment-Molecular-Orbital Calculations
for Biomolecular and Nano Systems, Phys. Chem. Chem. Phys., 16: 10310, 2014.
[4] T. Nakano, T. Kaminuma, T. Sato, Y. Akiyama, M. Uebayasi, K. Kitaura, Fragment molecular orbital method: application topolypepties, Chem. Phys. Lett., 318: 614, 1999.
[5] G. H. Golub, C. Reinsch, Singular Value Decomposition and Least Square Solutions: Handbook Series Linear Algebra, Numer.
Math., 14: 403, 1970.

P2-21
GPCR/Membrane Model Builder for Molecular Dynamics Simulation
Mitsuhito Wada 1

Yoshifumi Fukunishi 2

m-wada@aist.go.jp

y-fukunishi@aist.go.jp

Haruki Nakamura 3
harukin@protein.osaka-u.ac.jp

1

2

3

Technology Research Association for Next Generation Natural Products Chemistry2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Molecular Profiling Research Center for Drug Discovery (molprof)
National Institute of Advanced Industrial Science and Technology (AIST)
2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Research Center for State-of-the-Art Functional Protein Analysis,
Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan

Keywords: GPCR, MD simulation, protein/membrane modelingG protein-coupled receptors (GPCRs) are the most important drug targets in human bodies .
For the purpose of elucidating the functions of GPCRs and their conformational changes in atomic
level, molecular dynamics (MD) simulations are extensively employed starting fromexperimentally solved structures or homology based theoretical models. However, without taking
account of explicit lipid bilayer environments, realistic MD simulations for GPCRs could not be
carried out.
To prepare the initial model for the MD simulations easily, we developed a tool building
coordinates of protein(s) embedded in membrane and generates a corresponding force-field file.
The scheme is as followings; (i) locate ligand and cholesterols to GPCR. (ii) palmitoylate Cys inhelix 8 if necessary. (iii) rotate and translate GPCR in suitable direction based on its charged
residue positions. (iv) place appropriate phospholipids around the GPCR. (v) solvate
GPCR/membrane complex in saline. (vi) carry out energy minimizations and MD simulation with
NPT condition for equilibrium. (vii) perform product runs with NVT ensemble. The tool isimplemented in myPresto program suit[1][2], which will be provided in our download site.

[1] Mashimo, T., Fukunishi, Y., Kamiya, N., Takano, Y., Fukuda, I., and Nakamura, H., Molecular
Dynamics Simulations Accelerated by GPU for Biological Macromolecules with a Non-Ewald
Scheme for Electrostatic Interactions, J. Chem. Theory Comput. 9: 5599-5609, 2013.
[2] Y. Fukunishi, Y., Mikami, Y., and Nakamura, H., Similarity among receptor pockets andamong compounds: Analysis and application to in silico ligand screening, The Journal of
Molecular Graphics and Modelling, 24:34-45, 2005.

P2-22
Postprocessing protein-ligand docking based on
the distance among interaction energy vectors
Nobuaki Yasuo 1

yasuo.n.aa@m.titech.ac.jp
12

Masakazu Sekijima 1,2

sekijima@c.titech.ac.jp

Department of Computer Science, Tokyo Institute of Technology, Ookayama, Meguro, Tokyo,152-8550 Japan.
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, Ookayama,
Meguro, Tokyo, 152-8550 Japan.

Keywords: Protein-ligand docking, Interaction analysis, Structure-based drug discovery
Protein-ligand docking is an important method in Structure-based Drug Discovery [1, 2].
Although many programs have been developed for docking [3], the accuracy is still insufficientdue to the difficulty in the scoring function [4]. Interaction fingerprint is one of the solutions,which generate fingerprints of ligands using the interactions between the ligand and the protein.
Interaction fingerprints use the information of known compounds so that new compounds that have
similar interaction to the known active ligand are expected to find through the virtual screening.
However, existing interaction fingerprints such as SIFt [5] and SPLIF [6] only assess the existenceor the distance of the interactions and do not consider the strength correctly. In this study, we
made a new scoring function of protein-ligand docking called SIEVE-Score (Similarity ofInteraction Energy VEctor-Score), which can consider the strength of each interaction explicitly.
SIEVE-Score is calculated based on the similarity of the interaction energy vector, which is the listof interaction energy between the ligand and each residue of the protein. We also evaluate theaccuracy of virtual screening using SIEVE-Score after the docking by Glide [7].
[1] Grinter S. Z, and Zou X., Challenges, applications, and recent advances of protein-ligand
docking in structure-based drug design. Molecules, 19(7):10150-76, 2014.
[2] Chiba, S., et al. Identification of potential inhibitors based on compound proposal contest:
Tyrosine-protein kinase Yes as a target. Scientific reports 5:17209, 2015.
[3] Elizabeth Y., Jessica H., and Paul A. R., Improvements, trends, and new ideas in moleculardocking: 2012–2013 in review. Journal of Molecular Recognition, 28(10):581–604, 2015.
[4] Yan L., Li H., Zhihai L., and Renxiao W., Comparative assessment of scoring functions on an
updated benchmark: 2. evaluation methods and general results. Journal of Chemical
Information and Modeling, 54(6):1717–1736, 2014.
[5] Zhan D., Claudio C., and Juswinder S., Structural interaction fingerprint (SIFt): a novel method
for analyzing three-dimensional protein-ligand binding interactions. Journal of Medicinal
Chemistry, 47(2):337–344, 2004.
[6] Da C., and Kireev D., Structural protein–ligand interaction fingerprints (SPLIF) for structurebased virtual screening: Method and benchmark study. Journal of Chemical Information andModeling, 54(9):2555–2561, 2014.
[7] Richard A. F., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. method
and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7):1739–1749, 2004.

P2-23
Drug repositioning for Chagas disease by docking
simulation and in vitro assay

1

2

3

Ryunosuke Yoshino 1

Nobuaki Yasuo 2yoshino.r.aa@m.titech.ac.jp

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

Hiromasa Kuroda 2

Masakazu Sekijima 2,3

kuroda.h.ac@m.titech.ac.jp

sekijima@c.titech.ac.jp

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi, Bunkyo,
Tokyo 113-8657 Japan.
Department of Computer Science, Tokyo Institute of Technology, Ookayama, Meguro, Tokyo,152-8550 Japan.
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, Ookayama,
Meguro, Tokyo, 152-8550 Japan.

Keywords: Drug repositioning, Protein-ligand docking, Chagas disease, Spermidine synthase
Chagas disease is a kind of neglected tropical diseases (NTDs) caused by the parasite
Trypanosoma cruzi [1-2]. Although nifurtimox and benznidazole are currently available fortreatment of Chagas disease, these drugs are associated with serious side effects and exhibit
limited effectiveness during the chronic phase of Chagas disease . Drug repositioning uses thedrugs which were already approved for different targets, and has been recognized as an effective
strategy because repositioned drugs have been through several R&D preclinical and clinical phases[5].
In order to discover a new anti-Chagas drug, we focused on spermidine synthase (SpdSyn) which
synthesize polyamine as target protein, and conducted docking simulation and in vitro assay.
Docking simulation [6,7] was performed for SpdSyn to select drug candidate from among 39,218
compounds which were already approved or tried clinical phase. Moreover, we conducted in vitro
assay to determine the half maximal inhibitory concentration value (IC 50 ). As a result, we
successfully obtained active compounds with IC 50 values of the 10 M order against SpdSyn.
[1] Lockman J.W., Hamilton A.D., (2005) Recent developments in the identification ofchemotherapeutics for Chagas disease. Curr. Med. Chem. 12: 945-959.
[2] Hayley E.M., et al., (2014) Neglected Zoonotic Diseases—The Long and Winding Road to
Advocacy, PLOS Neglected Tropical Diseases, 8(6): e2800.
[3] Van den Bossche H., (1978) Chemotherapy for parasitic infections. Nature 626-630.
[4] Linares G.E.G., Ravaschino E.L., Rodriguez J.B., (2006) Progresses in the field of drug design
to combat tropical protozoan parasitic diseases. Curr. Med. Chem., 13: 335-360.
[5] Roberto W, et al., (2016) Drug-repositioning opportunities for cancer therapy: novel moleculartargets for known compounds, Drug Discov Today 291:1304-1351, 2001.
[6] Richar A.F., et al., (2004) Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1.
Method and Assessment of Docking Accuracy. J. Med. Chem., 47: 1739-1749.
[7] Chiba S., et al., (2015) Identification of potential inhibitors base d on compound proposal contest:
Tyrosine-protein kinase Yes as a target Scientific Reports, 5: doi:10.1038/srep17209

P2-24Development of the selective inhibitor of WNK1
using fragment compounds
Nae Saito 1

Yukio Tada2

nae-saito@mol.f.u-tokyo.ac.jp

tada.y.af@m.titech.ac.jp

Takayoshi Okabe 1

Tetsuo Nagano 1

tokabe@mol.f.u-tokyo.ac.jp

tlong@mol.f.u-tokyo.ac.jp

1

Drug Discovery Initiative, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033,
Japan
2
Interdisciplinary Graduate School of Science, Tokyo Institute of Technology, 3-3-6 Shibaura,
Minato-ku, Tokyo 108-1123, Japan

Keywords: Fragment library, WNK1, docking simulation
Pseudohypoaldosteronism type II has been known as a rare autosomal dominant disorderallegedly triggered by overexpression of WNK1 [with no lysine(K)] or mutations in WNK4. Thusfar, there have been only a few reports regarding the specific inhibitor for WNK1 or WNK4.
The WNK1 has the back pocket just behind the ATP binding site (Figure 1). Therefore, wetargeted the back pocket rarely existed in the common kinase rather than the ATP binding site inorder to get more selective inhibitor.
In order to harness the peculiar structure and get more selective inhibitor, we targeted the backpocket less existed in the common kinase, rather than the abundant ATP binding site. A docking
simulation was conducted using MOE with Amber 10: EHT, and ca. 9,000 kinds of fragment
library compounds in Drug Discovery Initiative were assessed here.
Firstly, we classified the higher-frequency scaffolds that were in the top 20% of GBVI/WSA dG
scores into three types (Figure 2) and then examined the interaction of the selected compoundswith WNK1 by MOE. Using mobility shift assay and surface plasmon resonance techniques, we
assessed the inhibitory activity for phosphorylation of WNK1 and the binding abilities to WNK1
as well.
TypeI

Back pocket
TypeIIATP binding site
TypeIII

Figure 1. Structure of WNK1

Figure 2. Three types of compounds and some examples.

P2-25
Discovery of Novel LPA-1 Antagonists- Computational Analysis for Molecular Design Hiromu Egashira 1

Taku Fujimoto 1

Masahiko Terakado 1

Haruto Kurata 1

egashira@ono.co.jp

ta.fujimoto@ono.co.jp

terakado@ono.co.jp

h.kurata@ono.co.jp

Osamu Ichihara 2
osamu.ichihara@schrodinger.com

1

2

Minoru Nishizaki 1

Toru Maruyama 1

nishizaki@ono.co.jp

to.maruyama@ono.co.jp

Medicinal Chemistry Research Laboratories, Ono Pharmaceutical Co., Ltd., 3-1-1 Sakurai,
Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan.
Schrödinger K.K, 17F Marunouchi Trust Tower North, 1-8-1 Marunouchi, Chiyoda-ku, Tokyo
100-0005, Japan

Keywords: LPA1, GPCR, Molecular Design, WaterMap, MD simulation
Lysophosphatidic acid (LPA) evokes various physiological responses through a series of G
protein-coupled receptors known as LPA1-6. We conducted a high throughput screen against LPA1
to give a hit compound. Subsequent optimization of the hit compound led to the discovery of noveland potent LPA1 antagonists, which showed good efficacy in vivo. 1),2) We also successfully
determined the first crystal structures of LPA1 in complex with antagonists. 3)
In recent years, various kinds of X-ray crystal
structure analysis of GPCR have been reported.
Some of such studies include molecular design
based on GPCR X-ray structures, but it is stillchallenging to develop potent and selective
ligands for GPCRs by SBDD approach.
In our presentation, we will show someexamples of SBDD approach based on the
LPA1 X-ray crystal structures. Molecular
design based on the ligand-receptor interaction
map and WaterMap (Schr ö dinger Inc.) 4)
analysis of the ligand binding pocket will be
discussed.
LPA1 Complex WaterMap Result[1] M. Terakado, et al., Discovery of ONO-7300243 from a Novel Class of Lysophosphatidic Acid
Receptor 1 Antagonists: From Hit to Lead, ACS Med. Chem. Lett, ASAP, 10.1021/
acsmedchemlett.6b00225.
[2] M. Terakado, et al., 250th ACS National Meeting[3] J. E. Chrencik, et al., Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid
Receptor 1, 2015, Cell, 161, 1633–1643[4] T. Lazaridis, et al., Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory,
1998, J. Phys. Chem. B, 102, 3531-3541

P2-26
Semi-automated FMO calculation protocol
for construction of IFIE-database
Chiduru Watanabe 1

Yoshio Okiyama 1

Daisuke Takaya 1

chiduru.watanabe@riken.jp

yoshio.okiyama@riken.jp

daisuke.takaya@riken.jp

Shunpei Nagase 1

Kikuko Kamisaka1

Hirofumi Watanabe 2

shunpei.nagase@riken.jp

kikuko.kamisaka@riken.jp

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

Teruki Honma 1
honma.teruki@riken.jp

1
2

RIKEN Center for Life Science Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan
Kobe University Center for Collaborative Research and Technology Development 1-1,
Rokkodai, Nada, Kobe 657-8501, Japan

Keywords: Modeling, Optimization, MM-PBSA, FMO, Semi-automation, IFIE-database
Since 2014, we have performed fragment molecular orbital (FMO) calculations for various drug
targets (e.g. Kinase, Nuclear receptor, Protease, PPI) with activity values (e.g. IC 50 ) as theactivities of FMO drug design (FMODD) consortium. To efficiently calculate huge amount ofdifferent structures, we must refine modeling conditions. For example, appropriate structure
preparation, which includes complementation of missing atoms and missing residues, addition ofhydrogen atoms, and structure minimization, is necessary as pretreatme nt before FMO calculations.
However, the appropriate method(s) for preprocess are not yet established. Thus we have discussed
the modeling condition (e.g. complementation of heavy atoms, with/without water molecules,
restraint of heavy atoms on minimization) in FMODD consortium based on the results so far.
Know-how of the modeling has accumulated. Another issue is treating massive structure data (e.g.
PDB having ca. 120,000 entries) to construct IFIE-database (IFIE-DB) in the future. There arelimits to prepare huge amount of structures by manual labor. Thus, we are currently developing“semi-automated FMO calculation protocol” using pipeline pilot as described below.
Semi-automated FMO calculation protocol with Pipeline Pilot
1

2

3

Structure preparation of protein and ligand complex.
1.1 Correction of missing atoms and missing residues based on PDB files.
1.2 Structure optimization by MM method with MOE software.
1.3 Structure optimization by QM/MM method with Gaussian (option)
MM-PBSA/GBSA calculations
2.1 Input files generation of MM-PBSA/GBSA calculation.
2.2 Perform MM-PBSA/GBSA calculation on PC cluster machine with AMBER.
2.3 Analysis of solvation and binding energies between ligand and protein.
FMO calculations
3.1 Input files generation FMO calculation.
3.2 Perform FMO calculation on PC cluster machine or K-computer with ABINIT-MP.
3.3 Analysis of IFIE between ligand and protein and its energy decomposition with PIEDA .

This research was done in activities of the FMO drug design consortiu m (FMODD). The results of FMO
calculations were obtained using the K computer (project ID: hp150160 and hp160103). Science
Technology Systems supported to develop the FMO calculation protocol. The FMO calculation protocol is
automated by BIOVIA Pipeline Pilot from Dassault Systè mes BIOVIA. Structure modeling and MM
optimization were carried out by MOE software package. QM/MM optimization was using Gaussian09 andwere used computational resources of the supercomputer, HOKUSAI (RIKEN Advanced Center for
Computing and Communications).. MM-PBSA was performed by AMBER 12. PIEDA calculation was doneby using MIZUHO/BioStation software package.

P2-27
Flexible ligand docking to multiple receptor:
application to tankyrase2-ligand system

1

Yoshinori Hirano 1

Noriaki Okimoto 1

hirano@riken.jp

okimoto@riken.jp

Shigeo Fujita 1

Makoto Taiji 1shigeo.fujita@riken.jp

taiji@riken.jp

Laboratory for Computational Molecular Design, Computational Biology Research Core,
Quantitative Biology Center (QBiC), RIKEN, 2F, QBiC Building B, 6-2-4 Furuedai,
Suita, Osaka 565-0874, Japan

Keywords: Molecular Screening, Multiple Protein Structures
In early stage of drug discovery, computational compound screening is widely used to find
hit compounds. Molecular docking is one of the principal methods for the compound screening.
This method generally docks a flexible ligand to a rigid (static) protein structure and predicts thebinding pose and affinity for ligand in a practical time. As the different structural characteristics(chemotypes) of compounds induce different receptor’s binding pocket conformations, moleculardocking tends to predict that a specific ligand whose chemotype binds to a specific pocket
conformation binds to other binding pocket conformation improperly and weakly. To resolve theproblem, incorporation of protein flexibility into molecular docking is required. Moleculardocking using multiple receptor conformations (“ensemble docking” [1-3]) is one of approaches toconsider protein flexibility. These multiple receptor conformations can be obtained by X-ray
crystallographic structures and/or structures sampled by molecular dynamics (MD) simulations.
However, there still remains difficulty on proper generation and selection of multiple receptors
used in ensemble docking.
In this work, we study the effective method of ensemble docking for tankyrase2-ligand
system. From analysis of X-ray crystallographic structures on tankyrase2-ligand system, we foundthree tankyrase2-ligand complex structures where the chemotypes of ligands and the conformationof binding pocket are so different, respectively. In three receptor conformations, we found that
positions of side chains of His and two or three Tyr residues at binding pocket are closely related
to conformations of binding pocket and focused on these residues to classify receptor
conformations. To generate various receptor conformations, several MD simulations of tankyrase2
with and without ligand were performed. We are researching various methods on classification andselection of receptor’s binding pocket conformations obtained from MD simulations for effective
ensemble docking and gives information on the relationship between chemotypes of ligands andreceptor’s pocket conformations.

References:[1] Totrov M. and Abagyan R., Current Opinion in Structure Biology, 2008, 18:178-184[2] Barril X. and Morley S. D., J. Med Chem. 2005, 48: 4432-4443.
[3] Knegtel R. M. et al., J. Mol. Biol. 1997, 266:424–40.

P2-28An Application of Deep Learning for Classifying Chemical Structures

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: Deep Leaning, Caffe, Chemical Structure
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. In this work we demonstrate how deep neural networks (DNN)
trained on images of chemical structure can classify various drugs to therapeutic chemical
categories based on the INN (international nonproprietary names) stems. We prepared an
automated scheme to retrieve chemical table files from the KEGG database[1] and to transformthem to chemical structure images. The Caffe framework[2] was used to construct DNN models.
GPU-accelerated parallel computing platforms were developed for data training on Amazon Web
Services(AWS), a suite of cloud-computing service. Cross-validation experiments showed thatthe DNN-based models achieved extremely high classification accuracy in predicting therapeutic
category for each chemical structure image. This preliminary work indicates that DNN extract
features from chemical structure images successfully. Our next goals are applying DNN toproblems related to prediction of biological activities and drug repositioning.

[1] http://www.genome.jp/kegg/[2] http://caffe.berkeleyvision.org/

P3-01
Generation of Amino Acid Indicesto Distinguish Two Peptide Groups
Riho Miyaki 1

dsq65581@kwansei.ac.jp 	
  	
 Hiroyuki Toh 1
tohhir@kwansei.ac.jp

Kazuo Ohnishi 2
ohnishik@nih.go.jp

1
2

Department of Biomedical Chemistry, School of Science and Technology, Kwansei Gakuin
University, 2-1 Gakuen, Sanda, Hyogo, 669-1337, Japan
Department of Immunology, National Institute of Infectious Disease, 1-23-1 Toyama,
Shinjuku-ku, Tokyo, 162-8640, Japan

Keywords: Genetic algorithm, Amino acid index, Principal component analysis
Classification of data is a basic step for data mining, where multivariate analyses, such as principal
component analysis (PCA), are widely used. The approach is also used for the classification of
peptides in the field of proteochemometrics. In the analysis, a peptide is often expressed as a set ofseveral numerical values with different amino acid indices. However, there are several problems in
the approach with the amino acid indices. One of the problems is the number of amino acid indices.
There are many amino acid indices, which are available from the KEGG database, but there is noclear criterion to select the indices for the analysis. The other problem is the result of theclassification. Even if we applied PCA, for example, to the classification of peptides, there is no
proof that the peptides are classified into groups, for which proper interpretation is possible. Bothproblems are intertwined each other. In this presentation, we propose a method to solve theproblems simultaneously. Consider that we have two sets of peptides. The object is to find aminoacid indices to distinguish the two groups in the space of reduced dimensions by PCA. Instead ofusing the currently available amino acid indices, we generate the amino acid indices to fit theobject described above by the genetic algorithm (GA). The fitness for an amino acid index isdefined as a product of a modified Fisher’s ratio, which is used for the discriminant analysis, in thespace of the reduced dimensions by PCA and the contribution ratio for GA. The chromosome
consists of a set of amino acid indices and a set of two parameters. The size of the former set isequal to that of the latter. Therefore, a pair of parameters corresponds to an amino acid index. Eachset of the two parameters, which is used to express a beta function, is introduced to treat the
peptides of different length. As a result, the sets of amino acid indices and the parameters are
optimized and the two groups are distinguished in the space of the reduced dimensions by PCA.
The program was constructed by the C language. The performance of our method is discussed.
[1] van Westen, G. J., et al., Benchmarking of protein descriptor sets in proteochemometricmodeling (part 1): comparative study of 13 amino acid descriptor sets. Journal ofcheminformatics, 5.1 (2013): 1.
[2] Holland, J. H., Adaptation in natural and artificial systems: an introductory analysis withapplications to biology, control, and artificial intelligence. U Michigan Press, 1975.
[3] Nalband, S., et al., Feature selection and classification methodology for the detection ofknee-joint disorders. Computer methods and programs in biomedicine, 127, 94-104, 2016.
[4] Nguyen, T., et al., Mass spectrometry cancer data classification using wavelets and genetic
algorithm. FEBS letters, 589(24), 3879-3886, 2015.
[5] http://www.genome.jp/aaindex/

P3-02

Network-based-Analysis of
Glycosyltransferase and Protein Interaction
Shogo Satoyama1
ehq60382@kwansei.ac.jp

1

	
  	
 

Hiroyuki Toh1
tohhir@kwansei.ac.jp

Bioscience, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen,
Sanda, Hyogo, 6691337, Japan

Keywords: Network, Glycan chain
Most of the secreted and cell membrane proteins in mammals are glycosylated, and the glycan
chains constitute a representative group of biopolymers [1]. Unlike nucleic acid and amino acidsequences, the glycan chain has a branched structure with a wide variety [2]. It is known that theglycosylation of proteins is deeply involved in the regulation of the function and the structural
stability of proteins. However, the relationship between diseases and glycoproteins still remains
unclear. Barabasi and his colleagues reported a method to derive the relationship between genes andthat between diseases by marginalizing genes or diseases in a bipartite graph consisting of diseases
and associated genes to constitute a network of only diseases or genes [3]. In this study, we tried toconstruct an interaction network of glycosyltransferases and target proteins based on the similar
approach. At first, the information about the diseases related to proteins was obtained from OMIM.
Then, the secreted proteins and membrane proteins including glycosyltransferases were collectedbased on the GO annotation. A bipartite graph between disease and collected proteins was generated.
Finally, a network of the proteins was generated by the marginalization of diseases. The features of
the network associated with the glycosylation are discussed.

[1] O'Riordan, N., et al., Structural and functional characteristics of bovine milk protein
glycosylation. Glycobiology,24, 220-236, 2014.
[2] 産業技術総合研究所., きちんとわかる糖鎖工学., 白日社., 2010[3] Goh, K. I., et al., The human disease network. Proc.Natl.Acad.Sci.U.S.A., 104, 8685-8690, 2007.

P3-03
Neuronaldifferentiation
model
constructedfromcombination of network motifs in the comprehensive
molecular interaction map
Tsuyoshi Iwasaki 1
tsuyoshi-iwasaki@ds-pharma.co.jp

Ryo Takiguchi 2
takiguchi@bio.keio.ac.jp

Tadamasa Kimura 2

Takumi Hiraiwa 2
hiraiwa@bio.keio.ac.jp

hiroi@bio.keio.ac.jp

2kazuto-yamazaki@ds-pharma.co.jp

kimura@bio.keio.ac.jp

Noriko Hiroi 2

1

Kazuto Yamazaki 1

Akira Funahashi 2
funa@bio.keio.ac.jp

Sumitomo Dainippon Pharma Co., Ltd., 3-1-98, Kasugade-naka, Konohana-ku, Osaka,554-0022, Japan
Department of Biosciences and Informatics, Keio University, 3-14-1, Hiyoshi, Kouhoku-ku,
Yokohama, 223-8522, Japan

Keywords: Neuronal differentiation, Signaling pathway, network motif, CellDesigner
The regenerative medicine mediated by neurogenesis is expected to remedy refractory
neurological disorders like spinal cord injury. Although various studies have been conducted toestablish the therapy mediated by neurogenesis, it is a big problem because the underlying
mechanism of neuronal differentiation remains to be elucidated. To understand a neuronaldifferentiation which has very complex biological processes, an approach of systems biology is
expected to be effective. However, existing bottom-up or top-down approaches of systems
biology is not appropriate for comprehensive and complex biological process like neuronaldifferentiation. To analyze such comprehensive and complex problem, we used hybrid method ofbottom-up and top-down. In the method, network motifs which are important to cell dynamics are
extracted from the comprehensive network of target biological process. And then, mathematicalmodel is constructed by piecing together the motifs. The model is expected to maintain original
cell dynamics of comprehensive network. We applied the method to comprehensive network ofneuronal differentiation that was constructed by relations collected from literatures and pathway
databases, and got simple mathematical model of neuronal differentiation . The model could
simulate the characteristic dynamics of HES1 and ASCL1 changes before and after differentiation.
This mathematical model will be useful to understand the mechanism of neuronal differentiation.
[1] Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U., Science, 298,
824–827, 2002.
[2] Imayoshi, I., Isomura, A., Harima, Y., Kawaguchi, K., Kori, H., Miyachi, H., Fujiwara, T.K.,
Ishidate, F. and Kageyama, R, Science, 342, 1203-1208, 2013.

P3-04
Prediction of interacting proteinsfrom homologous protein-protein interactions
Yoichi Murakami 1

yoichi@nibiohn.go.jp
1

2

	
  	
 

Kenji Mizuguchi 1,2

kenji@nibiohn.go.jp

Center for Drug Design Research, Laboratory of In-silico Drug Design, National Institutes ofBiomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki-shi, Osaka, 567-0085,
Japan
Bioinformatics Project, National Institutes of Biomedical Innovation, Health and Nutrition,
7-6-8, Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan

Keywords: Prediction of protein-protein interactions, Homology, Machine Learning, AODE
The identification of protein-protein interactions (PPIs) is essential for a better understanding of
biological processes, pathways and functions. However, experimental identification of thecomplete set of PPIs in a cell/organism remains a difficult task. To circumvent limitations ofcurrent high-throughput experimental techniques, it is necessary to develop high-performancecomputational methods for predicting PPIs. In this study, we propose a new computational method
to predict proteins that interact with a given protein using three sequence-based features derived
from known PPIs using Averaged One-Dependence Estimators (AODE; [1]). The three features
are: (I) sequence similarities to a known interacting protein pair (F Seq ), (II) statistical propensitiesof domain pairs observed in the protein pair (F Dom ) and (III) a sum of edge weights along theshortest path between their homologous proteins in a PPI network (F Net ) [2].
The AODE models were trained on the highly imbalanced, realistic dataset, which contained only
0.13% of high confidence, direct physical human PPIs (43,060 positive PPIs / 33,098,951 negative
PPIs) obtained from TargetMine [3], and evaluated in 3-fold cross validation. As a result, the
AODE model trained with all the three features, F Net , F Dom and F Seq , achieved the highest AUC of
0.887 (pAUC FPR ≤ 0.5% = 0.243). These models were applied to the identification of all the proteinsthat would interact with a query protein. This new computational system can assess the potential
interactions between a query protein and the proteins registered in the UniProt database at highspeed, and then it prioritizes the predicted interactions according to their prediction scores. The
proposed method and the new database scanning system will contribute to the identification ofnovel PPIs and the estimation of complete PPI networks.
[1] Webb GI, Boughton JR, and Wang Z, Not so naive Bayes: Aggregating one-dependence
estimators. In Machine Learning. Volume 58, Issue 1. Netherlands: Springer; 2005:5–24.
[2] Murakami Y, and Mizuguchi K, Homology-based prediction of interactions between proteinsusing Averaged One-Dependence Estimators, BMC Bioinformatics, 15:213, 2015.
[3] Chen Y-A, Tripathi LP, and Mizuguchi K, An integrative data analysis platform for gene set
analysis and knowledge discovery in a data warehouse framework. Database, Mar 17;2016. pii:
baw009, 2016.

P3-05
Toxygates for toxicogenomics analysis: an update
Y ayoi N atsu m e-K itatan i 1

Joh an N yström -P ersson 2

natsume@nibiohn.go.jp

johan@level-five.jp

1

2

	
  	
 

Y osh in ob u Igarash i 1
y-igarashi@nibiohn.go.jp

Daisuke Satoh 2

Kenji Mizuguchi 1

daisuke.satoh@level-five.jp

kenji@nibiohn.go.jp

National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), 7-6-8, Asagi,
Saito, Ibaraki-shi, Osaka 567-0085, Japan
Level Five Co. Ltd., 1-2-3, Kaigan, Minato-ku, Tokyo 105-0022, Japan

Keywords: Toxicogenomics, web application, microarray
In order to assess the safety of drugs based on their gene expression profiles, the Japanese
Toxicogenomics Project consortium (TGP) [1] collected microarray data and pathology
information for about 200 compounds and developed a toxicogenomics database, Open TG-GATEs[2], which is open to public and now widely used all over the world. Toxygates [3] was originallydeveloped as a user-friendly interface to enable users to access the data deposited in OpenTG-GATEs. Here we report recent updates in Toxygates, which include the “user data upload
function”, “interactive heatmap”, “integration with TargetMine [4] for enrichment analysis” and“multi-species mode”.
[1] Uehara, T., Ono, A., Maruyama, T., Kato, I., Yamada, H., Ohno, Y., and Urushidani, T., The
Japanese toxicogenomics project: application of toxicogenomics, Mol Nutr Food Res,
54(2):218-27, 2010.
[2] 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 Res, 43(Databaseissue):D921-7, 2015.
[3] N yström -P ersson, J., Igarashi, Y ., Ito, M ., M orita, M ., N akatsu, N ., Y am ada, H ., andM izuguchi, K ., T oxygates: interactive toxicity analysis on a hybrid m icroarray andlinked data platform , B ioinform atics, 29(23):3080-6, 2013 .
[4] Chen, Y.A., Tripathi, L.P., and Mizuguchi, K., TargetMine, an integrated data warehouse forcandidate gene prioritization and target discovery, PLoS One, 6(3):e17844, 2011.

P3-07
Network inference from promoter activity in lung cancer
Yukiko Sano 1

y-sano@juntendo.ac.jp

Hideya Kawaji 3,4

kawaji@gsc.riken.jp
1
2
3
4
5

Kazuya Takamochi 2

ktakamo@juntendo.ac.jp

Yoshihide Hayashizaki 4

yoshihide.hayashizaki@riken.jp

Kenji Suzuki 2

kjsuzuki@juntendo.ac.jp

Kaoru Mogushi 5

kmogushi@juntendo.ac.jp

Center for Genomic and Regenerative Medicine, Graduate School of Medicine, Juntendo
University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
Department of Thoracic Surgery, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo
113-8421, Japan
Preventive Medicine and Applied Genomics Unit, RIKEN Advanced Center for Computing andCommunication, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, JapanRIKEN Preventive Medicine and Diagnosis Innovation Program,1-7-22, Suehiro-cho,
Tsurumi-ku, Yokohama, 230-0045, Japan
Intractable Disease Research Center, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo
113-8421, Japan

Keywords: Lung cancer, cap analysis of gene expression (CAGE), gene regulatory network (GRN)
Various molecular mechanisms in carcinogenesis have been revealed by recent studies, and moleculartargeted drugs have also been developed one after another. For example, it has been reported thatspecific mutations in the epidermal growth factor receptor (EGFR) gene are associated with sensitivity
to EGFR inhibitors such as gefitinib and erlotinib in lung cancer patients [1]. However, many
challenges in drug discovery (e.g., acquisition of resistance to therapy, different mechanisms in
carcinogenesis and progression, and prediction of drug efficacy in individual patients) have been left
for the future.
In this study, we aimed to investigate the gene regulatory networks (GRNs) in lung cancer in order toreveal the molecular characteristics of the disease subtypes. We used the surgical specimens of lungsquamous cell carcinoma (SCC, n=22) and adenocarcinoma (AD, n=73) at the Department of Thoracic
Surgery, Juntendo University. The expression profiles of gene promoters were obtained by the cap
analysis of gene expression (CAGE) method [2] using the RNA samples extracted from each specimen.
Then, we constructed GRNs for each subtype of lung cancer using GINIE3 software [3], which is oneof the best algorithms to estimate GRNs from gene expression profiles. Then, we compared andinterpreted the functional differences in the promoter activity among each subtype of lung cancer.
In the AD patients, we carried out comparison between of EGFR mutation-positive group and thewild-type group. By inferring the network among the expression regulatory genes, co-expression was
found on the transcription factors that involved in cancer (HMGA1/2), homeobox genes(ONECUT2/3),
and nuclear receptor (HNF4A). We will also investigate the network differences from patients with thegene mutations in KRAS.

[1] Sharma, S.V., et al., Epidermal growth factor receptor mutations in lung cancer, Nature
Reviews Cancer, 7:169-181, 2007.
[2] Murata, M., et al., Detecting expressed genes using CAGE, Methods in Molecular Biology,
1164:67-85, 2014.
[3] Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Geurts, P. , Inferring regulatory networks
from expression data using tree-based methods , PLoS ONE, 5: e12776 , 2010.

P3-08An integrative systems analysis of gene expression profilesof the fibrotic lung from CD151 knockout mice reveals
insights into the mechanisms of lung fibrosis
Lokesh P. Tripathi 1

lokesh@nibiohn.go.jp

Mari N Itoh 1

mari@nibiohn.go.jp

Yoshito Takeda 3
yoshito@imed3.med.osaka-u.ac.jp

Yasushi Kondo 2

yasushi-kondo@ds-pharma.co.jp

Kenji Mizuguchi 1
kenji@nibiohn.go.jp

1

National Institutes of Bio medical Innovation, Health and Nutrition, 7 -6-8 Saito-asagi,
Ibaraki, Osaka 567-0085, Japan
2
Drug Research Division, Dainippon Sumitomo Pharma Co., Ltd, Suita, Osaka, 564 -0053, Japan3
Departments of Respiratory Medicine, Allergy and Rheumatic Diseases, Osaka University
Graduate School of Medicine, Suita, Osaka, 565-0871, Japan,, Keywords: Disease omics analysis, Pathway analysis, Translational systems biology

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, irreversible, and age -related lungdisease associated with high morbidity and poo r survival. Because the exact mechanisms
responsible for the development of IPF remain largely unknown, there are currently no proventherapies targeting the fibrotic process itself. Previously, we found that tetraspanin CD151
knockout mice spontaneously developed fibrosis 1) . In this study, we have employed integrative
systems biology-based computational approaches 2) to investigate the global gene expression
profiles of the fibrotic lung from CD151 knockout mice. We analyzed genes that weredifferentially expressed in the mouse disease model by constructing protein-protein interaction(PPI) networks and performing pathway enrichment and topological analysis with TargetMine data
analysis platform 3) . Our analysis illuminated specific genes and pathways that explain the onset
and progression of pulmonary fibrosis. We observed an overall upregulation of factors associatedwith extracellular matrix deposition and a specific involvement of the circadian clock pathway inthe fibrotic process. Our analysis provided novel insights into the role of CD151 function
pulmonary fibrosis and identified potential targets for better anti-fibrotic therapy and potentiallynovel biomarkers of IPF.
[1] Tsujino K, Takeda Y, Itoh M., et al., Tetraspanin CD151 protects against pulmonary fibrosis by
maintaining epithelial integrity, Am J Respir Crit Care Med. 186(2):170-80, 2012.
[2] Chen Y.A, Tripathi L.P. and Mizuguchi K., An integrative data analysis platform for gene set
analysis and knowledge discovery in a data warehouse f ramework, Database, 1-14, 2016.
[3] http://targetmine.mizuguchilab.org

P3-09Pathway-based drug discovery and repositioning bylarge-scale chemically-induced transcriptomics
Michio Iwata 1

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

Masaaki Kotera 2

maskot@bio.titech.ac.jp
1

2

3

4

Ryusuke Sawada 1

rsawada@bioreg.kyushu-u.ac.jp

Yoshihiro Yamanishi 1,3,4

yamanishi@bioreg.kyushu-u.ac.jp

Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, 3-1-1
Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan
School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama,
Meguro-ku, Tokyo, 152-8550, Japan
Institute for Advanced Study, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka,
Fukuoka 812-8582, Japan
PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan

Keywords: bioactive compound, drug repositioning, drug target, modes of action, transcriptome
The identification of the modes of action of bioactive compounds is a major challenge inchemical systems biology of diseases. Diseases are caused by dysfunction of the human biologicalsystem, which consists of genes, proteins, and pathways. Most drugs are bioactive compounds thatmodulate the activity of biological systems for the treatment of diseases. However, drugs may
interact not only with their primary target proteins but also with other proteins (off-targets) andthereby cause unexpected side effects. Side effects derived from off-target interactions, althoughtypically undesired, may occasionally enable new therapeutic indications [1] because different
diseases often share same or similar pathogenic mechanisms. Therefore, u nderstanding thecomplex responses of the human body to the treatments of bioactive compounds is a vital
important issue in medical and pharmaceutical research. A promising unbiased approach is
genome-wide gene expression profiling of the transcriptional response to chemical
perturbations of human cell lines.
Here we developed a novel computational approach to elucidate the modes of actionof bioactive compounds using large-scale chemically-induced transcriptome data acquired
from the Library of Integrated Network-based Cellular Signatures, and analyzed 16,268
compounds (including most approved drugs) and 68 human cell lines (e.g., MCF7, PC3 andHL60). First, we performed pathway enrichment analyses of regulated genes to reveal
active pathways among 163 pathways. Next, we explored potential target proteins(including primary targets and off-targets) with cell-specific transcriptional similarity
using chemical–protein interactome. Finally, we predicted new therapeutic indications for461 diseases based on the target proteins. We demonstrated the usefulness of the proposed
approach and confirmed its advantages over the previous related approach [2] in terms ofprediction coverage, interpretation, and large-scale applicability. We also validated thenew prediction results experimentally by an in vitro cellular assay. The approach has a highpotential for advancing drug discovery and repositioning.
[1] Ashburn, T.T. and Thor, K.B., Drug repositioning: identifying and developing new uses forexisting drugs, Nature Reviews Drug Discovery, 3:673-683, 2004.
[2] Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small
molecules, genes, and disease, Science, 313:1929-1935, 2006.

P3-10Evaluating immune related function and characteristic
analysis of the Okinawan lactic acid bacteria
Shinya Ikematsu 1
ikematsu@okinawa-ct.ac.jp

Ipputa Tada 1

Makoto Miyagi 1
ac144811@edu.okinawa-ct.ac.jp

Yuichi Inoue 2

tipputa@nig.ac.jp inoue@kct.ac.jp

Hiroharu Kawahara 2
hk128@kct.ac.jp

12

Bioresources Engineering, National Institute of Technology, Okinawa College, 905 Henoko,
Nago, Okinawa 905-2192, Japan
National Institute of Technology, Kitakyushu College, 5-20-1, Shii, Kokuraminami-ku,
Kitakyushu, Fukuoka 802-0985, Japan

Keywords: Lactic Acid Bacteria, Immune function, IL-12, IgE
Lactic acid bacteria (LAB) are bacteria that produce lactic acid. It is reported there are LAB thathave various functions. In Okinawa, there is variety of bioresources that have adapted to thesubtropical climate. Our laboratory made the LAB library from bioresources of Okinawa. In thislibrary, both Enterococcus faecalis had an anti -allergic function and one that didn’t have it was
found. In this study, we evaluated and compared immune stimulating activity and anti -allergic
function of the two Enterococcus faecalis strains, named No.5 and No.6. In addition, the causes inthe difference function are investigated by genome sequencing and genome analysis. As a result,
No.5 strain increased the amount of Il-12b mRNA expression as index of immune stimulating
activity and decreased IgE which is the cause material of allergy. No.6 strain didn’t have thesefunctions. We conducted genome sequencing and compared CDS of these strains, No.5 strain only
had CRISPR related genes that work as immune system against phage. Enterococcus faecalis isreported that its derived extracts are effective for hay fever sufferers. Therefore, we concluded that
No.5 strain had this function because the defensive system should work against phage thatnegatively affected the function of LAB.
[1] Aziz, RK.,et al., The RAST Server: Rapid Annotations using Subsystems Technology. , BMC
Genomics, 2008.
[2] Overbeek, R.,et al., The SEED and the Rapid Annotation of microbial genomes using
Subsystems Technology(RAST)., Nucleic Acids Res., 2014.
[3] Brettin, T.,et al., RASTtk: A modular and extensible implementation of the RA ST algorithm forbuilding custom annotation pipelines and annotating batches of genomes. , Sci Rep., 2015.
[4] R. Core Team, R: A language and environment for statistical computing. , R Foundation for
Statistical Computing, 2015.
[5] R.Barrangou,et al., CRISPR provides acquired resistance against viruses in prokaryotes. , Science,
315:1709-1712, 2007.
[6] Shida. K, Makino. K, et al., Lactobacillus casei inhibits antigen -induced IgE secretion throughregulation of cytokine production in murine splenocyte cultures, Int Arch Allergy Immunol,
278-287, 1998

P3-11
Using gene expression profiles to identify the underlying
mechanism of adverse drug events

1

Attayeb Mohsen

Kenji Mizuguchi

Attayeb@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: Osteonecrosis, Gene expression profiling, Machine learning, adverse drug reactions
Understanding of the effects of adverse drug events (ADE) on gene expression can help inunderstanding the underlying mechanism of ADE; it can also be utilized for prediction of these
events in newly discovered drugs. In this study, we aimed to explore the underlying mechanism ofadverse drug events using bioinformatics’ tools. We used four publicly available databases, The
Japanese Toxicogenomics Project (TGP) database (Open TG-GATEs[1]), curated and standardized
FAERS (FDA’s Adverse Event Reporting System (FAERS[2]), SIDER[3], and Medical dictionary
for regulatory activities (MedDRA®[4]).
We included in our analysis only those compounds for which Open TG-GATEs provided gene
expression profiles. We identified drugs that had a statistically significant association with adverse
events reports in FAERS using Fisher’s exact test (p < 0.001). The other compounds in open TGGATEs were considered controls. We obtained gene expression profiles for these compounds fromOpen TG-GATEs. TGP measured gene expression across different time intervals and dose levels.
We applied machine-learning methods, and enrichment analyses to find out the pathways affectedby these drugs, and estimate the underlying mechanism of these ADE.

[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(Databaseissue):D921-D927, 2015.
[2] Banda, J. M., Evans, L., Vanguri, R. S., Tatonetti, N. P., Ryan, P. B., and Shah, N. H., A curated
and standardized adverse drug event resource to accelerate drug safety research. Scientific Data,
3. http://doi.org/10.1038/sdata.2016.26, 2016.
[3] Kuhn, M., Letunic, I., Jensen, L. J., and Bork, P., The SIDER database of drugs and side effects.
Nucleic Acids Research, 44(D1), D1075–D1079, 2016.
[4] Brown, E.G., Wood, L., and Wood, S., The Medical Dictionary for Regulatory Activities(MedDRA). Drug-Safety, 20:109, 1999
[5] Friedman, J., Hastie, T., and Tibshirani, R., Regularization Paths for Generalized Linear Models
via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22, 2010.

P3-12Garuda Platform –Connecting Analytics for Open Innovation
Yukiko Matsuoka 1,2
myukiko@sbx-corp.com

1
2

Ayako Yachie 1,2yachie@sbi.jp

Nikolaos Tsorman 1,2
tsorman@sbx-corp.com

Vipul Gupta 1,2
vgupta@sbi.jp

Takeshi Hase 1,2Samik Ghosh 1,2

Hiroaki Kitano 1,2

thase@sbi.jp

ghosh@sbx-corp.com

kitano@sbi.jp

The Systems Biology Institute, 5-6-9 Shirokanedai, Minato-ku, Tokyo 108-0071, JapanSBX Corporation, 5-6-9 Shirokanedai, Minato-ku, Tokyo 108-0071, Japan

Keywords: Computational analytics platform, open innovation, integrated data analytics pipeline,
privacy preserving queries
To understand the disease states and pathogenesis at the molecular level, or to achieve a
meticulous patient stratification leading to drug discovery, there is a need to understand the
dynamic behavior of each layer of the living systems by examining a large set of high dimension
data. With recent advancements in data collection techniques for various domains, data analysis
methods have been steadily diversified and rapidly expanding including the introduction of AI andmachine learning. This becomes an urgent challenge for researchers worldwide, requiring
immediate need for a platform that enables easy access to up-to-date and diverse analysis
workflow and can apply to systematic analysis both in life science and clinical research. Further,
the complexity and multi-dimensionality of data and analytics make it extremely challenging todevelop a single, stand-alone system that can integrate a broad range of dataset and analysis
techniques. Instead, a platform that connects the diverse components through open interfaces, andallows the community to discover and navigate through them, can provide a unique collaborative
eco-system.
One of the efforts to build such platform is Garuda, which is a community-driven open platformthat provides a framework to connect, discover and navigate through different analytics
applications, databases and information services in biology and medicine. Garuda provides
programming language independent APIs to connect software/tools as gadgets, explore themthrough a gateway and operate them through the dashboard, supported by a global alliance ofleaders in computational biology and informatics. Now, Garuda is extending its wings to data
security. In the poster, we would illustrate some potential user scenarios for privacy preserving
queries over clinical data or in-development compound structure information, which would serve
as a gateway to the life science/drug discovery workflow.

[1] Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K.-Y. & Kitano, H. Software for systems biology:
from tools to integrated platforms. Nat. Rev. Genet. 12, 821–832, 2011.
[2] Kitano, H. Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for
Scientific Discovery. AI Mag. 37, 39–49, 2016.
[3] http://www.garuda-alliance.org

P3-13
Genetic analysis of mitochondrial disorder
Masakazu Kohda 1 mkoda@saitama-med.ac.jp
Kei Murayama 2 kmuraya@mri.biglobe.ne.jp
Akira Ohtake 3 akira_oh@saitama-med.ac.jp
Yasushi Okazaki 1,4 okazaki@saitama-med.ac.jp
1

Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical
University
2

Department of Metabolism, Chiba Children's Hospital3

Department of Pediatrics, Saitama Medical University

4

Division of Functional Genomics & Systems Medicine, Research Center for Genomic Medicine,
Saitama Medical University

Keywords: Mendelian genetic disorders, Metablic disorders, Mitochondria

Mitochondrial disorder has the highest incidence among congenital metabolic disorders
characterized by biochemical respiratory chain complex deficiencies. It occurs at a rate of 1 in
5,000 births, and has phenotypic and genetic heterogeneity. More than 250 genes that cause
mitochondrial disorder have been reported to date. However exact genetic diagnosis for patientsstill remained largely unknown. Recently, we published genomic analyses for 142 juvenile patientswith mitochondrial respiratory chain complex deficiencies. We identified 37 novel mutations in
known mitochondrial disease genes and 3 mitochondria-related genes (MRPS23, QRSL1, andPNPLA4) as novel causative genes, and also achieved genetic diagnoses in 49 of 142 patients(34.5%). The high proportion of novel mutations indicates the different ethnicity in Japanese
population compared with those so far reported mainly by studies on Caucasian patients. Genetic
causes for a large number of cases are still unknown. Therefore, to further clarify these unsolved
cases, we are currently focusing on the analysis of exonic deletions as well as
SNVs/INDELs/CNVs. By increasing the number of cases analyzed by exome sequencing, we will
present our latest results and discuss about the contribution of these variants for the pathogenesisof mitochondrial disorder.
[1] Kohda, M. et al., A Comprehensive Genomic Analysis Reveals the Genetic Landscape ofMitochondrial Respiratory Chain Complex Deficiencies., PLOS Genet. 2016 Jan
7;12(1):e1005679.

P3-14
A novel ChIP-Seq spike-in strategy enables detection of global histone
modification changes across the genome
Masato Yonezawa1, Brian Egan1, Madeleine Craske1, Paul Labhart1, Chisato Henry1, Terry
Kelly1, Gulfem Guler2, Chih-Chi Yuan3, Patrick Trojer3, and Marie Classon2
1 Active Motif, Inc., Carlsbad, CA USA, 2 Genentech, Inc., South San Francisco, CA USA, 3 Constellation

Pharmaceuticals, Cambridge, MA USA

Abstract:
Chromatin immunoprecipitation and DNA sequencing (ChIP-seq) has been instrumental inunderstanding genome-wide distribution of chromatin proteins and their modifications (e.g.,histone modifications). However, current standard ChIP-seq methodologies do not allow
quantitative analyses and thus make it difficult to directly compare ChIP-Seq data among
different samples (e.g., disease vs normal or treated vs untreated). To overcome this issue,
we have developed a universal chromatin spike-in strategy that introduces Drosophilamelanogaster chromatin and a D. melanogaster-specific antibody into standard ChIP
reactions with human chromatin samples. To prove the principle of this concept, we
performed ChIP-Seq of human cell samples treated with DMSO or small molecule inhibitorsof the methyltransferase EZH2. The Drosophila ChIP-Seq reads were used to normalize
human ChIP-Seq data. The spike-in strategy allowed detection of dramatic and global
reduction of histone H3 lysine 27 trimethylation (H3K27me3) in inhibitor-treated samples,
which was invisible with standard analysis methods. Thus, our ChIP-Seq spike-in strategy
proves useful to compare ChIP-Seq data among different samples and to understand theeffect of drug treatments.

P3-15Development of the Protein Motif Extraction System
based on an Artificial Bee Colony Algorithm

1

Takashi Yamamoto 1

Hiroaki Kato 1

yamagen @ mbi.cs.tut.ac.jp

kato @ cs.tut.ac.jp

Department of Computer Science and Engineering, Toyohashi University of Technology,
1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi 441-8580 Japan

Keywords: protein motif, motif extraction, artificial bee colony, swarm intelligence, motif length,zinc-finger
It is well known that the amino acid sequence of a protein is closely related to its structure andfunction. This is especially true for particular structural features called motifs, and they are
considered to be well-reserved sites in the genomic sequences. Some sequence motifs contains
several gaps which corresponded to insertions or deletions in the biological evolution. Since it isalso known that two or more motif sites in a particular protein sequence, the motif extraction isregarded as a multimodal problem. To extract novel motif candidates from unaligned protein
sequences, the authors have employed an artificial bee colony (ABC) algorithm which has highsearch ability of multivariable functions.
ABC is an optimization algorithm based on the intelligence behavior of honey bee swarm, whichcontains three types of bees: employed bees, onlookers and scouts [1]. The position of a foodsource represents a possible solution of the problem, i.e., a set of the peptide fragment that
corresponded to the potential motif site for given protein sequences. A peptide fragment is shownusing the starting and terminating residues in the sequence, so that a solution is described a
2N-dimensional vector. Here, N denotes the number of given protein sequence. The nectar
amount of a food source corresponds to the quality of the associated solution. In this case, anapproximated multiple alignment score (SP score) for a set of peptide fragments is used for thefitness function that is a measurement for the motif probability. The range of motif length isspecified by the user for flexible motif extraction. At first, the ABC generates a randomly
distributed initial population of solutions. An employee bee modified specific fragment on asolution for finding a new solution. An onlooker bee chooses a solution depending on theprobability value, and tries to improve the solution using selected index. If a position cannot be
improved further through a predefined limit number of cycles, a scout replaces new food source byrandom selection. Our system iterated these process to extract novel motif candidates.
The motif extract trials were carried out using the DNA binding proteins which contained five
entries taken from the UniProt database. The range of fragment length was set to 21-24.
BLOSUM-62 substitution matrix was employed for alignment, and gap penalty was set to -1. As
the result, our system successfully identified the known zinc-finger C2H2 related motif sites [2].
In addition, several novel motif candidates were also extracted for each solutions. These results
show the potential applicability of the present approach for sequence feature analysis of proteins.
References:[1] Karaboga, D., Basturk, B., A powerful and efficient algorithm for numerical function
optimization: artificial bee colony (ABC) algorithm, J. Glob. Optim, 39, 459–471 (2007).
[2] Laity, J.H., Lee, B.M., Wright, P.E., Zinc finger proteins: new insights into structural andfunctional diversity, Curr. Opin. Struct. Biol., 11, 39-46 (2001).

P3-16
Clustering of Flaviviridae based on RNA sequencesusing Deep Learning
Shuya Yamamoto 1
s.yamamoto@bpe.es.osaka-u.ac.jp

Satoshi Asatani 2

Seiichi Tagawa 1

s.asatani@bpe.es.osaka-u.ac.jp tagawa@bpe.es.osaka-u.ac.jp

Hirohiko Niioka 1
niioka@bpe.es.osaka-u.ac.jp

Jun Miyake 1
jun_miyake@bpe.es.osaka-u.ac.jp

1

Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka,
Osaka, 560-8531, Japan
2
School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka,
560-8531, Japan

Keywords: Deep Learning, Classification, Flaviviridae
Infections caused by Flaviviridae are virulently poisonous and the diseases are often threats topeople. Many of Flaviviridae can often spread widely becaus e they are spread by mosquitoes [1]. If
we construct a method of treatment after the confirmation of infection to people, the treatments are
not completed in time and virus spreads over many people and areas. It is necessary to grasp thecharacteristic of prevailing virus in order to understand what types of virus are spread. Accurateclassification is needed to analyze the characteristic of virus scientifically. However, the shape ofvirus is similar to the homologous ones by observing through electron microscopes or considering
the genes. In this research, we used one of machine learning, Deep Learning. Because Deep
Learning is good at feature extraction [2], we applied it to analyzing virus. Stacked Autoencoder,
one of Deep Learning, can compress the dimension of inputted data with little information loss [3].
We compressed the dimension of the information of the base sequences of the viruses until it
becomes two by applying this technology and evaluated it by plotting the results on a twodimensional plane. When we inputted the sequences of six kinds of viruses (West Nile virus,
dengue virus, Zika virus, Japanese encephalitis virus, yellow fever virus, Hepatitis C virus),clusters for each virus were generated. Also, the result suggests that the differences of species are
expressed as distances between the clusters. In addition, when we inputted only the data of dengue
virus, it was classified into four clusters depending on four kinds of models of the virus. This
result shows that Deep Learning can classify things in detailed way. We would like to apply thistechnique to examine how viruses evolve.
[1] T. Ishikawa and E. Konishi, “ フ ラ ビ ウ イ ル ス ”, Journal of Japanese society for virology, Vol. 61,
221-238,2011.
[2] Krizhevsky A., Ilya S., and Geoffrey E. H., "Imagenet classification with deep convolutional
neural networks", Advances in neural information processing systems, Vol. 25, 1106-1114, 2012.
[3] Yoshua B., Pascal L., Dan P., and Hugo L., "Greedy layer-wise training of deep networks",
Advances in neural information processing systems , Vol. 19, 153-160, 2007.

P3-17
Deep Learning and Human Recognition of Biomolecules
for Drug Design and Screening
Jun Miyake1 Masato Shimabayashi1 Shuya Yamamoto1, Yuhei Kaneshita1,
jun_miyake@bpe.es.osaka-u.ac.jp, m.shimabayashi@bpe.es.osaka-u.ac.jp,s.yamamoto@bpe.es.osaka-u.ac.jp

Satoshi Asatani1s.asatani@bpe.es.osaka-u.ac.jp

Seiichi Tagawa1

Hirohiko Niioka1

tagawa@bpe.es.osaka-u.ac.jp

niioka@bpe.es.osaka-u.ac.jp

1Graduate School of Engineering Science, Osaka University, 1-3

Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan

Keywords: Deep Learning, Autoencorder, Mitochondrial DNA, Virus, Antibody
In drug discovery research, methods are required to link biological properties and the compounds.
Deep Learning (DL) potentially has an overall understanding ability of such relations.
1) Structural analysis of the compound having the sequence as protein
We have been analyzing influenza virus, flavivirus, human mitochondrial DNA and human antibodies.
The major concern is the meaning of the entire sequence. In the case of mtDNA, haplogroups arecommonly identified by SNPs, very small parts of DNA sequences. The Deep Lerning (Autoencoder)analysis of mtDNA gave a specific distribution corresponding to the haplogroup. The result indicatesthe positive relation of entire sequence and the haplogroup. However, they are not identically same[1].
Flavivirus group virus gene have been analyzed to identify the viruses. A clear separation forming theclusters of the virus species [2].
Influenza virus has been examined to elucidate the year-by-year and pandemic change of the gene.
Entire sequence analysis was effective to classify the virus.
Variable regions (100 amino acid residues , 5932 lines) of the H chain of a human antibody wereanalyzed in a similar manner as formed for each particular antigen (virus). The positions of theclusters were sensitive to the change of the amino acids.
2) Cognition made by Autoencoder
Cluster formation of the haplogroup, species and the antibodies are commonly found by the sequence
analysis. As far the virus classification, human naming and the classification by Autoencorder arealmost identical. Human mtDNA analysis by Autoencorder gave specific characteristics as the
haplogroup.
The comparison gives a conclusion that the human cognition and Deep Learning cognition are
substantially same. Application of Deep Learning to the biological molecules could be possible for
medical and pharmaceutical uses.
[1] Masato Shimabayashi et al in this symposium.
[2] Shuya Yamamoto et al in this symposium.

P3-18Analysis of mitochondrial DNA based on whole sequence
using Deep Learning
Masato Shimabayashi 1

Satoshi Asatani 1

m.shimabayashi@bpe.es.osaka-u.ac.jp

s.asatani@bpe.es.osaka-u.ac.jp

Seiichi Tagawa 1
tagawa@bpe.es.osaka-u.ac.jp

1

Hirohiko Niioka 1
niioka@bpe.es.osaka-u.ac.jp

Jun Miyake 1
jun_miyake@bpe.es.osaka-u.ac.jp

Graduate School of Engineering Science, Osaka University, 1-3
Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan

Keywords: Deep Learning, mitochondrial DNA, classification
Human haplogroups has been studied by using indexes as blood type, virus infection,
Y-chromosome. Mitochondrial DNA (mtDNA) is one of the most popular substance for the
analysis. It is regarded as a key factor to study human evolution for three reasons. First, mtDNA
gives a magnified view of the diversity present in the human gene pool, because mutations
accumulate in this DNA several times faster than in the nucleus. Second, because mtDNA isinherited maternally and does not recombine, it is a tool for relating individuals to one another.
Third, mtDNA molecules are abundance within a typical human [1].
In general, an approach for differentiation of identical mtDNA haplogroups is the analysis ofsingle nucleotide polymorphisms (SNPs). In this research, we focused on whole sequence
mtDNA-based classification with Deep Learning.
A mechanical learning method “Deep Learning” opened a promising way for artificial
intelligence. Deep Learning has dramatically improved the state-of-the-art in speech recognition,
visual object recognition, object detection and many oth er fields such as drug discovery andgenomics [2].
We used Deep Learning to classify whole sequence mtDNA. We plotted the results compressed bydeep auto encoder in two dimensions (The number of bas es: about 16570, The number of data:396).
The dimensional compression results suggest that mtDNA sequences should be classified in somegroups. The meanings of the spatial dispersion and the correlation distances are discussed withreference to the divergence of human haplogroups. We think that the relative relationshi p of
haplogroups from mtDNA data can be expressed using deep auto encoder.
We classified human haplogroups by deep convolutional neural network.
We evaluated this approach with six haplogroups dataset (The number ofbases:16581, The number of training data:360, The number of test
data:90) and achieved over 90% accuracy. We also found thatclassification may not be based only on existing haplogroup definition.
The main conclusion of our study is that whole sequence mtDNA-basedclassification with Deep Learning seems to be a new index to study
Fig1: An illustration
human evolution.
of deep auto encoder.
[Acknowledgements] I would like to thank Yuhei Kaneshita for his supporting this work.
[1] Rebecca L. Cann, Mark Stoneking and Allan C. Wilson . “Mitochondrial DNA and human
evolution.” Nature 325 (1987): 1-5.
[2] Yann LeCun, Yoshua Bengio and Geoffrey Hinton. “Deep learning,” Nature 521, (2015):
436–444.

P3-19
VaProS: A Data Cloud Approach for Integration of Protein
and Genome Information
Takashi Gojobori 1,2
Akira R. Kinjo 4
Hisashi Mizutani 2

Kazuho Ikeo 2

Yukie Katayama 3

Kengo Kinoshita 5,6,7
Masafumi Muraoka 2

Hideaki Sugawara 2

Takeshi Kawabata 4

Yeondae Kwon 3

Ohsuke Migita 7,8

Koji Nagata 3

Satoshi Omori 5

Daichi Yamada 9

Kei Yura 2,9yura.kei@ocha.ac.jp

1.

King Abdullah University of Science and Technology, Computational Bioscience ResearchCenter, Biological and Environmental Sciences and Engineering, Thuwal 23955-6900,
Kingdom of Saudi Arabia
2.
National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan
3.
Graduate School of Agricultural and Life Sciences, University of Tokyo, Bunkyo, Tokyo
113-8657, Japan
4.
Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
5.
Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi 980-8597, Japan
6.
Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi 980-8573, Japan
7.
Department of Maternal-Fetal Biology, National Research Institute for Child Health andDevelopment, Setagaya, Tokyo 157-8535, Japan
8.
Department of Pediatrics, St. Marianna University School of Medicine, Miyamae, Kawasaki
216-8511, Japan
9.
Center for Informational Biology, Ochanomizu University, Otsuka, Bunkyo, Tokyo 112-8610,
Japan

Keywords: Big data analysis, Lysosomal storage disease, Protein 3D structure
Current life science research depends heavily on all sorts of omics data, such as genome
sequences, transcriptome, protein-protein interactome, protein three-dimensional (3D) structurome,phenome and others. The knowledge compiled by all the omics research is so huge that acomputer-aided search of databases is now a starting point for a new study. Especially, acombinatorial search of multiple databases has a big chance to retrieve new idea and new
hypotheses that can be tested by wet-lab experiments. We therefore tried to launch an applicationthat virtually integrates the related databases on the Internet. Specifically, we have built a new web
application that facilitates life science researchers for retrieving experts’ knowledge stored in the
databases and for building new hypothesis for their research targets. The web application is named
VaProS and puts stress on the interconnection between the functional information of genome
sequence and protein 3D structurome, such as structural effect of gene mutation. In this poster
presentation, we will demonstrate the overview of VaProS, and the databases and tools that can beaccessed without any knowledge of database locations and data formats. We will further
demonstrate the power of search on VaProS using lysosomal storage disease as an example for theapplication. VaProS is freely available at http://p4d-info.nig.ac.jp/vapros/.

P4-01
Evaluation of drug-likeness on bioavailability
by in vitro screening
Yukinori Kawai, Masaru Iwasaki, Kyosuke Suzuki, Hideo Kobayashi
Pharmaceutical and Biomedical Analysis Department, DAIICHI SANKYO RD NOVARE CO., LTD.,
1-16-13, Kitakasai, Edogawa-ku, Tokyo 134-8630, Japan

Keywords: Drug-likeness, Bioavailability, Hybrid parameter
Analysis of launched drug by in vitro ADME/physicochemical properties screening (in vitro
screening) is important to improve drug-likeness in early drug discovery stage. Althoughbioavailability (BA) is a useful parameter which represents the degree of oral absorption inpharmacokinetic profile, details of the relationship between BA and in vitro screening data are notsufficiently clear. In order to obtain a new parameter reflecting drug-likeness focused on BA, analysisof launched drugs was investigated by the use of in vitro screening dataset which measured fourparameters of solubility, permeability, metabolic stability and protein binding.
We could obtained BA potential score (BAP score) as a new parameter. BAP score were calculated
by absorption and hepatic availability potentials that expressed as hybrid parameters of in vitro
screening. Drugs with high BAP score exhibit higher BA potential. Forty-eight percent of launcheddrugs existed in area above 3 of BAP score. As shown in Figure 1, BA potential map (BAP map) is
valuable to better understand the relative positioning between launched drugs and in-house compounds,
and the balance of absorption and hepatic availability potential.
Finally, it was considered that BAP score and BAP map could visually show the direction of drug
design, and that this analysis was a practical and useful tool in effective improvement of drug-likeness.

Hepatic availability potential

1.0
0.8
0.6
0.4
0.2
0.0
0.1

10

1000

100000Absorption potential
Figure 1 BA potential map based on absorption and hepatic availability potentials●: launched drug, ○: in-house compound
Solid line: BAP score = 3.0, dashed line: BAP score = 2.4.

P4-02
Proposal of in silico mechanism-based methodology forprediction of toxicity
Yu Haranosono 1 Shingo Nemoto 1 Masaaki Kurata 1 Hideyuki Sakaki 1
y-haranosono@senju.co.jp
1shingo@senju.co.jp

m-kurata@senju.co.jp

hideyuki-sakaki@senju.co.jp

Senju Pharmaceutical Co. Ltd., 1-5-4 Murotani, Nishi-ku, Kobe, Hyogo 654-0103, Japan

Keywords: Structure-Property-Activity-Relationship, toxicity prediction, mechanism-based
In silico toxicity prediction becomes to be of importance in drug discovery and development.
Especially, quantitative-structure-activity-relationship (QSAR) currently attracts attention because
the ICH M7 guideline was issued. QSAR predicts toxicity with some structural alerts which are
related to the toxicity outcome in experience or in statistics. In many cases, the relationshipbetween the mechanism of toxicity and the structural alerts is uncertain. On the other hands, newstatistical methods, the random forest and the deep learning, have been used to predict sometoxicities. These methods have a relationship with the mechanism of toxicity by using
physico-chemical-properties and experimental data. However, these methods are complicatedsystem to discuss their results for common toxicologists. Thereby, toxicologists desire new insilico methodology for toxicity prediction based on the mechanism of toxicity with simple
procedure.
We have been developing a new methodology for toxicity prediction to call positive or negative,
which is based on the mechanism of toxicity. The methodology was consistent with 5 steps asfollows: 1) translate the mechanism of toxicity to some physic-chemical-properties (descriptors);
2) put the criteria of prediction power (sensitivity, specificity, concordance); 3) set the cut-offlines; 4) repeat the 3 rd step for each descriptor; 5) combine descriptors to call one result for one
compound. Among these steps, the 1 st and 3 rd steps are important to achieve good prediction power.
Especially, the 3 rd step is the key-point of our method. In many case, there is a “gray zone” whichis co-existed of positive and negative compound at similar rate on 2D plot of descriptor andtoxicity. The discriminatory analysis is unsuitable at the above situation because of existing many
false predictions. Therefore, we used the decision tree-like method to set cut-off lines and to define“gray zone”. By using our methodology, we achieved to establish new toxicity prediction methods
for phototoxicity and phospholipidosis [1], [2]. Each new prediction method shows highconcordance (>80%) with 2 or 3 descriptors. In conclusion, our methodology proposal here isconsidered to be useful in establishing new toxicity prediction methods for themechanism-well-known toxicity.
[1] Haranosono, Y., Kurata, M., Sakaki, H. Establishment of an in silico phototoxicity prediction
method by combining descriptors related to photo-absorption and photo-reaction. The Journalof Toxicological Science, 39:655-664, 2014.
[2] Haranosono, Y., Nemoto, S., Kurata, M., Sakaki, H. Establishment of an in silico
phospholipidosis prediction method using descriptors related to molecular interactions causing
phospholipid-compound complex formation. The Journal of Toxicological Science,
41:321-328.

P4-03
Prediction of chemical-induced developmental andreproductive toxicity in human using the machine learning
Koki Matsuoka 1
c401631029@tokushima-u.ac.jp

A. Ammar Ghaibeh 2
ammargh@acm.org

Shiro Omura 1c401203002@tokushima-u.ac.jp

Youichi Sato 1

Hiroki Moriguchi 2

Aiko Yamauchi 1

youichi.sato@tokushima-u.ac.jp

h_moriguchi@ap6.mopera.ne.jp

yamauchi.aiko@tokushima-u.ac.jp

12

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, Developmental and reproductive toxicity, Decision tree, SVM
Introduction: The developmental and reproductive toxicity (DART) in humans is one of the
crucial adverse reactions. However , because pregnant women are largely excluded from clinicalresearch the data available for research is limited. The toxicity has too complex and unknown
mechanisms to be empirically predicted, and the results of the time consuming andanimal-intensive studies are not always extrapolated to humans. In this study, we try to predict thehumans’ DART from the physical and chemical properties of drug molecules using two machinelearning methods, decision trees and support vector machine (SVM).
Methods：The DART in humans is classified according to FDA pregnancy risk categories (A, B, C,
D or X) which indicate the potential of a drug to cause birth defects if used during pregnancy. In
our work compounds were defined as “non-toxic” if they are categorized as A or B, “unknown”(n=227) if they are categorized as C, and “toxic” (n=162) if they are categorized as D or X. The insilico model for human DART was built on the data set of compounds with 39 atom-type
descriptors and 4 molecular properties (molecular weight (MW), pKa, topological polar surface
area and log P). RapidMiner Studio 7.0 was used for the generation of decision tree and SVM, with
under-sampling methods: Nearmiss-1 (NM1), Nearmiss-2 (NM2), Most distant [1], and Random
sampling. Forward features selection and backward elimination were used with the SVM model.
The performance of generated model was evaluated using 10 -fold cross validation method.
Results and discussion: In order to avoid models with high false-negative, model sensitivity wasconsidered in addition to its accuracy. The best performance model of decision tree was obtainedwith NM2, yielding a mean (±SD) accuracy, sensitivity and specificity of 69±6%, 84±10% and58±7%, respectively. The resulted decision tree was simple enough to assess the DART of“unknown” compounds in a short time. The tree showed that DART is associated with S3 (sp3sulfur in thioether), MW and N3 (sp3 nitrogen). SVM with NM2 and backward elimination has
also produced a good prediction yielding a mean (±SD) accuracy, sensitivity and specificity of
76±7%, 83±11% and 70±7%, respectively. The DART of “unknown” compounds was also
evaluated by the SVM and compared with the results of decision tree.
Conclusion: Present results showed that data mining algorithms such as decision tree or SVM canbe useful in predicting the human DART in silico not only before screening of new chemical
entities but also at the selection of safer therapeutics for pregnant women.
[1] Yen, S.-J., & Lee, Y.-S. (2009). Cluster-based under-sampling approaches for imbalanced data
distributions. Expert Systems with Applications, 36:5718–5727, 2009.

P4-04
Informatic assessments of global gene expression dataobtained from the analysis of hepatic stellate cell
deactivation process
Seiichi Ishidaishida@nihs.go.jp

Shinichiro Horiuchi
Su-Ryang Kim

Yukie Kuroda

Shoko Uchida

Yuko Sekino

Division of Pharmacology, National Institute of Health Science s,
1-18-1 Kamiyoga, Setagaya-ku Tokyo 1588501, Japan

Keywords: hepatic stellate cell, hepatic fibrosis, GneChip, informatic assessment【Introduction】Hepatic stellate cells (HSCs) are pericytes presenting in a small area between thesinusoids and hepatocytes, and storing vitamin A as quiescent state in normal liver. When liver isdamaged, HSCs are activated and playing a key role in hepatic fibrogenesis by secreting collagen.
HSCs cultured on a normal culture plate usually changes into the activated state. We found thatwhen the HSCs were cultured on VECELL membrane, activated HSCs turned into deactivated state.
In this research, gene expression pattern of HSCs cultured on VECELL membrane was comparedto that cultured on normal culture plate or on matrigel coated plate, and the effect on HSCs
deactivation was evaluated.
【Methods】The data used in this study were obtained from the following experiments. Hep aticstellate cell line LI90 was cultured on VECELL, on matrigel coated plate or on normal culture
plate. Total mRNA was isolated from cells cultured for four days or for seven days. Gene
expression profiles were determined by the human genome U133A Gene Chip DNA Microarray.
Gene expression data were filtered according to the following criteria: (i) genes with a signal
above 500, (ii) genes with more than 2.0 fold change, (iii) genes with p -values less than 0.05.
Statistical analysis of the expression data was performed using ANOVA. The genes remaining after
filtering were analyzed using MetaboAnalyst3.0 [1], DAVID [2] and DiRE [3].
【Results】There were 203 genes selected in common with four days and seven days of culture.
Gene expressions in VECELL culture and in matrigel culture were compared to those in normal
plate culture. The partial least squares-discriminant analysis (PLS-DA) was performed on thesegenes by MetaboAnalyst3.0. VECELL culture and matrigel culture formed a cluster separated fromnormal plate culture. When the gene list was analyzed by DAVID on the base of gene ontology
function annotation, the gene groups related to HSCs activation (TGFβ pathway, p53 pathway,
immune response and extracellular matrix) were found to be enrich ed. DiRE analysis showed that
SRF, NFκB and HMEF2 were associated with down-regulated genes in VECELL culture or in
matrigel culture. These transcription factors are reported to be related to HSCs activation.
【Conclusion】 In this research, gene expressions among three culture conditions were comparedand those changed both in VECELL culture and in matrigel culture in common were identified.
Furthermore, informatic analysis suggested pathways, functions and transcription factors relating
to HSCs deactivation were regulated in VECELL culture and matrigel culture.
【References】[1] http://www.metaboanalyst.ca/MetaboAnalyst/[2] https://david-d.ncifcrf.gov/[3] http://dire.dcode.org/

P4-05Prediction for chemical-induced hepatomegaly andcomputational analysis of feature motifs
Kaori Ambe

Kana Ishihara

ambek@phar.nagoya-cu.ac.jp

c112010@ed.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: Hepatomegaly, Machine learning, Molecular features, QSAR
Hepatomegaly is often observed in the repeated-dose toxicity studies of chemicals [1], andfrequently affects the derivation of the No Observed Adverse Effect Level (NOAEL). So, we are
expected to understand and predict hepatomegaly in toxicological research.
In this study, we have developed the prediction Quantitative Structure Active Relationship(QSAR) model for the hepatomegaly from chemical substance’s descriptors by machine learning
methods known as deep learning, support vector machine and random forest. Our original data sets
were composed of hepatomegaly-positive or hepatomegaly-negative compounds based on the risk
assessment reports of pesticides, food additives, and veterinary medicine products that were
published by Food Safety Commission of Japan [2]. These our prediction models had the nearly
80% prediction accuracy.
However, it is difficult to understand their contents in machine learning methods [3]. In order tounderstand what molecular features are important for hepatomegaly, we analyzed about molecular
descriptors by Dragon, the application for the calculation of molecular descriptors [4]. Then, we
calculated 154 functional groups to examine characteristic partial structure of hepatomegaly.
Results showed that Pyrazoles, halogen on aromatic ring and secondly amides are characteristic ofhepatomegaly-positive compounds. It might be possible to distinguish hepatomegaly by thesestructural features combination. For the screening of chemicals, these results are expected to be
useful.
[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/hyouka/[3] C. M. Bishop., Pattern recognition and machine learning, Springer New York, 2006.
[4] http://talete.mi.it/index.htm

P4-06The importance of the applicability domain in prediction
QSAR model for chemical-induced hepatomegaly
Kana Ishihara

Kaori Ambe

c112010@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, Hepatomegaly, Machine learning methods, QSAR
It is important to predict hepatomegaly, which is often observed in repeated-dose toxicologicalstudies, in the toxicity assessment [1]. The in silico predicting methods are expected to be used as
the alternative to animal testing from the point of not only 3Rs in animal study but also the efficient
toxicity study. So, we have developed hepatomegaly prediction Quantitative Structure Active
Relationship (QSAR) model based on the structure of the chemicals.
In this study, we used two databases. One is our original toxicological database from the risk
assessment reports of pesticides, food additives, and veterinary medical products that were
published by Food Safety Commission of Japan [2]. The other is Hazard Evaluation Support System
Integrated Platform (HESS-DB) [3] by National Institute of Technology and Evaluation. First, we
extracted rat repeated-dose toxicological data from these databases. Then, chemical substance’s
descriptors were calculated and selected using Dragon 6.0 software [4]. Finally, we constructed
QSAR model for hepatomegaly prediction by machine learning methods.
The applicability domain (AD) is important to construct a reliable QSAR model, which generally
limits the application for chemicals structurally similar to the training compounds [5]. Therefore,
the definition of the AD is necessary to improve the performance of the prediction model. Then, we
defined the AD based on data density [6]. As a result, we could confirm the validity of the AD
because the accuracy of predictions and the area under the curve (AUC) were increased.
[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/hyouka/[3] http://www.nite.go.jp/chem/qsar/hess.html[4] H. Kaneko., K. Funatsu., Applicability Domain Based on Ensemble Learning in Classification
and Regression Analyses, J. Chem. Inf. Model., 54:2469-2482, 2014.
[5] OECD, ENV/JM/MONO, 2, 2007.
[6] D. Horvath., G. Marcou., A. Varnek., Predicting the predictability: a unified approach to theapplicability domain problem of QSAR models, J. Chem. Inf. Model., 49:1762-1776, 2009.

P4-07
Estimation of Mechanism-of-Action of Pharmaceutical
Compounds Based on Systems Biology Approach
Ryuta Saito 1,2
Saitou.Ryuuta@mc.mt-pharma.co.jp

Masahiro Okamoto 2
okahon@brs.kyushu-u.ac.jp

12

Advanced Drug Research Laboratories, Mitsubishi Tanabe Pharma C orporation, 2-2-50,
Kawagishi, Toda-shi, Saitama 335-8505, Japan.
Graduate School of Bioresource and Bioenvironmental Sciences , Kyushu University, 3-1-1,
Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

Keywords: Modeling and Simulation, Metabolic profiling, Endocrine disruptors
Systems biology provides a framework for constructing mathematical models of biological andphysiological systems from systematic measurements of multiple molecular levels. Recently
systems biology is in an exponential development stage and has been widely used in drug
discovery and development to deeply understand molecular basis of disease and pharmacologicalaction. However, most of these conventional researches have been based on the exploratory
approach, and comprehensive technologies for quantitative estimation of mechanism of drug action
based on differentially omics analysis, such as metabolic profiling, are not widely reported. We
have proposed useful computational approach for elucidation of mechanism of adrenal action of
endocrine-disrupting compounds based on differentially metabolic profiling as an application ofsystems biology for risk assessment of novel pharmaceutical compounds [1].
Adrenal toxicity is one of the major concerns in drug development. To quantitatively understandthe effect of endocrine-active compounds on adrenal steroidogenesis, and to apply to assess thehuman adrenal toxicity of novel pharmaceutical drugs, we developed a mathematical model ofadrenal steroidogenesis in human adrenocortical carcinoma N CI-H295R cells. The computationalmodel includes cellular proliferation, intracellular cholesterol translocation, diffusional transportof steroids, and metabolic pathways of adrenal steroidogenesis. It reconstructed in an experimental
dynamic patterns of cholesterol and 14 steroids from an in vitro steroidogenesis assay using
NCI-H295R cells. Results of dynamic sensitivity analysis suggested that 3β-HSD enzyme plays themost important role in the metabolic balance of adrenal steroidogenesis. Based on diffe rentiallymetabolic profiling of 12 steroid hormones and 11 adrenal toxic compounds, we could estimate
which steroidogenic enzymes were affected in this mathematical model by using a hybrid
optimization method of genetic algorithm (AREX/JGG) and local search (non-linear least squares).
In terms of adrenal steroidogenic inhibitors, the predicted action sites were approximately matchedto reported target enzymes. On the other hands, vasculotoxic agents were estimated to have noeffect according to the results obtained by our system. Thus, our computer-aided system based on asystems biological approach may be useful to understand the mechanism of action of
endocrine-active compounds and to assess the human adrenal toxicity of novel pharmaceuticaldrugs.
[1] Saito, R., Terasaki, N., Yamazaki, M., Masutomi, N., Tsutsui, N., and Okamoto M., Estimationof the Mechanism of Adrenal Action of Endocrine -Disrupting Compounds Using a
Computational Model of Adrenal Steroidogenesis in NCI -H295R Cells. Journal of Toxicology,
2016:4041827, 2016.

P4-08Intestinal absorption of citrus fruits-derived
heptamethoxflavone in rats
Shion ITO 1

16110051@cc.matsuyama-u.ac.jp 	
 Satoshi OKUYAMA 3
sokuyama@cc.matsuyama-u.ac.jp
1

2

3

Ayano KAKIUCHI 1, 2	 Yuuki KONDO 116110304@cc.matsuyama-u.ac.jp

Yoshiko FURUKAWA 3
furukawa@cc.matsuyama-u.ac.jp

Takashi MIZUMA 1
tmizuma@cc.matsuyama-u.ac.jp

Department of Pharmaceutics, College of Pharmaceutical Sciences, Matsuyama University,
4-2 Bunkyo-cho, Matsuyama, Ehime 790-8578, Japan
Present address: Department of Pharmacy, Kochi Medical School Hospital, 185-1 Kohasu,
Oko-cho, Nankoku-shi, Kochi 783-8505, Japan
Department of Pharmaceutical Pharmacology, College of Pharmaceutical Sciences,
Matsuyama University, 4-2 Bunkyo-cho, Matsuyama, Ehime 790-8578, Japan

Keywords: Intestinal absorption, Absorption clearance, Intestinal metabolism
3, 5, 6, 7, 8, 3’, 4’-heptamethoxyflavone (HMF), which is present in citrus fruits, has beenreported to induce brain-derived neurotropic factor (BDNF) production, and have ananti-inflammatory effect [1-3]. However, HMF pharmacokinetics has been unclear. Therefore, inthe present report, intestinal absorption of HMF was studied using the upper (jejunum) and lower(ileum) parts of the small intestine isolated from rats [4].
HMF appeared on the serosal side, and the absorbed amount increased with time. Absorption
clearance (CLabs) of HMF increased from 0.81 to 1.6 µL/min/cm as HMF concentration increased
from 10 to 50 µM in the jejunum, whereas in the ileum the CLabs of HMF was nearly constant tobe around 2.4 µL/min/cm. The CLabs of 10 µM HMF in the jejunum was significantly higher inthe presence of verapamil than that in its absence, whereas in the ileum the CLabs of 10 µM HMF
in the presence of verapamil was almost equal to that in its absence.
These results suggest that HMF is metabolized in the jejunum, and its metabolism impede
HMF absorption, whereas in the ileum, verapamil does not have any significant effect on the HMF
absorption under this condition.
[1] Y. Furukawa, S. Okuyama, Y. Amakura, S. Watanabe, T. Fukata, M. Nakajima, M.
Yoshimura, T. Yoshida, Isolation and characterization of activators of ERK/MAPK from citrus
plants, Int. J. Mol. Sci., 13: 1832-1845, 2012.
[2] S. Okuyama, N. Shimada, M, Kaji, M, Morita, K. Miyoshi, S. Minami, Y. Amakura, M.
Yoshimura, T. Yoshida, S. Watanabe, M. Nakajima, Y. Furukawa, Heptamethoxyflavone, acitrus flavonoid, enhances brain-derived neurotrophic factor productionand neurogenesis in the
hippocampus following cerebral global ischemia in mice. Neurosci. Lett., 528: 190-195, 2012.
[3] S. Okuyama, M. Morita, K. Miyoshi, Y. Nishigawa, M. Kaji, A. Sawamoto, T. Terugo, N.
Toyoda, N. Makihata, Y. Amakura, M. Yoshimura, M. Nakajima, Y. Furukawa Y,
3,5,6,7,8,3',4'-Heptamethoxyflavone, a citrus flavonoid, on protection against memory
impairment and neuronal cell death in a global cerebral ischemia mouse model. Neurochem. Int.,
70: 30-38, 2014.
[4] T. Mizuma, K. Ohta, S. Awazu, The β-anomeric and glucose preferences of glucose transport
carrier for intestinal active absorption of monosaccharide conjugates. Biochim. Biophys. Acta.,
1200: 117-122, 1994.

P4-09Prediction of hERG binding using a nondominated sorting
genetic algorithm and Support Vector Machine
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, genetic algorithm
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 has
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
related studies have been continuously reported [1,2]. Most of the previous studies haveundertaken the small and/or biased datasets, the predictability and/or scope of models were notsufficient for drug discovery process. To overcome this limitation, we have collected a large
dataset containing 317,232 diverse compounds (positives (<10 uM): 6,619, negatives: 310,613)
with IC50 values or % inhibition determined by the biological assay. To reduce the computation
cost, we selected the 13,238 structurally diverse compounds (ratio (positives/negatives) = 1/1)
covering the original chemical space for machine learning use. Then, support vector machine(SVM) was employed to build classification models with ECFP4 and 15 molecular descriptors
selected by nondominated sorting genetic algorithm-II (NSGA-II [3]) were used. NSGA-II is analgorithm given to solve the multi-objective optimization problems. It was used to find the Pareto
optimal solutions in which both high predictability (Kappa statistics) and the small number ofdescriptors in the SVM models are compatible. The best model showed Kappa = 0.772 and 0.791
for the 5-fold cross-validation and external test, respectively.
In this poster session, we will compare predictability of the model with those of previous studies(e.g. Pred-hERG[1]) and commercial software.
[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] Jia, L., and Sun, H., Support vector machines classification of hERG liabilities based on atom
types, Bioorg. Med. Chem., 16: 6252-6260, 2008.
[3] Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist multiobjective genetic
algorithm: NSGA-II, Evolutionary Computation, IEEE Transactions, 6: 182−197, 2002.

P4-10Characterization of cardiac phenotype and drug responses in MiraCell™ Cardiomyocytes
from ChiPSC12, a new product of iPS-derived Cardiomyocytes
Toshikazu Nishie 1
nishiet@takara-bio.co

Hiroyuki Fukushima 2
hiro.fukushima@cira.kyoto-u.ac.jp

Yasuhiro Tosaka 1
tosakay@takara-bio.co.jp

Masahide Kawato 2
masamasa@kuhp.kyoto-u.ac.jp

Jun K Yamashita 2
juny@frontier.kyoto-u.co.jp
1

2

Toshiyuki Miura 1
miuraty@takara-bio.co.jp

Tatsuji Enoki 1
enokit@takara-bio.co.jp

Junichi Mineno 1
minenoj@takara-bio.co.jp

CDM center, TAKARA BIO. INC., NOJIHIGASHI 7-4-38, KUSATSU, SHIGA
525-0058, Japan
Laboratory of Stem Cell Differentiation, Department of Cell Growth & Differentiation,
Center for iPS cell research and application (CiRA), Kyoto University, 53
KAWAHARA-CHO, SHOGOIN, SAKYO-KU, KYOTO 606-8507, Japan

Keywords: iPS cells, cardiomyocytes, drug toxicity testing, Multi -electrode array
Cardiomyocytes derived from human iPS cells (iPS-CMs) have potential application forregenerative medicine as well as cardiac toxicity testing. Recently, it has been reported that
iPS-CMs can predict drug induced QT-prolongation, which is a major safety issues around drug
development.
Production methods of the iPS-CMs have been developed by many labs including us . It’s
important to clarify the basic characters and drug responsiveness of iPS-CMs produced by eachmethod because the differences in production method may affect the ir characters.
We have developed novel production method for highly purified (>95%) iPS-CMs without
drug selection using cardiomyocyte specific promoter and drug resistant gene (in preparation).
Currently, the iPS-CMs produced by the method are commercially available as MiraCell ™
Cardiomyocytes (from ChiPSC12).
The aim of this study is to evaluate phenotypic characters and the response to 4 reference
drugs for cardiac toxicity testing using MiraCell™ Cardiomyocytes (from ChiPSC12). Furthermore,
we compared MiraCell™ Cardiomyocytes with two types of commercially available iPS-CMs
provided by other vendors (vendor X and Y) in drug response.
Immunostaining of MLC2V and MLC2A, a maker for ventricular and atrial cardiomyocytes
respectively, revealed that majority of the MiraCel l™ Cardiomyocytes showed MLC2V positive
cells, suggesting ventricular-like cardiomyocytes.
In order to assess the response to the 4 reference drugs, we estimated field potential duration(FPDs), which is correlate to QT intervals in vivo ECG, and beats per minute (BPM) of the cells by
multi-electrode array system (MED64). We observed prolonged FPDs against E-4031 (Ikr blocker),
chromanol 293B (Iks blocker) and mexiletine (Na and Ikr blocker), and shortened FPDs against
verapamil (Ca blocker). In addition, early after depolarization could be detected by E4031 andmexiletine. While the degree of FPD prolongation by chromanol 293B was similar among three
iPS-CMs we compared, those by E-4031 was most moderate in MiraCell™ Cardiomyocytes. The
treatment of Verapamil caused slightly increase of BPM in MiraCell ™ Cardiomyocytes in dose
dependent manner, but the degree of increased BPM was significantly lower than that of the
iPS-CMs provided from vendor Y. The iPS-CMs provided from vendor X showed decrease of BPM
in dose dependent manner of Verapamil.
These results demonstrated that MiraCell™ Cardiomyocytes showed expected response of 4
reference drugs. And we have confirmed some differences in the responsiveness among three
iPS-CMs we compared in this study. Further studies are needed for elucidating the reason for thesedifferences.

P4-11
Study of Effects of NF-κB for Reporting Frequency ofAdverse Drug Reactions based on Tox21-AOP Database
Jun Teraoka
y131219@std.my-pharm.ac.jp

Yurie Yoshiday131344@std.my-pharm.ac.jp

Yuhei Mashiyama
y131288@std.my-pharm.ac.jp

Ryota Higuchi
y131264@std.my-pharm.ac.jp

Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

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

Keywords: Tox21, JADER, NF-κB, Adverse Drug Reaction, Adverse Outcome Pathway
Introduction
NF-κB is one of the adverse outcome pathways (AOPs). In this study, comprehensive analyses ofa dataset consisting of the Tox21-AOP database merged with the Japanese Adverse Drug Event
Report (JADER) database were performed to investigate adverse drug reactions involving NF-κB.
Methods
Molecular information was added to JADER. NF-κB activity scores described in the Tox21-AOP
database were then merged with JADER via the molecular information. Odds ratios and P-values
were calculated by Fisher's exact test based on the relation between NF-κB-activating propertiesof drugs and the reported frequency of whole cases in the dataset. Adverse drug reactions related to
NF-κB were extracted by using these statistical values.
Results and Discussion
Dermatological inflammatory diseases, such as generalized rash, erythema multiforme, and drugrash, as well as many other adverse reactions, were significantly related to drugs with NF-κB
activity. These findings are consistent with reports in which NF-κB is one of the responsible
pathways for generation of proinflammatory cytokines. Our results may be useful for both drug
discovery and clinical management of disease.

P4-12
HGS is a new molecular target for antitumor medicine.
Kiyoshi Ogura 1

Tomohiro Iguchi 1

Ikuo Kawashima 1

Ogura-ky@igakuken.or.jp

Koji Kasahara 1
1

Laboratory of Biomembrane, Tokyo Metropolitan Institute of Medical Science ,Kamikitazawa, Setagaya-ku, Tokyo 156-8506, Japan

Keywords: ESCRT, Signal Transduction, Tumor, Peptide
The endosomal sorting complexes required for transport (ESCRT) machinery plays multivesicularbody (MVB) biogenesis, cellular abscission, and viral budding [1]. The ESCRT machinery is madeof ESCRT-0, ESCRT-I, ESCRT-II, and ESCRT-III [1]. The ESCRT-0 complex plays a vital role in
the generation of MVB and in the signal transduction mediated by cytokines and growth factors [2].
The ESCRT-0 complex is a 1:1 heterodimer of Hepatocyte growth factor-regulated tyrosine kinase
substrate (HGS) and Signal transducing adapter molecule (STAM) through antiparallel coiled-coildomains [1]. This dimerization is necessary to play the role of the ESCRT machinery. We
demonstrated that HGS was involved in HGF and TGF-β-SMAD signal transduction and promoted
cancer properties. The HGS overexpression caused carcinogenesis in normal cell lines. The HGS
overexpression also aggravated cancer properties such as TGF-β-SMAD signal transduction,
cancer cell metastasis, angiogenesis ability, and tumor growth ability in a mouse melanoma B16
cancer cell line. On the other hand, the overexpression of C domain (HGS/C) protein of HGS
obstructed the TGF-β-SMAD signal transduction and suppressed the cancer properties.
Furthermore, HGS/C oligopeptides (10 amino acid residues in length) which constituted HGS/C
protein suppressed in vitro cancer properties in the B16 cell line and in a human colorectal
COLO205 cell line. HGS/C oligopeptides also suppressed the tumor growth in vivo by a
subcutaneous administration and an intravenous administration. These findings suggested that
HGS was a new molecular target for antitumor medicine . The result of this study is expected tocontribute to medicine design and development for antitumor medicine that targets HGS
molecule.
[1] Oliver Schmidt and David Teis., Curr Biol. 2012 Feb 21; 22(4): R116–R120.
[2] Thomas Wollert and James H. Hurley., Nature. 2010 Apr 8; 464(7290): 864–869.

P4-13
Construction of the integrated database for hERG blocking
small molecules
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 avoid delay of the development of therapeutic
compounds or withdrawal of marketed drugs. Utilizing the recent increase of information abouthERG inhibitors in public databases, many successful applications of machine learning techniques
to predict hERG inhibition were reported[2]. However, most of such researches constructed theirdataset from only one database because the difference of the data format and ontology made theintegration of the databases difficult. This incompleteness of the dataset could limit the prediction
performance and applicability domain of the statistical models. In this study, we curated existingdataset in ChEMBL, PubChem, GOSTAR, and hERG Central[3], and integrated them into a largest
database for hERG inhibition by small molecules (Table 1). Assessment of structural diversity of
the hERG inhibitors in each database using structural clustering revealed that the integrated
database contains more than 1.5 times as many clusters as any of the existing databases.
Table 1
Database
ChEMBL(21)GOSTAR
PubChem (AID588834)
ChEMBL(21)GOSTARhERG Central
integrated hERG database

Value type
IC50
IC50
IC50
% inh.
% inh.
% inh.

hERG inhibitors(IC50≦10uM)
3,986
2,772
572
587
494
231
5,650

Inactive compounds Compounds(IC50>10uM)(total)
3,937
7,923
2,729
5,501
1,136
1,708
1,212
1,799
921
1,415
303,108
303,339
309,560
315,210[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] Braga R.C., Alves V.M., Silva M.F., Muratov E., Fourches D., Lião L.M., Tropsha A., Andrade C.H.
Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity, Mol. Inform.
34(10), 698-701 2015[3] 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

P4-14
Analyses of Chemical Properties Associated with Reporting
Frequency of Erythema Exudativum Multiforme Major inAdverse Drug Reaction Database
Yuhei Mashiyama

Yurie Yoshida

Ryota Higuchi

y131288@std.my-pharm.ac.jpy131344@std.my-pharm.ac.jpy131264@std.my-pharm.ac.jp

Jun Teraoka

Yoshihiro Uesawa

y131219@std.my-pharm.ac.jp

uesawa@my-pharm.ac.jp

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

Keywords: Erythema exudativum multiforme major (EEMM) , JADER, Adverse Drug Reaction,
Chemical Structure
Introduction
Erythema exudativum multiforme major (EEMM) is a group of diseases, including
Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), characterized by fever,general malaise, systemic erythema, and erosion. EEMM occurs as an adverse reaction to a variety
of medications. The mechanism of development of EEMM is unclear. The purpose of the pres ent
study is to clarify properties of drugs related to EEMM.
Methods
Japanese Adverse Drug Event Report (JADER) database added chemical structures of drugs wereused to characterize molecular features, such as structural and physicochemical properties, re lated
to the reported frequency of EEMM.
Results and Discussion
Many kinds of molecular descriptors related to molecular refractive index, partial equalization oforbital electronegativity, and logP were involved in the reported frequency of EEMM in the
JADER database. Discrimination models for compounds could be constructed by using thesedescriptors. These findings might be useful to avoid usage of risky drugs in patients with EEMM
constitution.

P4-15
Analyses of Chemical Properties for Reporting Frequency ofThrombocytopenia in Adverse Drug Reaction Database
Yurie Yoshida

Ryota Higuchi

y131344@std.my-pharm.ac.jpy131264@std.my-pharm.ac.jp

Yuhei Mashiyama
y131288@std.my-pharm.ac.jp

Jun Teraoka
y131219@std.my-pharm.ac.jp

Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

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

Keywords: JADER, Thrombocytopenia, Adverse Drug Reaction, Chemical Structure
Introduction
Thrombocytopenia as an adverse drug reaction may involve thrombocyte production -suppressing
effects resulting from myelosuppression by the drug. This side effect may be difficult to predict
from pharmacological aspect. The Japanese Adverse Drug Event Report (JADER) databasepublished from Pharmaceutical and Medical Devices Agency (PMDA) includes many cases ofthrombocytopenia and is useful for the identification of drugs associated with this adverse
reaction.
Methods
JADER added chemical structures of drugs were used to characterize molecular features, such as
structural and physicochemical properties, related to the reported frequency of thrombocytopenia.
Results and Discussion
Many molecular descriptors of drugs, especially those related to van der Waals surface area, vander Waals surface area, and logP were statistically significantly associated with thrombocytopenia.
Understanding of the common chemical properties of drugs associated with thrombocytopenia may
be helpful to prevent this adverse drug reaction.

P4-16

Analyses of Chemical Properties Associated with
Reporting Frequency of Interstitial Lung Disease
in Adverse Drug Reaction Database
Ryota Higuchi
y131264@std.my-pharm.ac.jp

Yurie Yoshiday131344@std.my-pharm.ac.jp

Yuhei Mashiyama
y131288@std.my-pharm.ac.jp

Jun Teraoka
y131219@std.my-pharm.ac.jp

Yoshihiro Uesawa
uesawa@my-pharm.ac.jp

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

Keywords: Interstitial Lung Disease, JADER, Adverse Drug Reaction, Chemical Structure
Introduction
Interstitial lung disease (ILD) has a variety of symptoms, such as difficulty breathing, cough, andshortness of breath due to thickening of the lung stroma with overproduction and accumulati on ofcollagen. ILD has the highest reporting frequency among all adverse drug reactions listed in theJapanese Adverse Drug Event Report (JADER) database. Therefore, characterization of thechemical structures of drugs associated with ILD might be helpful to improve the safety of drug
usage and to avoid the use of risky seed compounds in early stages of drug development.
Methods
JADER added chemical structures of drugs were used to characterize molecular features, such as
structural and physicochemical properties, related to the reporting frequency of ILD.
Results and Discussion
Numerous significant descriptors were found by analysis of variance (ANOVA). Descriptors
related to van der Waals surface area and partial equalization of orbital electronegativity were
especially useful to identify drugs associated with ILD. Construction of discrimination models for
ILD-causing drugs is planned with the use of these descriptors.

P5-01
Lipoproteins for DDS
Tatsuya Murakami 1,2
murakami@pu-toyama.ac.jp

Hirotaka Nakatsuji 3nakatsuji.hirotaka.36z@st.kyoto-u.ac.jp

Tomohiro Nobeyama 4

nobeyama.tomohiro.44z@st.kyoto-u.ac.jp

Hyungjin Kim 1
p58d7m9s@okayama-u.ac.jp

Ryosuke Fukuda 3
fukuda.ryosuke.82z@st.kyoto-u.ac.jp

Hiroshi Sugiyama 1,4

hs@kuchem.kyoto-u.ac.jp

Hiroshi Imahori 1,3
imahori@scl.kyoto-u.ac.jp

1
2
3
4

Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University,
Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
Department of Biotechnology, Faculty of Engineering, Toyama Prefectural University, 5180
Kurokawa, Imizu City, Toyama 939-0398, Japan
Department of Molecular Engineering, Graduate School of Engineering, Kyoto University,
Kyodai-Katsura, Nishikyo-ku, Kyoto 615-8510, Japan
Department of Chemistry, Graduate School of Science, Kyoto University,
Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan

Keywords: High-density lipoprotein, gold nanorods, carbon nanotubes, phototherapy
High-density lipoprotein (HDL) is a ca. 10 nm nanoparticle that consists of lipids andlipid-binding proteins. HDL has long been known as a cholesterol transporter from the periphery to
the liver, but recent studies have clarified its broader functions, including anti-inflammation,
anti-oxidation, anti-thrombosis, etc. HDL is easily reconstituted with phospholipids andrecombinant apoA-I proteins and incorporates or binds a wide variety of therapeutic agents. HDL
as a drug carrier has at least distinct two advantages over well-known drug carriers, liposomes andpolymeric micelles, which are now used in clinics and tested in clinical trials, respectively. Theyare (1) no need of polyethylene glycol for its formulation and (2) its much smaller size.
Polyethylene glycol has been most widely used for DDS development but recently it is reported to
be immunogenic under some conditions. Smaller drug carriers (but >8 nm) are able to penetrate
deeper into tumor tissues. We have chemically and genetically engineered HDL to explore its DDS
potential [1-4]. In this meeting, we will present our recent HDL researches for development oflocal and systemic DDSs.
[1] Murakami, T., Nakatsuji, H., Morone, N., Heuser, J. E., Ishidate, F., Hashida, M., and Imahori,
H., Mesoscopic metal nanoparticles doubly functionalized with natural and engineered lipidic
dispersants for therapeutics, ACS Nano, 8:7370–7376, 2014.
[2] Murakami, T., Okamoto, H., and Kim, H., Structural and functional changes in high-density
lipoprotein induced by chemical modification, Biomater. Sci., 3:712-715, 2015.
[3] Nakatsuji, H., Numata, T., Morone, N., Kaneko, S., Mori, Y., Imahori, H., and Murakami, T.,
Thermosensitive ion channel activation in single neuronal cells by using surface-engineered
plasmonic nanoparticles, Angew. Chem. Int. Ed., 54:11725-11729, 2015.
[4] Kim, H., Okamoto, H., Felber, A. E., Polomska, A., Morone, N., Heuser, J. E., Leroux, J.-C.,
and Murakami, T., Polymer-coated H-responsive high-density lipoproteins, J. Control. Release,228:132-140, 2016.

P5-02
Spontaneous pseudorotaxane formation methods
targeting on nucleic acids
Kazumitsu Onizuka

Takuya Miyashita

onizuka@tagen.tohoku.ac.jp

Tomoko Chikuni

Fumi Nagatsugi

nagatugi@tagen.tohoku.ac.jp

Institute of Multidisciplinary Research for Advanced Mate rials, Tohoku University, 2-1-1
Katahira, Aoba-ku, Sendai, Miyagi, 980-8577, Japan

Keywords: Nucleic acids, Pseudorotaxane, Template reaction, Molecular robotics
Rotaxane is a molecular architecture consisting o f a dumbbell shaped molecule which isthreaded through a molecular ring. In the nucleic acid chemistry, unique mechanically interlocked
molecular architectures such as catenane and rotaxane have been constructed by taking advantage
of base pairing. A variety of methods to construct interlocked molecular architectures have beendeveloped for DNA nanotechnology 1 and DNA topological labeling 2 . However, these methods
either require a toxic chemical reagent or an enzyme to form topological structures; thus, it isdifficult to construct them inside the cell.
Currently, we are investigating novel methods to form topological DNA/RNA architectures thatneither require a toxic chemical reagent nor an enzyme (Fig.). Here we report a method to form a
pseudorotaxane architecture spontaneously using only a pair of reactive oligodeoxyribonucleotides(ODNs), which we designed and synthesized, and then performed the pseudorotaxane formation
reaction with both DNA and RNA oligonucleotides. The reaction proceeded smoothly without
any extra reagents at 37 °C and pH 7.2, leading to the formation of a stable complex on a
denaturing polyacrylamide gel 3 . Interestingly, the pseudorotaxane was formed with the cyclized
ODN reversibly by temperature control via the slipping process. This new pseudorotaxane
formation represents a promising method for developing new DNA nanotechnologies and antisense
oligonucleotides.

37 o C, pH 7.2
heating

Fig. Schematic representation of pseudorotaxane formation[1] D. Ackermann, T. L. Schmidt, J. S. Hannam, C. S. Purohit, A. Heckel, M. Famulok, Nat.
Nanotechnol., 2010, 5, 436-442.
[2] Y. Liu, A. Kuzuya, R. Sha, J. Guillaume, R. Wang, J. W. Canary, N. C. Seeman, J. Am. Chem.
Soc., 2008, 130, 10882-10883.
[3] K. Onizuka, F. Nagatsugi, Y. Ito, H. Abe, J. Am. Chem. Soc., 2014, 136, 7201-7204.

P5-03
Construction of Organic-Inorganic Nanostructures byMultiple Site-Specific Precipitations
on DNAs Using Artificial Peptides.
Makoto Ozaki 1

Yoshio Hamada 1

Takahito Imai 2

m1561001@s.konan-u.ac.jp

pynden@gmail.com

imai@rins.ryukoku-ac.jp

Kin-ya Tomizaki 2,3
tomizaki@rins.ryukoku-ac.jp1

2
3

Kenji Usui 1
kusui@center.konan-u.ac.jp

Faculty of Frontiers of Innovative Research in Science and Technology (FIRST), Konan
University, Kobe 650-0047, Japan
Department of Materials Chemistry, Ryukoku University, Otsu, Shiga 520 -2194, Japan
Innovative Materials and Processing Research Center, Ryukoku University, Otsu, Shiga
520-2194, Japan

Keywords: Biomineralization, Artificial peptide, DNA, Organic-inorganic nanostructure
Biomineralization, mineralization (precipitation of inorganic compounds) in a biological system,
is controlled by biomolecules including proteins. These biomolecules can control spatiotemporally
both the size and the shape of the inorganic deposits with high reproducibility and accuracy during
the biomineralization processes. Few studies have demonstrated controlled site-specificmineralization on organic compounds for creating novel nano-structures. Development of such a
process would enable the easy construction of inorganic-organic nanocomposites. Consequently,
focusing on SiO 2 and CaCO 3 mineralization, we attempted to construct organic-inorganic
nanocomposites by site-specific precipitation using DNA and silica precipitating peptide [1] withpeptide nucleic acids (PNA) [2].
The results of micro-scale observation such as TEM and TEM-EDX showed that precipitating
structures composed of submicrometer-sized chains and submicrometer-sized spheres. We also
observed oxygen, silicon and calcium ( SiO 2 and CaCO 3 ) in the spheres and calcium, oxygen andphosphorus in the chains. In addition, we conducted macro-scale observation such as blue
silicomolybdic assay (quantitative analysis of silicic acid) and chelate titration with
ethylendiaminetetraacetate (quantitative analysis of calcium hydrogen carbonate). Thesemacroscopic results also agreed with microscopic results. Additionally, we attempted to control thespheres and chain diameters by changing the incubation time and calcium hydrogen carb onate
concentration.
In conclusion, we succeeded in shape control by site-specific precipitation of two different
inorganic compounds [3]. This method could contribute to manufacturing novel functionalorganic-inorganic nanomaterials.
[1] Knecht, M.R., and Wright, D. W., Chemical Communications, 3038-3039, 2003.
[2] Ozaki, M., Nishiyama, H., Nagai, K., Tsuruoka, T., Fujii, S., Imai, T., Tomizaki, K.-Y., andUsui. K., Chemical Communications, 52:4010-4013, 2016.
[3] Usui, K., Ozaki, M., Yamada. A., Hamada, Y., Tsuruoka, T., Imai, T., Tomizaki, K. -y.,Nanoscale, 2016, in press.

P5-04
Construction of self-assembled DNA microstructures forsynthesis of molecular robots
Masamune Morita 1

morita.m.ah@m.titech.ac.jp

Satoshi Murata 2

murata@molbot.mech.tohoku.ac.jp

12
3

Shin-ichiro M. Nomura 2

nomura@molbot.mech.tohoku.ac.jp

Miho Yanagisawa 3

myanagi@cc.tuat.ac.jp

Masahiro Takinoue 1

takinoue@c.titech.ac.jp

Department of Computer Science, Tokyo Institute of Technology, G3-53, 4259, Nagatsuta-cho,
Midori-ku, Yokohama, Kanagawa 226-8503, Japan
Department of Robotics, Tohoku University, 6-6-01 Aoba-yama, Sendai 980-8579, Japan
Department of Applied Physics, Tokyo University of Agriculture and Technology, 2-24-16

Naka-cho, Koganei, Tokyo 184-8588, Japan
Keywords: DNA microstructures, water-in-oil droplets
One of the ultimate goals of material science is to create and control self-organized systems
inspired by the living systems. Recently, molecular robotics has been attracting much attention asa novel bottom-up approach to accomplish the artificial cell-like systems by using DNA, RNA andlipids [1]. Bottom-up DNA nanotechnology has achieved the construction of various-sized DNA
structures such as nanometer-sized DNA origami and macroscopic DNA hydrogels [2, 3].
Self-assembled DNA microstructures have potential application to create a body of molecularrobots. However, it is difficult to control the form of self-assembled DNA microstructures underconventional methods.
Here, we present a self-assembled DNA microstructure with fractal-like patterns (Fig), and theinvestigation of its formation mechanism. The self-assembled DNA microstructure is formed at awater-oil interface of a water-in-oil (W/O) microdroplet coated with a cationic lipid monolayer.
The electrostatic interaction between the lipid and DNA induced gelation of DNA on the interfaceof the W/O microdroplets. Because of the kinetic process of the DNA self-assembly, DNA gel
exhibited the fractal-like pattern at the interface of the W/O microdroplets. We hope that thisgeneration method can lead to novel way for construction of DNA microstructures.

Fig. A DNA microstructure with fractal-like pattern[1] Hagiya, M., Konagaya, A., Kobayashi, S., Saito, H., Murata, S., Molecular robots with sensors
and intelligence, Acc. Chem. Res., 47:1681–1690, 2014.
[2] Rothemund, P. W. K., Folding DNA to create nanoscale shapes and patterns, Nature,
440:297–302, 2006.
[3] Um, S.H., Lee, J.B., Park, N., Kwon, S.Y., Umbach, C.C., Luo, D., Enzyme-catalysed assemblyof DNA hydrogel, Nature Materials, 5:797–801, 2006.

P5-05
Allosteric control of nanomechanical DNA origami devices
Naohide Akamatsu

Ryosuke Watanabe

Yuta Kondo

K245579@kansai-u.ac.jp

Akinori Kuzuya

Yuichi Ohyakuzuya@kansai-u.ac.jp

ohya@kansai-u.ac.jp

Department of Chemistry and Materials Engineering, Kansai University, 3-3-35
Yamate, Suita, Osaka 564-8680, Japan

Keywords: Molecular Robotics, DNA nano structures
By introducing two groups of ligands to a pliers-shaped DNA origami nanostructure (DNA pliersand DNA chopsticks) [1, 2] , allosterically controllable nanomechanical DNA origami devices havebeen constructed. Four pairs of complementary chain attached to one side of the lever to provide
allosteric site, which have photo-cleavable linker at the end of complementary chain, and tightly fix
DNA pliers into parallel closed form. This structural change of DNA pliers arranges two specificligands for the chemical target of detection, which are attached to the opposite side of the levers asanchors, in close proximity to promote ligand–target–ligand bidentate binding. Successive
irradiation of UV cleave the PC zipper, and leaves the second bridge, formed by the anchors andthe target, to keep the parallel closed form, visually distinguishable under AFM. Significantly
enhanced binding to miRNA target in a pre-formed binding site as well as bidentate conjugation of
thiolated DNA to single AuNP were also clearly demonstrated with the present system.
[1] P. W. K. Rothemund, Nature 2006, 440, 297-302.
[2] A. Kuzuya, Y. Sakai, T. Yamazaki, Y. Xu, M. Komiyama, Nature Commun. 2011, 2, 499.

P5-06Creation of Phototactic Liposome
by Photo-Responsive Peptide
Kazunori Matsuura, Akihiko Uemura , Kazushi Morishita, Hiroshi Inaba
ma2ra-k@chem.tottori-u.ac.jp

Graduate School of Engineering Tottori University, 4 -101 Koyama-Minami, Tottori 680-8552,
Japan

Keywords: Molecular Robotics, Phototaxis, Liposome, DNA-peptide conjugate, Photocleavage,
Nanofiber
Spaciotemporal control of peptide nanofiber growth was achieved by photocleavage of a
DNA-conjugated  -sheet forming peptide which is linked through a photoresponsible amino acidresidue. Peptide nanofibers were selectively formed by photocleaving the conjugate oncomplementary DNA-immobilized glass substrate [1] and giant liposome. Photo-cleavage of the
conjugate on phase-separated giant liposome caused phototaxis of giant liposome due toMarangoni convection.

ab-sheet forming peptide
DNA

H
N

H2N
O

UV light

O

Oo
NB

Photocleavable
group

H
N

H2N

O
O

O

Conjugate 1bConjugate 1

Nanofiber
Nanofiber

UV light

Liposome[Phase separation]

Formation of nanofiber
by release of peptide

Translational motion

Fig. 1. (a) Photo-induced peptide nanofiber growth (b) Spatiotemporal modulationof translational motion of liposome by modification of conjugate 1[1] M. Furutani, A. Uemura,
51, 8020 (2015).

A. Shigenaga, C. Komiya, A. Osaka, K. Matsuura, Chem. Commun.,P5-07
Density Functional Theory and Random Matrix Theory
in Proteogenesis
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: Protein, Molecular evolution, Random matrix, Density functional theory
Proteins are synthesized from amino acids based on the genetic information encoded into DNA.
This is the central dogma, to which almost all of life on Earth adheres. It is obvious that the firstprotein was not synthesized according to this dogma, because another protein must haveparticipated in the role of as an enzyme. By what kind of mechanism was the first protein
synthesized? Because this question addresses the very origin of life itself, answering it is adifficult and important problem.
Meanwhile, a fine-tuning of the localization of the existing amino acid sequence in its entire
configuration space and the existence of the upper bound of the total number of protein tertiary
structures are a mystery in the study of biological evolution. [1] For example, assuming 20 types ofamino acids and 100 amino acid residues, the possible number of proteins is 20 100 . However, thefine-tuning results that the upper bound of the total number of proteins in an existing protein foldis only 400-1000 and that in an existing protein superfamily is only 1000-30000. Despite the factthat the above estimates of the upper bounds were made over 10 years ago, they are found to be
fairly accurate compared with recent estimates from recent, vigorous analyses of a proteins tertiary
structure. Fine-tuning of the sequence localization was accomplished via the process of molecular
evolution itself, and it is essential to existence. However, it is not known whether the fine -tuningof the localization is a coincidence or an inevitability.
Recently, physics-based approach was proposed by [2], where the electronic state of the valence
electrons for macromolecules are calculated using the extended Huckel method and multi-fractal
and random matrix analysis are performed. Their analysis showed that the system is critical andthe wave functions are multifractal.
We reexamine their results in detail and study the energy-level statistics of amino acids byrandom matrix theory and calculate the orbital energy of 20 kinds of amino acids using thedensity-functional formalism. [3] To generate statistical data, we performed a multipoint
calculation on 10000 molecular structures for each amino acid produced via the molecular
dynamics simulation. For the valence orbitals, the energy-level statistics exhibit level repulsion,
but the universality in the random matrix cannot be determined. For the unoccupied orbitals, theenergy-level statistics agrees with an intermediate distribution between the Gaussian orthogonalensemble and the semi-Poisson statistics for all 20 kinds of amino acids. These amino acids are
considered to be in a type of critical state.
[1] For example, C. Chothia, Nature 357, 543 (1992); C.A. Orengo, D.T. Jones, and J.M. Thornton,
Nature 372, 631 (1994); N.N. Alexandrov and N. Go, Protein Sci. 3, 866 (1994); Z.-X. Wang,
Proteins 26, 186 (1996); C.-T. Zhang, Protein Eng. 10, 757 (1997); S.E. Brenner, C. Chothia,
and T.J. Hubbard, Current Opinion in Struct. Bio. 7, 369 (1997).
[2] G. Vattay, D. Salahub, I. Csabai, A. Nassimi, and S. A. Kaufmann, J. of Physics: Conference
Series 626, 012023 (2015).
[3] Masanori Yamanaka, submitted to JPSJ.

P5-08Morphological control of tubulin-encapsulating
giant-liposomes induced by change of hydrostaticpressure, temperature and osmotic pressure

Masahito Hayashi 1 , Masayoshi Nishiyama 2,3 , Yuki Kazayama 4 , Taro Toyota 4,5 ,Yoshie Harada 2 , Kingo Takiguchi 1,6
j46037a@nucc.cc.nagoya-u.ac.jp

1

2
3
4

5

6

Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya
464-8602, Japan
Institute for Integrated Cell−Material Sciences (WPI−iCeMS)
The HAKUBI Center for Advanced Research, Kyoto University, Kyoto 606-8501, Japan
Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku,
Tokyo 153-8902, Japan
Research Center for Complex Systems Biology, The University of Tokyo, 3-8-1 Komaba,
Meguro-ku, Tokyo 153-8902, Japan
Structural Biology Research Center, Nagoya University, Nagoya 464-8601, Japan

Keywords: Molecular robotics, Giant liposome, Microtubule, Morphological control
We examined the effect of hydrostatic pressure, temperature, and osmotic pressure on the
tubulin-encapsulating giant-liposomes (TEGLs). Many of TEGLs kept a bipolar shape with acentral sphere and two tubular protrusions that aligned in a straight line at room temperature andambient pressure as reported previously [1]. Immediately after the application of high pressure (80MPa) or low temperature (4ºC), the protrusions shrunk within several tens of seconds. This process
was reversible; after the pressure was released or the temperature increased to 25ºC, theprotrusions regenerated within several minutes [2]. Surprisingly, TEGLs showed poly-spine
shape, which had a hundred of membrane spines, when microtubules elongated after
hypertonic treatment. Our results demonstrate that the shape of giant liposomes iscontrollable by changing the polymerization and depolymerization dynamics of
microtubules. The findings should help to develop molecular robotics to construct an
artificial motile cell model driven by physical stimulation cycle.
[1] Kaneko, T., Itoh, T. J., and Hotani, H., Morphological Transformation of Liposomes Caused by
Assembly of Encapsulated Tubulin and Determination of Shape by Microtubule-AssociatedProteins (MAPs), J. Mol. Biol., 284: 1671−1681, 1998.
[2] Hayashi, M., Nishiyama, M., Kazayama, Y., Toyota, T., Harada, Y., and Takiguchi, K.,
Reversible Morphological Control of Tubulin-Encapsulating Giant Liposomes by Hydrostatic
Pressure, Langmuir, 32: 3794-3802, 2016.

P5-09Challenge to Develop the Platform of Personalized
Medicine and Point-of-Care for Elderly People
in Local City of Japan
Toshihisa Ishikawa 1,2
toshihisa.ishikawa.r@gmail.com
1

2

NGO Personalized Medicine & Healthcare, Kirigaoka, Midori -ku, Yokohama, Kanagawa
226-0016, JapanThe Saijo Office of NGO Personalized Medicine & Healthcare, SICS , Kambai-ko, Saijo Ehime,
793-0041, Japan

Keywords: Personalized medicine, pharmacogenomics, genetic testing, point -of-care
The rapid growth of personalized medicine is being supported by emerging new technologies
together with accumulating knowledge of pharmacogenomics. Basic technologies of moleculardiagnostics play a role in expanding pharmacogenomic information, particularly with respect to
SNP genotyping. Diagnosis is thus integrated with therapy for selecting treatments as well formonitoring results. Cost-effective methods should be developed for genotyping, however, and it
would be desirable to include this information in the patient's record as guidance for physicians to
individualize the treatment. The accurate measurement of allele frequency variations among
population groups with different sensitivities to diseases and/or different responses to drugs isfundamental to genetic epidemiology.
Development of personalized medicine, including point-of-care technology, requires theintegration of various segments of biotechnology, clinical medicine, and pharmacology. Multiple
players should be involved in the development of personalized therapy. In particular,
pharmaceutical and biotechnology companies would take leading roles in this venture in keepingwith their future roles as healthcare enterprises.
Nevertheless, the reality is different from such scientific thoughts. It is not easy to develop theplatform for personalized medicine and point-of-care in local cities in Japan, where most people
are very conservative. Since 2014, I have been concentrating my efforts and time on that challenge
in Saijo city (ca. 100,000 population) and have recently realized th at education of medical doctors,
pharmacists, as well as citizens of the local city is utmost critical issue.
[1] Ishikawa, T., https://www.facebook.com/personalized.medicine.healthcare/?

P5-10Causality Analysis for Finding Potential Cancer Driver
Genes Using a Differential Gene Expression profile
in Gene Regulatory Network

1

Bharata Kalbuaji 1

Akihiko Konagaya 1

kalbuaji.b.aa@m.titech.ac.jp

kona@c.titech.ac.jp

Department of Computational Intelligence and System Science, Tokyo Institute of Technology

Keywords: gene regulatory network, cancer driver gene, RNA sequencing
Finding driver genes which affect cancer growth has been a major challenge for per sonalized
medicine. One of the latest technological advances for elucidating potential driver genes isnext-generation RNA sequencing (RNA-seq). RNA sequencing technology can be used to derivegene expression profiles for normal and cancer cells. In this research, we develop a computational
method derived from mathematical model of gene expression process. The method integrates gene
expression data from RNA sequencing and transcription factor gene data to discover potential
driver gene candidates.
Potential cancer driver genes may have different gene expression levels from normal cells.
The genes affected by the driver genes may also have different gene expression levels. Such genes
are called passenger genes. In order to find the driver genes, we need to develop a method that candistinguish the driver genes from the passenger genes in differential RNA expression profile incancer cells.
We hypothesize that analysing the expression difference within a regulatory network is the key
to discovering the driver gene candidates. We assume that if transcription factor genes aredifferentially expressed, then the target genes will have a great chance to be differentiallyexpressed. Based on this assumption, we can derive a relationship between transcription factor
genes and target genes. When this potentially causal relationship has been inferred, we candistinguish which transcription factor genes influence the changes in cancer and normal cells andwhich genes are affected by the transcription factor genes. This method is applied to a gene
regulatory network so that we can see a broader view of causality relationship among the genes.
We use the Wnt signalling network as an example.
[1] de-Leon, S. B.-T., & Davidson, E. H. (2009). Modeling the dynamics of transcriptional gene
regulatory networks for animal development. Developmental Biology, 325(2), 317 –328.
[2] Boulter, L., Guest, R. V., Kendall, T. J., Wilson, D. H., Wojtacha, D., Robson, A. J., … Forbes,
S. J. (2015). WNT signaling drives cholangiocarcinoma growth and can be pharmacologicallyinhibited. The Journal of Clinical Investigation, 125(3), 1269 –1285.
[3] Karlebach, G., & Shamir, R. (2008). Modelling and analysis of gene regulatory networks.
Nature Reviews Molecular Cell Biology, 9(10), 770-780.

P5-11
Microtubules Flow Dynamics Analysis on Gliding Assay
Bulbul Mahmot 1
bulbul.mm@gmail.com

1
2

Daisuke Inoue 2
daisukeinoue@mail.sci.hokudai.ac.jp

Akira Kakugo 2

Akihiko Konagaya 1

kakugo@sci.hokudai.ac.jp

kona@c.titech.ac.jp

Tokyo Institute of Technology, Nagatsuta, Midori-ku, Yokohama, 226-8502, Japan
Faculty of Science, Hokkaido University, Sapporo 060-0810, Japan

Keywords: Microtubule dynamics, fluid flow, Structure tensor
Various complex microtubules patterns including rings and streams can be observed in
microtubule gliding assay experiment. Understanding microtubule motion patterns has
important implications for establishing nanometer level robots. We investigate a “fluid
flow” of microtubules assuming microtubules as indicators of the fluid flow. A structure
tensor provides knowledge about a local orientation of microtubules at each pixel in microtubule
images. This knowledge can used to investigate specific microtubule flows. We adopt this method
for measuring the collective behavior of microtubules driven by kinesin fixed on a gliding assay.
This method enables us to characterize the degree of transition of vortex like motion with regards
to an order parameter. A gradient tensor is obtained from the Cartesian product of the gradientvector of an image. The order parameter helps us to estimate how microtubule ring-like swarm
behavior is undertaken through the first frame to the last frame. In this work, we describe theimage processing steps and algorithms in detail. Numerical analysis demonstrates the existence oflarge ring like motion and stream like motion on our images.

[1] D. Inoue, B. Mahmot, et al., Depletion force induced collective motion of microtubules driven
by kinesin, Nanoscale, 7, 43:18054-18061, 2015.
[2] Gupta, Mukund, et al., Adaptive rheology and ordering of cell cytoskeleton govern matrix
rigidity sensing, Nature communications, 6, 2015.

P5-12PROLOQL: Integration of SPARQL and Prolog for
Sematic Web Systems

1

Kenta Watanabe 1

Akihiko Konagaya 1

watanabe.k.cd@m.titech.ac.jp

kona@dis.titech.ac.jp

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

Keywords: Semantic Web, Linked Open Data, SPARQL, Text mining
In recent years, the World Wide Web mainly written in HTML for data description has been also
standardized to the Semantic Web for data linking description. In keeping with this trend,
enormous LOD (Linked Open Data) have been published by academia, industry and government,
so far. However, effective uses of LOD are still limited mainly due to the difficulties inprogramming and data access performance. In order to solve these issues, we propose an inference
system PROLOQL to use LOD with SPARQL and Prolog.
Firstly, we combined a logic programming language Prolog and SPARQL, a query language forsearching RDF[1], the data format for LOD[2]. RDF format and Prolog clauses are very similar inlanguage structures and would be compatible with each other. In RDF, the relationship of two data
is described as Subject, Predicate and Object: predicate(subject, object) in Prolog clause.
In PROLOQL, data getting from LOD are treated as atomic clauses. This means that an enormous
amount of various data on the webs can be accessed from Prolog programs just describing
SPARQL queries for LOD.
Secondly, we developed a LOD based text mining system. In this system, we can collect themeaning and related concepts of given words through dictionary web site such as Wikipedia. We
can also solve the problem of a spelling inconsistency and synonyms in similar way. The mostremarkable point is that these functions are very similar to what human does in web surfing.
Lastly, we apply above two systems to understand “Tenpu-Bunsho”, a Japanese medical documentfor drug usages, as an example for our natural language understanding system. There is a lot ofspelling fluctuations and synonyms in Tenpu-Bunsho as well as technical words. So, it is difficult
for a computer to understand the Tenpu-Bunsho in general. We believe our system will be aneffective solution to analyze domain specific texts using LOD as live dictionaries.
[1] W3C:W3C Recommendation 15 January 2008 (http://www.w3.org/TR/2008/REC-rdf-sparql-query-20080115/)[2] W3C:W3C Recommendation 10 February 2004 (http://www.w3.org/TR/2004/REC-rdf-prim
er-20040210/)

P5-13
Liposome-based Liquid Handling System
Shota Katsuta 1

Taiji Okano 1

katsuta@nano.mech.chuo-u.ac.jp

okano@mech.chuo-u.ac.jp

Hiroaki Suzuki 1
suzuki@mech.chuo-u.ac.jp

1

Dept. Precision Mechanics, Faculty of Science and Engineering, Chuo University
1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan

Keywords: Liposome, Fusion, Division, Liquid HandlingThis paper describes the utilization of giant unilamellar vesicles (GUVs) as a platform forhandling miniature amount of chemical and biochemical reagents. GUVs with diameters of 5 to 10µm and containing chemical/biochemical reagents together with inert polymers were fused withelectric pulses (electrofusion). After reagent mixing, the fused GUVs spontaneously defo rmed to abudding shape, separating the mixed solution into sub -volumes [1]. We utilized a microfluidic
channel and optical tweezers to select GUVs of interest, bring them into contact, and fuse them
together to mix and aliquot the reaction product. We als o show that, by lowering the ambient
temperature close to the phase transition temperature T m of the lipid used, daughter GUVs
completely detached (fission). This process performs all the liquid -handing features used inbench-top biochemistry using the GUV, which could be advantageous for the membrane -related
biochemical assays [2].
[1] Terasawa, H. et al., Coupling of the fusion and budding of giant phospholipid vesicles
containing macromolecules, PNAS, 109, 5942-5947, 2012.
[2] Shiomi H. et al., Liposome-Based Liquid Handling Platform Featuring Addition, Mixing, andAliquoting of Femtoliter Volumes, Plos One, 9(7): e101820, 2014.

P5-14
Photochemical RNA editing using 3-vinylcarbazole analog
containing oligodeoxynucleotide
Yasuharu Takashima

S1530026@jaist.ac.jp

Shigetaka Nakamuras7kmr4gtk6a@jaist.ac.jp

Kenzo Fujimoto

kenzo@jaist.ac.jp

Department of Advanced Science and Technology, Japan Advanced Institute of Science andTechnology, 1-1 Asahi-dai, Nomi, Ishikawa, 923-1292, Japan

Keywords: RNA editing, Photo-cross-linking, Nucleic acid medicine
DNA/RNA editing is powerful tool for the treatment of genetic disease, and enzymatic or
chemical editing have been reported. In previous literature, the photochemical C→ U transition
occurred via [2+2] photocycloaddition between C and 3-cyanovinylcarbazole( CNV K) 1,2 . This
reaction is distinctive and modifies the C to U in a sequence specific manner. However, thisreaction required heating at 90°C for 1h, which prevents the application of this method to living
cell. In this study, we designed and demonstrated the C→ U transition via [2+2]photocycloadditionof 3-vinylcarbazole derivatives to overcome this advantage of the C→ U transition.
We synthesized 3-carboxyvinylcarbazole( OHV K), 3-methoxycarbonylcarbazole( OMeV K), and3-carbonylamidevinylcarbazole( NH2 K) to demonstrate C→U transition. The oligodeoxynucleotides(ODN) containing 3-vinylcarbazole derivatives and complementary ODN was photo-cross-linked
and then its photo-cross-linked dsDNA was incubated to perform C→U transition. After C→Utransition, it was analyzed by denaturing PAGE. The C→ U transition in the DNA duplex using
CNV
K was processed to yield 46% by heating at 70°C for 4h. In contrast, the yield of C→ U
transition using OHV K increased to 61%. The C→ U transition activity was in the order of OHV K >CNV
K > NH2V K > OMeV K, suggesting that the C → U transition yield was influenced by thesubstitution of 3-vinylcarbazole.
[1] K. Fujimoto, K. Konishi-Hiratsuka, T. Sakamoto, Y. Yoshimura, Chem. Commun., 2010, 46,
7545.
[2] K. Fujimoto, D. Futamura, T. Sakamoto, Chem. Lett., 2013, 42, 289.

P5-15The effect of 5-substitution in pyrimidine base in DNA
photo-cross-linking using 3-cyanovinylcarbazole
Shigetaka Nakamura

S7kmr4gtk6a@jaist.ac.jp

	
  	
 

Kenzo Fujimoto

kenzo@jaist.ac.jp

Department of Advanced Science and Technology, Japan Advanced Institute of Science andTechnology, 1-1 Asahi-dai, Nomi, Ishikawa, 923-1292, Japan

Keywords: DNA photo-cross-linking, 3-cyanovinylcaarbazole, LUMO	  DNA photo-cross-linking and photo-ligation via [2+2]photocycloaddition is a useful for the
manipulation, regulation, and detection of nucleic acids. The famous photo-cross-linker such as
psoralen, cumarin, and stilbene and their application have been widely reported. We have alreadyreported that the oligodeoxynucleotide (ODN) containing 3-cyanovinylcarbazole ( CNV K) 1,2 cancross-link to pyrimidine base in the complementary DNA or RNA with a few seconds ofphotoirradiation. The biological application using CNV K such as detection of miRNA based on large
shift of 19F MR signal 3 , photoregulation of the antisense effect in living cell 4 ,acceleration of DNA
strand displacement. Thus, the CNV K has potential for biological applications; however, the detailof the photo-cross-linking, such as the effect of 5-substituent group of pyrimidine base in DNA
photo-cross-linking, are still unclear.
	  In this study, we investigated the effect of 5-substitution of the pyrimidine base in DNA
photo-cross-linking using CNV K. The duplex of ODN 1 containing CNV K and ODN 2 containing
pyrimidine derivatives such as thymidine(T), Uridine(U), Fluorouridine( F U), Hydroxyuridine( HM U),
and Trifluorothymidine( TF T) was photoirradiated at 366 nm and then those was analyzed by UPLC.
Each purimidine base having another substituent group in the C5 position had deiverse
photoreactivity with CNV K. There was a good correlation with the photoreactivity of eachpyrimidine base and the LUMO level of pyrimidine base.
[1] Y. Yoshimura, K. Fujimoto, Org. lett., 2008, 10, 3227.
[2] S. Nakamura, H. Kawabata, K. Fujimoto, ChemBioChem, 2016, 17,1499.
[3] S. Nakamura, K. Fujimoto, Chem. Commun., 2015, 51, 11765.
[4] T. Sakamoto, A. Shigeno, Y. Ohtaki, K. Fujimoto, Biomater. Sci., 2014, 2, 1154.
[5] S. Nakamura, H. Kawabata, H. Muramatsu, K. Fujimoto, Chem. Lett., 2016, 45, 887.

P5-16
Construction and functional analysis of DNA origami base
DNA-RNAP hybrid nanomachine
Takeya Masubuchi 1
kk147326@mgs.u-tokyo.ac.jp

Masayuki Endo 2
endo@kuchem.kyoto-u.ac.jp

1

2

3

Hisashi Tadakuma 1
tadakuma.hisashi.8s@kyoto-u.ac.jp

Ryo Iizuka 3
iizuka@mol.f.u-tokyo.ac.jp

Hiroshi Sugiyama 2

Yoshie Harada 2

hs@kuchem.kyoto-u.ac.jp

harada.yoshie.4r@kyoto-u.ac.jp

Takashi Funatsu 3

Takuya Ueda 1

funatsu@mol.f.u-tokyo.ac.jp

ueda@k.u-tokyo.ac.jp

Graduate School of Frontier Science s, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa-shi, Chiba 277-8562, Japan
Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University,
Yoshida-Ushinomiyacho, Sakyo-Ku, Kyoto 606-8302, Japan
Graduate School of Pharmaceutical Sciences, The University of Tokyo,
7-3-1Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: Molecular robotics, DNA nanostructure, RNA polymerase
In the cell, gene expression is highly controlled. To create biologically inspired nanoscale
device enabling the control of gene expression, we create a molecular device consisted oftranscription factors (T7-RNAP and target genes) and DNA origami tile scaffold as the functional
module (Miyazono et al., EMBO J , 2010) . The molecular device had unique features such as
orthogonality, where the device transcribes only its own gene, and rational designability, where thetranscription activity can be designed by molecular layout.

[1] Miyazono Y., Hayashi M., Karagiannis P., Harada Y., Tadakuma H. Strain through the necklinker ensures processive runs: a DNA-kinesin hybrid nanomachine study. EMBO J. 29:93 – 106.
2010.

P5-17
A Universal Asynchronous Cellular Automatonwith Cyclic-States Cells

1

2

3

4

Teijiro Isokawa 1

Ferdinand Peper 2

Ibuki Kawamata 3

isokawa@eng.u-hyogo.ac.jp

peper@nict.go.jp

kawamata@molbot.mech.tohoku.ac.jp

Nobuyuki Matsui 1

Satoshi Murata 3

Masami Hagiya 4

matsui@eng.u-hyogo.ac.jp

murata@molbot.mech.tohoku.ac.jp

hagiya@is.s.u-tokyo.ac.jp

Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo,671-2280, Japan
Brain Networks & Communication Laboratory, National Institute of Information andCommunications Technology, 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan
Division of Mechanical Engineering, Tohoku University, 6-6-01 Aoba Aramaki-Aza Aoba-Ward
Sendai Miyagi, 980-8579, Japan
Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1
Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Keywords: Molecular Robotics, Cellular Automaton, Turing Complete, Cyclic State
Cellular Automata (CA) have attracted increasing attention as architectures for computers withnanometer-scale devices, because their regularity has the potential for bottom-up manufacturing, likemolecular self-assembly. Fewer efforts, though, have gone into biological implementations of CAs,even though their features, such as their modular structure, make them suitable for such a framework.
Research in this direction has aimed to implement the enclosures surrounding each cell compartment
by DNA-based gels that are dissolved or reestablished depending on control asserted from asupervising mechanism [1]. Another mechanism for microscale control of computation by DNA has
been touched upon recently [2,3], but one of the problems it faces is the lack of a well-definedchemical environment in which operations are enclosed. Such control is important, because it is aprecursor to the successful realization of molecular robots [4], which to date remains a futuristic,
though exciting, goal.
This paper presents a CA model designed for possible implementation by the reaction and diffusionof DNA strands. The proposed CA works asynchronously, whereby each cell undergoes its transitions
independently from other cells and at random times. The state of a cell changes in a cyclic manner,
rather than according to an any-to-any mapping. The transition rules are designed as totalistic, i.e.,
the next state of a cell is determined only by the number of states in the neighborhood of the cell, notby their relative positions.
Universal circuit elements are designed for the CA as well as wires and crossings to connect them,which implies that the CA is Turing-complete. The designed CA consists of 14 types of cyclic state
cells and 57 rules for cells’ transitions.

[1] Hagiya, M., Wang, S., Kawamata, I., Murata, S., Isokawa, T., Peper, F., Imai, K.: On
DNA-Based Gellular Automata. Proc. of the 13th Int. Conf. on Unconventional Computation andNatural Computation. pp. 177-189 (2014)[2] Padirac, A., Fujii, T., Rondelez, Y.: Bottom-up construction of in vitro switchable memories.
Proceedings of the National Academy of Sciences of the United States of America 109(47),
E3212-E3220 (2012)[3] Rondelez, Y.: Competition for Catalytic Resources Alters Biological Network Dynamics.
Physical Review Letters 108(1), 018102 (2012)[4] Murata, S., Konagaya, A., Kobayashi, S., Hagiya, M.: Molecular Robotics: A New Paradigm
for Artifacts. New Generation Computing 31(1), 27-45 (2013)

P5-18Development of signal amplification circuit composedof acyclic D-threoninol nucleic acid ( D-aTNA) for
detection of RNA.
Ryuya Nagao1

Keiji Murayama1

nagao.ryuuya@f.mbox.nagoya-u.ac.jp

murayama@nubio.nagoya-u.ac.jp

Hiroyuki Asanuma1
asanuma@nubio.nagoya-u.ac.jp

1

Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, Aichi 464-8603, Japan

Keywords: Artificial nucleic acid, Signal amplification
DNA circuit[1],[2] can detect small
amount of target nucleic acid bysignalamplification
system.
However, it is difficult to detect thetarget RNA precisely, because DNA
is not only affected by impurities,
but degraded by nucleases in cell. Therefore,
we attempted to develop a signal amplification
circuits composed of D-aTNA[3] (acyclic
D-threoninol nucleic acid). D-aTNA cannot
hybridize with either DNA or RNA, while it
forms duplex with D-aTNA. Moreover,
D-aTNA has high nuclease resistivity due toits unnatural scaffold. We synthesized Fig. 1 D-aTNA signal amplification circuit
D-aTNA circuits (Fig. 1) and measured
fluorescence spectra. The results showed that the D-aTNA circuits amplified fluorescence signalby the addition of input strand. Additionally, the D-aTNA circuit was affected neither by
non-targeting DNA nor nuclease, whereas DNA circuit didn’t operate normally in the presenceof irrelevant DNA or RNA due to undesired hybridization. Furthermore, we revealed that
[4]
D-aTNA circuit could be applied to the detection of natural nucleic acids via SNA as aninterface.
[1] D. Y. Zhang and E. Winfree, J. Am. Chem. Soc. 2009, 131, 17303-17314.
[2] R. M. Dirks, N. A. Pierce, Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 15275-15278.
[3] H. Asanuma, T. Toda, K. Murayama, X.G. Liang, H. Kashida, J. Am. Chem. Soc., 2010, 132,14702-14703.
[4] H. Kashida, K. Murayama, T. Toda, H. Asanuma, Angew. Chem. Int. Ed., 2011, 50,
1285-1288.

P5-19Protein Structure Analysis Platform for Academia-based
Drug Discovery
Takashi Umehara 1,2

Hideaki Niwa 1,2

takashi.umehara@riken.jp

hideaki.niwa@riken.jp

Masaki Kikuchi 1,2

Naoki Sakai 1,3

masaki.kikuchi@riken.jp

naoki.sakai@riken.jp

Mutsuko Kukimoto-Niino 1,3
kukimoto@riken.jp

1

2

3

Mikako Shirouzu 1,3

mikako.shirouzu@riken.jp

Drug Discovery Structural Biology Platform Unit, RIKEN Center for Life Science
Technologies, 1-7-22 Suehiro-cho, Tsurumi, Yokohama, 230-0045, Japan
Epigenetics Drug Discovery Unit, RIKEN Center for Life Science Technologies, 1-7-22
Suehiro-cho, Tsurumi, Yokohama, 230-0045, Japan
Protein Functional and Structural Biology Team, RIKEN Center for Life Science Technologies,
1-7-22 Suehiro-cho, Tsurumi, Yokohama, 230-0045, Japan

Keywords: Crystal structure, High-throughput screening, Inhibitor, Ligand, Robotics
High-throughput (HTP) structural analysis of drug-targeted proteins in complexes with theirligands is now critical for rational drug development. The Division of Structural and Synthetic
Biology (DSSB) at the RIKEN Center for Life Science Technologies (CLST) has been developing
one-stop protein structure analysis platform for academia-based drug discovery in collaboration
with the RIKEN Program for Drug Discovery and Medical Technology Platforms (DMP). We havedeveloped and improved several methodologies aimed for HTP protein structure determination,
including large-scale cell-free protein synthesis system, bacterial co-expression system, andprotein/nucleoprotein complex preparation systems [1-5]. By taking advantages of theseproprietary technologies, we are now able to perform high-quantity protein production, HTP
crystallization of ligand-bound proteins, and HTP crystal structure determination through intimate
collaborations with several RIKEN platforms such as the Drug Discovery Computational
Chemistry Platform Unit in CLST and the SR Life Science Instrumentation Unit in the SPring-8
Center. Examples of our structure-based drug development along with our protein synthesis
technologies will be presented.
[1] Umehara, T., Shirouzu, M. and Yokoyama, S., Structural biology toward rational drug
development in collaboration with molecular imaging, Current Medical Imaging Reviews
8:308-313, 2012.
[2] Wakamori, M., Umehara, T. and Yokoyama, S., A series of bacterial co-expression vectors
with rare-cutter recognition sequences, Protein Expr Purif 74:88-98, 2010.
[3] Sato, S., Mimasu, S., Sato, A., Hino, N., Sakamoto, K., Umehara, T. and Yokoyama, S.,
Crystallographic study of a site-specifically cross-linked protein complex with a geneticallyincorporated photoreactive amino acid, Biochemistry 50:250-257, 2011.
[4] Wakamori, M., Umehara, T. and Yokoyama, S., A tandem insertion vector for large-scale
preparation of nucleosomal DNA, Anal Biochem 423:184-186, 2012.
[5] Higo, T., Suka, N., Ehara, H., Wakamori, M., Sato, S., Maeda, H., Sekine, S., Umehara, T. andYokoyama, S., Development of a hexahistidine-3× FLAG-tandem affinity purification method
for endogenous protein complexes in Pichia pastoris, J Struct Funct Genomics 15:191-199,
2014.

P5-20
Toward the Molecular Artificial Intelligenceby using DNA reactions

1

yasuhiro SUZUKI 1

rie Taniguchi 1

lysuzuki@nagoya-u.jp

mesomokyuu@gmail.com

Graduate School of Information Science, Nagoya University, Furocho Chikusa-ku Nagoya City
464-0841 Japan

Keywords: Molecular Robots, Strand Displacement Reactions, Molecular Artificial Intelligence
We have developed molecular Artificial Intelligence system by using DNA molecules, where"intelligence" means that the reaction system can "select" DNA molecules to sustain their reactions.
We have bio-chemically implemented the reaction system by using Chemical Reaction Systems, CRS;
CRS have been bio-chemically implemented by using the DNA strand-displacement reactions [1], inwhich two strands with partial or full complementarity hybridize, displacing in the process one or more
pre-hybridized strands[2]; we have bio-chemically implemented self maintaining reactions by using the
CRS of DNA reactions and it can sustain the reactions under noisy environment, where noises mean
mutations of DNA sequences that interact with an input sequence; in our system the input DNA
sequence A interacts with a DNA sequence B and transform into a sequence C then it interacts with asequence D and the sequence A is re-generated; hence the CRS is able to maintain A. We have obtained
several mutated DNA sequences that can sustain the reactions and we have investigated behaviors ofreactions when there are several mutated DNA sequences.
Acknowledgement
This research has been supported by Grant in Aid for Scientific Research for Innovative Area,
Molecular Robotics, No:24104002[1] D.Y. Zhang, G. Seelig, Dynamic DNA nanotechnology using strand-displacement reactions
AffiliationsCorresponding authors Nature Chemistry 3, 103–113 (2011) doi:10.1038/nchem.957[2] L. Qian and E. Winfree, Scaling up digital circuit computation with DNA strand displacement
cascades.. Science, 332:1196-1201, 2011.

P5-21
Microfluidic Formation of Lipid Bilayer-stacking
Kan Shoji

Ryuji Kawano

kan-shoji@m2.tuat.ac.jp

rjkawano@cc.tuat.ac.jp

Department of Life Science and Biotechnology, Tokyo University of Agriculture andTechnology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

Keywords: Lipid bilayer, Microfluidics, Five-layered flow
This paper describes planar lipid bilayer stacking by using the microfluidic technique. The
microchannel consisted of five channels. An organic solvent containing lipid molecules flowed
along the walls and lipid bilayers were formed at the parallel aligned walls. Although extensive
studies for preparing the planar lipid bilayers by microfluidics have been reported, the most ofthem have focused on single-stacking bilayers. In this study, we propose the method of adouble-stacking lipid bilayer formation by using the microchannel which has five -layered flow
channels and four micro walls.
Artificial lipid bilayers with membrane proteins are widely used as artificial m odels of cell
membranes [1]. Planar typed lipid bilayers have been attracted attentions as an evaluation systemof channel membrane proteins because the transport activity of the transmembrane proteins can beevaluated by electrical measurement. We have pr eviously proposed the stable and reproducible
planar lipid bilayer formation by using a droplet contact method and measured ion channels or
pore-forming proteins with single membrane system [2]. If we can form double -stacking planar
lipid bilayers and reconstituting cell adhesion proteins in the stacking lipid bilayers, it should not
only be useful for the understanding of the cell-to-cell communication mechanisms but also be
utilized as an interface for a living and artificial objects. However, it is difficult to stack planar
lipid bilayers in micro-space by using the conventional droplet contact method because of themillimeter scale chambers and droplets. Recent years, some groups have been reported
miniaturized artificial lipid bilayer systems by using microfluidic technologies [3]. In this paper,
as a first step in the reconstruction of cell-to-cell communications, we develop the microchannel
for lipid bilayer stacking and demonstrate to stack two planar lipid bilayers.
The formation process of the double-stacking lipid bilayer is as below. First, an aqueous solution
and the organic solvent containing lipid alternately flowed in each channel and the organic solvent
passes along the walls. Lipid monolayers are formed on the interface between the aqueous s olution
and the organic solvent. Then, the organic solvent layers between the walls get thin and lipid
bilayers are formed by contacting the monolayers. The organic solvent and the center and bothends of aqueous solution flowed at 4.0, 7.0 and 3.5 µL/h, respectively. The organic solvent flowed
along the walls and lipid monolayers were formed on the interface between the aqueous solution
and the organic solvent. Then, the organic solvent layers were become thin by clamping tubes toincrease the pressure and lipid bilayers were formed at the parallel aligned walls. We succeeded in
preparing and stacking planar lipid bilayers by using the microchannel. The double -staking lipid
bilayer prepared microchannel has an enough potential to be applied for the cell -to-cellcommunication model reconstruction.
[1] Tanaka, M., and Sackmann, E., “Polymer-supported membranes as models of the cell surface,”
Nature, 437, 7059:656-663, 2005.
[2] Kawano, R., Tsuji, Y., Sato, K., Osaki, T., Kamiya, K., Hirano, M., Ide, T., Miki, N., andTakeuchi, S., “Automated Parallel Recordings of Topologically Identified Single Ion Channels,”Sci. Rep., 3, 2013.
[3] M. Zagnoni, “Miniaturized technologies for the development of artificial lipid bilayer
systems,” Lab Chip, 12, 6:1026–1039, 2012.

P5-22
Novel Compensation method for Gene Expression
Fluctuation by Real-time Single-cell Selection
Yumiko Tanaka 1yumiko.tanaka@bs.s.u-tokyo.ac.jp

Mai Yamagishi 1,2,3
mai.yamagishi@bs.s.u-tokyo.ac.jp

Kaede Miyata 1

Yoshitaka Shirasaki 1,2,3
yoshitaka.shirasaki@bs.s.u-tokyo.ac.jp

Nobutake Suzuki 1,2,
nobutake.suzuki@bs.s.u-tokyo.ac.jp

Kazuyo Moro 3

miyata.kaede@bs.s.u-tokyo.ac.jp

kazuyo.moro@riken.jp

Sotaro Uemura 1,2
uemura@bs.s.u-tokyo.ac.jp

1

2

3

Department of Biological Sciences, Graduate School of Science, The University of Tokyo,
Yayoi 2-11-16, Bunkyo-ku, Tokyo 113-0032, JapanImPACT Program of Council for Science, Technology and Innovation (Cabinet Office,
Government of Japan)
RIKEN Center for Integrative Medical Sciences (IMS), 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama City, Kanagawa, 230-0045, Japan

Keywords: Single-cell analysis, secretion imaging, real-time selection
It has been known that single-cells show gene expression heterogeneity [1] , and therefore theaveraged data from the bulk cells collected by conventional method would miss some uniqueinformation of each cells. For instance we previously demonstrated that the onset of secretion ofsimultaneously stimulated immune cells were widely varied from cell to cell [2,3] . The fluctuation ofcell response ranged over hours, indicating that time-varying gene expression of cells are no longer
the same state. To overcome this, we developed a novel compensation method of gene expression
fluctuation by real-time single-cell selection technique. We monitored onset of type2 immune
response of IL-33 stimulated mouse type2 innate lymphoid cells (ILC2) by monitoring IL-13
secretion on Real-time Single-cell secretion imaging platform. The cell starting secretion of IL-13
was collected in a certain time by the glass capillary single-cell picker. These two process,Single-cell secretion imaging” and “Single-cell Selection”, proceeded in parallel for several
hundreds of cells. We found that the amount of IL-13 mRNA of the cells collected at 30 min after
onset of IL-13 secretion were less varied from that of the cells collected regardless of elapsed timefrom onset of IL-13 secretion. This result clearly shows that our method could eliminate
gene-expression fluctuation and may contribute to explore the dynamic gene-expression profiles.
[1]Elowitz, M.,B., Levine, A.,J., Siggia, E.,D., Swain, P.,S., Stochastic gene expression in a single
cell, Science, 297:1183-1186, 2002.
[2]Ting, L., Yamaguchi, Y., Shirasaki, Y., Shikada, K., Yamagishi, M., Hoshino, K., Kaisho, T.,
Takemoto, K., Suzuki, T., Kuranaga, E., Ohara, O., and Miura, M., Sincle-Cell Imaging of
Caspase-1 Dynamics Reveals an All-or-None Inflammasome Signaling Response, Cell Reports,
8(4):974-982, 2014.
[3]Shirasaki, Y., Yamagishi, M., Suzuki, N., Izawa, K., Nakahara, A., Mizuno, J., Shoji, S., Heike,T., Harada, Y., Nishikomori, R., Ohara, O., Real-time single-cell imaging of protein secretion,
Scientific Reports, 4:4736, 2014.

P5-23
Live Controlled Real-time Microtubule Gliding Assay Simulation for
Understanding Dynamic Behavior
Greg Gutmann 1
gutmann.g.aa@m.titech.ac.jp
1

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

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

Keywords: Live Control, Real-time, Microtubule Gliding Assay, GPU
Microtubule dynamics is now an area of research interest, with the goal of being able tocontrol the dynamics for use in molecular motors. However gaining a clear understanding fromexperiments alone can prove difficult. As a solution to this we have developed a 3D live controlled
simulation environment for microtubule swarm dynamics, which is able to run in real-time[1]. The
simulation is able to deal with very large test cases while maintaining the desired performance,
speed, needed for easy 3D viewing[2]. This was accomplished by using GPU cards, for bothgeneral purpose computing and graphical rendering; optimized parallel CUDA algorithms forcalculations and DirectX for rendering[3]. With the use of these technologies the simulation iscapable of simulating tens of thousands microtubules, while allowing for live control by theuser[4]. The interactions being computed allow for the recreation of microtubule interactions, suchas snuggling and overriding, naturally due to forces such as the Lennard-Jones potential.
[1] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (2014). Real-Time 3D Microtubule Gliding
Simulation. In Life System Modeling and Simulation (pp. 13-22). Springer Berlin Heidelberg.
[2] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (2016). Real-time 3D Microtubule Gliding
Simulation Accelerated by GPU Computing. International Journal of Automation and Computing.
[3] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (2016)Using a Master and Slave approach for
GPGPU Computing to Achieve Optimal Scaling in a 3D Real-Time Simulation. IEEE International
Conference on Nano/Micro Engineered and Molecular Systems.
[4] Gutmann, G., Inoue, D., Kakugo, A., & Konagaya, A. (In Publishing) Parallel Interaction Detection
Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System
Accelerated by GPGPU. New Generation Computing, Vol. 35 No. 2 Springer Publishing.

P5-24
Automatic AFM-Image Classification with Deep Learning
Fumiya Kawano 1
kawano.f.aa@m.titech.ac.jp

Junki Masada 1
masada.j.aa@m.titech.ac.jp

Akihiko Konagaya 1
konagaya.a.aa@m.titech.ac.jp

1

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

Keywords: Deep Learning, Atomic Force Microscope (AFM), Image Processing
Recently DNA-Origami has attracted a lot of attention and it is studied and developed actively allover the world for making DNA nano structures [1] and molecularrobotics [2] . In order to improve DNA-Origami design, automaticclassification of AFM image objects is highly deman ded [3] . In thisstudy, we apply a Deep Learning system (Convolutional Neural
Network) to classify smiley marks from other DNA origamis such asstars and hearts (Fig1).
Our Deep Learning system has achieved 90% accuracy after training
with regards to the classification of smiley marks and star shapes..

Fig1 AFM image (smiley and star)[1] Paul W. K. Rothemund, Folding DNA to create nanoscale shapes and patterns Supplementary
Notes 1- 11, Nature 440, 297-302, 2006
[2] Masami Hagiya, Akihiko Konagaya, Satoshi Kobayashi, Satoshi Murata, Molecular Robots with
Sensors and Intelligence, Accounts of Chemical Research, 47(6), 1681-1690, 2014[3] Yuexing Han, Akito Hara, Akinori Kuzuya, Rousuke Watanabe, Yuichi Ohya, Akihiko
Konagaya, Automatic Recognition of DNA Pliers in Atomic Force Microscopy Images , New
Generation Computing, 33(3), 253-270, 2015[4] Takayuki Okatani, Deep Learning, Kodansha Kodansha Ltd., 2015

P5-25Structural basis of kinase selectivity of pyrrolo pyrimidine
inhibitors between HCK and FLT3
Yuri Tomabechi 1 Mutsuko Kukimoto-Niino 1 Hitomi Yuki 1 Yasuko Koda 2 Ko Kikuzato 2

Junko Mikuni 1 Mariko Ikeda 1 Akiko Tanaka 1 Hiroo Koyama 2 Teruki Honma 1
Mikako Shirouzu 1
Mikako.shirouzu@riken.jp

1

2

Division of Structural and Synthetic Biology, RIKEN Center for Life Science Technologies,
1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan.
Division of Bio-Function Dynamics Imaging, RIKEN Center for Life Science Technologies, 2-1
Hirosawa, Wako, Saitama 351-0198, Japan.

Keywords: X-ray crystallography, kinase selectivity, SAR
Hematopoietic cell kinase (HCK) and Fms-like tyrosine kinase 3 (FLT3) are highly implicated inacute myeloid leukemia (AML). For example, upregulated expression is reported for the HCK
kinase in leukemic stem cells [1], and approximately 30% of AML patients are kno wn to possess
constitutive-active mutations in the FLT3 kinase [2]. Previously, we have discovered potent HCK
inhibitors by in silico screen together with a high -throughput enzyme inhibition assay. In addition,
we have solved several crystal structures of HCK with these inhibitors [3].
On the hypothesis that a dual inhibitor of HCK and FLT3 can be a better medicine for AML, wehave selected two pyrrolo-pyrimidine compounds which displayed inhibitory effect against FLT3
in addition to HCK, and solved co-crystal structures of FLT3 with these compounds. Bothcompounds bound to the inactive state of FLT3 with the activation loop stabilized in the closed
conformation. By combining structure determination with structure-activity relationship analysis(SAR), we were able to determine the binding mode s of these compounds for both HCK and FLT3,
which can explain their inhibitory potency. These findings can contribute to further developmentof a dual inhibitor of HCK and FLT3, including many AML-associated FLT3 mutants.
This work was supported by the RIKEN Program for Drug Discovery and Medical Technology
Platforms (DMP).

[1] Saito, Y., Uchida, N., Tanaka, S., Suzuki, N., Tomizawa-Murasawa, M., Sone, A., Najima, Y.,
Takagi, S., Aoki, Y., Wake, A., Taniguchi, S., Shultz, L. D., Ishikawa, F., Induction of cell cycle
entry eliminates human leukemia stem cells in a mouse model of AML., Nat Biotechnol 2010, 28,275.
[2] Levis, M., Small, D., FLT3: ITDoes matter in leukemia., Leukemia 2003, 17(9):1738–1752.
[3] Saito, Y1., Yuki, H., Kuratani, M., Hashizume, Y., Takagi, S., Honma, T., Tanaka, A., Shirouzu,
M., Mikuni, J., Handa, N., Ogahara, I., Sone, A., Najima, Y., Tomabechi, Y., Wakiyama, M.,
Uchida, N., Tomizawa-Murasawa, M., Kaneko, A., Tanaka, S., Suzuki, N., Kajita, H., Aoki, Y.,
Ohara, O., Shultz, LD., Fukami, T., Goto, T., Taniguchi, S., Yokoyama, S., Ishikawa, F., A
pyrrolo-pyrimidine derivative targets human primary AML stem cells in vivo. , Sci Transl Med.
2013 Apr 17;5(181):181ra52.

P5-26
Nanoscale Investigation of mRNA
Localization and Dynamics in Stress Granules
Ko Sugawara 1

Kohki Okabe 1, 2

sugawara@mol.f.u-tokyo.ac.jp

okabe@mol.f.u-tokyo.ac.jp

Takashi Funatsu 1
funatsu@mol.f.u-tokyo.ac.jp

12

Graduate School of Pharmaceutical Sciences, University of Tokyo, 7-3-1
Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
JST, PRESTO, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Keywords: mRNA, Stress granules, Super-resolution fluorescence microscopy, Single particle
tracking
mRNA serves as a key molecule for the gene expression in cells, conveying the genetic
information from DNA to the ribosomes. To control the quality of this process, eukaryotic mRNA
is regulated in various steps, including modification, localization, translatio n and degradation. In
response to the environmental stresses, cytoplasmic mRNAs and proteins dynamically gather toform granular structures called stress granules (SGs). Although it is identified that the main
components of SGs are mRNAs stalled in translation initiation, translation initiation factors andRNA binding proteins, the localization and dynamics of them remain unclear.
In this study, we investigated the spatio-temporal regulation of mRNAs in mammalian SGs at thenanometer scale by combination of super-resolution fluorescence microscopy and single particle
tracking technique. Fluorescently labeled antisense oligonucleotides are attached to the target
mRNAs to image the endogenous mRNAs.
Super-resolution microscopy revealed that mRNAs inside SGs distribute in heterogeneous manner,in which they form both high and low concentration domains. Based on the clustering analysis onhigh concentration domains, we found that the number of clusters increase with a rise in SGs size
while they maintain the same size (~70 nm in diameter). Single particle tracking of endogenous
mRNAs inside SGs showed that the mobility of mRNAs is decreased inside SGs compared to in thecytosol. Interestingly, a part of observed mRNAs was freely diffusive, suggesting that the
dynamics of mRNAs inside SGs is also heterogeneous as we confirmed in the localization analysis.
To investigate the relationship between the localization and dynamics of mRNA s inside SGs, we
combined the above two techniques in living cells. As a result, we observed mRNAs anchored to
the nanoscale clusters.
Based on these investigations, we hypothesize that SGs are not just random aggregations but are
well-organized structures that contain the high concentration domains with low mobility and the
low concentration domains with high mobility.
[1] Anderson, P. and Kedersha, N., RNA granules: post-transcriptional and epigenetic modulatorsof gene expression, Nature Reviews Molecular Cell Biology, 10:430-436, 2009.
[2] Zhang, J., Okabe, K., Tani, T., and Funatsu, T., Dynamic association-dissociation and harboringof endogenous mRNAs in stress granules, Journal of Cell Science, 124:4087-4095, 2011.
[3] Rust, M. J., Bates, M., and Zhuang, X., Sub-diffraction-limit imaging by stochastic opticalreconstruction microscopy (STORM), Nature Methods, 3:793-795, 2006.

P5-27
Control of DNA hybridization by Electric fields

1

Keitel Cervantes-Salguero 1

Ibuki Kawamata 1

cervantes@molbot.mech.tohoku.ac.jp

kawamata@molbot.mech.tohoku.ac.jp

Shin-ichiro M. Nomura 1

Satoshi Murata 1

nomura@molbot.mech.tohoku.ac.jp

murata@molbot.mech.tohoku.ac.jp

Department of Robotics, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai,
Miyagi 980-8579, Japan

Keywords: Electronic structure calculation, Ligand docking, Protein modelingControl of the DNA hybridization is important for realizing molecular mechanisms and artifacts
based on DNA. Electric-driven melting of DNA fixed to conductive surfaces is a promising method
because the melting requires short time; however, in such electrochemical experiments there arefactors such as pH variability and Joule heating that may affect the DNA dissociation. To enable
pure electric control, we need to exclude those factors.
Here, we developed an experimental setup to electrically control the hybridization of DNA
nanostructures to DNA hydrogels, while suppressing changes of pH and temperature. The DNA gel,which was incorporated in a capillary tube, was placed between platinum electrodes connected to a
power source. When applying a strong electric-field, the nanostructure dissociates from the gel anddiffuses. Re-association to the gel was also possible by applying an inverse weaker electric -field.
This repeatable hybridization control may be used for realizing reusable DNA devices andcomputers.
[1] Sosnowski, R. G., et al. Rapid determination of single base mismatch mutations in DNA hybrids
by direct electric field control, Proc. Natl. Acad. Sci. USA, Vol. 94, pp. 1119-1123, 1997,
Biochemistry.
[2] Johnson, R.P., et al. Denaturation of dsDNA immobilized at a negatively charged gold electrodeis not caused by electrostatic repulsion, Chem. Sci., 2013, 4, 1625.

P5-28Controlling diffusion coefficient in gelatinous mediumby using DNA strand displacement
Thanapop Rodjanapanyakul 1

Keitel Cervantes-Salguero 1

thanapop@molbot.mech.tohoku.ac.jp

cervantes@molbot.mech.tohoku.ac.jp

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

1

Satoshimurata@molbot.mech.tohoku.ac.jp

Department of Robotics, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai,
Miyagi 980-8579, Japan

Keywords: Pattern formation, Reaction-diffusion mechanism, Programmable chemical reaction
Pattern formation, which is ubiquitous in chemistry and biology, is modeled by reaction -diffusion
mechanism. Based on such models, it has been tried to synthesize artificial patterns by using DNA
nanotechnology. In this context, it is possible to program the reaction network by designing DNA
sequences, while controlling the diffusion coefficient requires to change the molecular weights andthe medium in general.
Here, we propose a new method to control the diffusion coefficient of a “target” DNA molecule ina gel medium by using an additional DNA strand called “competitor”. Target” is complementary to“template” DNA which is immobilized to a hydrogel. Upon the addition of “competitor”, the“competitor” binds to “template” via a short region called toehold so that the “target” is displaced.
Eventually, the diffusion coefficient of the “target” increases by the competition of hybridization
to the “template” between “target” and “competitor”.
We found that the diffusion coefficient of the “target” can increase up to 3 folds by changing theconcentration of the “competitor”, ranging from ~7.5 µm 2 /s to ~12 µm 2 /s.
[1] Waigh, T.A., Applied Biophysics: A Molecular Approach for Physical Scientists , John Wiley &Sons, 2007.
[2] Naghash, H.J., Okay, O., Formation and Structure of Polyacrylamide Gels , Journal of Applied
Polymer Science, 60(7):971-979, 1996.
[3] Sprague, B.L., McNally, J.G., FRAP analysis of binding: proper and fitting, Trends in Cell
Biology, 15(2):84-91, 2005.
[4] Soumpasis D.M., Theoretical analysis of fluorescence photobleaching recovery experiments ,Biophysical Journal, 41(1):95-97, 1983.

P5-29Simulation of DNA Nanostructure Self-assembly by using
Game Engines
Theo Dammaretz1
theo.dammaretz@etu.esisar.grenoble-inp.fr

Ibuki Kawamata1
kawamata@molbot.mech.tohoku.ac.jp

1

Shiyun Liu1
hikari@molbot.mech.tohoku.ac.jp

Satoshi Murata1
murata@molbot.mech.tohoku.ac.jp

Department of Robotics, Graduate School of Engineering, Tohoku University, 6-6-01, Aoba AramakiAza Aoba-ku, Sendai, 980-8579, Japan

Keywords: Physical Engines, Molecular Simulation, DNA Nanotechnology, Molecular
Robotics
Simulation of self-assembly process of DNA nanostructure is important for estimating themolecular dynamic properties. However, the simulation is difficult due to the enormous numberof atoms and the dynamic process. Here, we proposed a method to simulate the self-assembly of
DNA nanostructure utilizing game engines for optimizing the design of dynamic 3D molecules.
We developed coarse grained models to simulate the self-assembly processes of two different
molecules. The first model corresponds to a DNA origami monomer to control the size of multimers based on a Vernier mechanism. The second model corresponds to a DNA monomer whichforms a membrane.
We implemented the models as a molecular dynamics software utilizing the game engine [1].
Typical game engine consists of physical engines, objects handling, user interfaces and event
generation. We successfully simulated the self-assembly process and analyzed the time
evolution. Compared to the conventional method such as all-atom simulation, the proposed
software enables us to estimate the molecular dynamic properties of DNA nanostructure more
efficiently.
[1] Unity: http://unity3d.com/[2] Ken Dill, Sarian Bromberg. Molecular Driving Forces: Statistical Thermodynamics in Chemistry
and Biology. Garland Science, 2003.
[3] Team Sendai. Size-controllable Homomultimer. http://teamsendai.github.io/. BIOMOD, 2015

P5-30Membrane-like structure made of DNA motifs
Yuma Endo 1

Takeo Uchida 1

Ibuki Kawamata 1{endoy, uchida, kawamata}@molbot.mech.tohoku.ac.jp

Yuki Suzuki 1

Shin-ichiro M. Nomura 1

Satoshi Murata 1{ysuzuki79r, nomura, murata}@molbot.mech.tohoku.ac.jp

1

Tohoku University, 6-6-01, AramakiAzaAoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

Keywords: DNA membrane, DNA motif, DNA structure, Molecular Robotics, Lipid bilayer
In nature, lipid bilayer forms a membrane structure that serves as a continuous barrier between
cells and their surroundings. Similarly, in the field of molecular robotics, a vesicle made of lipid
bilayer is commonly used as a container encapsulating molecular circuits and integrating otherdevices [1,2].
Here, we propose a new membrane structure assembled with well -designed DNA motifs. ThisDNA membrane consists of 3 kinds of DNA motifs (double -Φ motifs) with different basesequences at their four connecting parts. These motifs connect with each other by the hybridizationof connecting parts to form a membrane structure. Moreover, melting temperature (25 ±1℃) of theconnecting parts is expected to give liquidity of the membrane like a lipid bilayer under room
temperature.
The membrane made of DNA is supposed to be more useful for molecular robotics compared tolipid bilayer due to the programmability of DNA sequence. In the poster presentation, we will
discuss the experimental results of the DNA membrane.
[1] Murata, S., Konagaya, A., Kobayashi, S., Saito, H., Hagiya, M., Molecular Robotics: A New
Paradigm for Artifacts, New Generation Computing, 31:27-45, 2013[2] Hagiya, M., Konagaya, A., Kobayashi, S., Saito, H., and Murata, S., Molecular Robots with
Sensors and Intelligence, Accounts of Chemical Research, 47:1681-1690, 2014.