10月26日(火) ~28日(木)Remo 会場

Poster Presentation
List of Poster Presentation ................1
Remo Floor Guide...............................5
Poster Abstracts .................................9

PosterViewing
October 26 (Tue)

12:30-19:00

October 27 (Wed)

12:30-19:00

October 28 (Thu)

12:30-14:30

Presentation Core Time
Duty time
Free time
17:00-18:00 (odd#)
18:00-19:00 (even#)
12:30-17:00
17:00-18:00 (even#)
18:00-19:00 (odd#)
Award winning posters only

Category (Topics)
1. 計算化学（分子計算）：Computational Chemistry (Molecular Modeling)
2. 計算化学（分子認識）：Computational Chemistry (Molecular Recognition)
3. データサイエンス：Data Science
4. 構造相関解析 ：Structure Activity Relationship
5. 構造生命科学 ：Structural Bioscience
6. ADME・毒性 ：ADMET
7. バイオインフォマティックス ：Bioinformatics
8. 新型コロナウィルス対策と感染症対策 ：Against COVID-19 and Infectious Diseases
9. 創薬応用 ：Drug Discovery Application
10. 臨床応用 ：Clinical Application
11. レギュラトリサイエンス ：Regulatory Science
12. 分子ロボティクス ：Molecular Robotics
13. その他 ：Others

<List of Poster Presentation>
Category No. Poster No.

P01-01(F-2)
Remo Floor
No.

Title

First Author

Affiliation

Maho Takahashi

Yokohama City Univ.

Hiroaki Hata

Mitsui Knowledge
Industry Co., LTD

MayukoNakagawa

Yokohama City Univ.

Application of visualization of the
P01-04 interfacial electrostatic complementarity(F-2) (VIINEC) to antibodies targeting PD1/PD-L1 interaction

Hiroki Ozono

Department ofChemistry,
Biotechnology, andChemicalEngineering,
Graduate School of
Science andEngineering,Kagoshima University

P01-05 Advanced methods to predict conformers(F-2) of cyclic peptides accurately by FRECCA

Sakiko Mori

Fujitsu Ltd

Akari Ito

Graduate School of
Medical Life Science,
Yokohama City
University

Dynamics of Orexin2 Receptor and GP01-07
protein Complex with Molecular(F-2)
Dynamics Simulations

Shun Yokoi

Department of
Physics, School of
Science andTechnology, Meiji
University

P01-08 Transport simulation of human LAT1(F-2) CD98hc complex

Natsumi Yoshida

Dept. of Med. Life
Sci., Yokohama City
Univ.

Floor 2
Elucidation of binding interactions
P01-01
between neurotrophin receptor TrkA
(F-2)
domain 5 and its binding peptide
QAEmap: Validation of low-resolution
P01-02protein crystal structures using deep
(F-2)
learning
P01-03 Interaction analysis of cyclic artificial ion(F-2) channel AmFF

P01-06 Comparison of molecular dynamics of(F-2) cyclosporin A and cyclosporin E

Activation Pathway of IL-2-inducible T
P01-09cell kinase Explored by Tree-Search
Yukina Nakai(F-2)
Molecular Dynamics
Floor 3

Yokohama City
University

P01-10 Molecular dynamics simulations of(F-3) GABAA receptor and general anesthetics

Shuya Nakata

Graduate School of
System Informatics,
Kobe University

P01-11 Free energy analysis of co-solvent effect(F-3) on insulin dimer dissociation

Simon Hikiri

Graduate School ofEngineering Science
Osaka University

MDContactCom: A tool to identify
P01-12 differences of protein molecular dynamics(F-3) from two MD simulation trajectories interms of residue-residue contacts
P01-13 Molecular Dynamics Simulations of DNA(F-3) ExonucleaseP02-01 Insight into Allosteric ERK2 Inhibitors by(F-3) using Fragment Molecular Orbital Method
Dynamic Residue Interaction Network
P02-02 Analysis of Secondary Mutations in(F-3) Protease that Promote Drug Resistance in
HIV-1
Dynamic Residue Interaction Network
P02-03
Analysis of Primary Mutations in Protease(F-3)
that Promote Drug Resistancein HIV-1
Study of the Binding Mechanism of
P02-04
Inhibitor to InterLeukin-2 by Free Energy(F-3)
Calculations
Dynamic FMO analysis for RNA sequence
P02-05
specificity in inhibitor recognition of(F-3)
translation initiation factor complex

Chie Motono

Cellular and Molecular
Biotechnology
Research Institute,
National Institute ofAdvanced Industrial
Science andTechnology (AIST)

Hiroki Otaki

Nagasaki University

HajimeSugiyama

Mitsubishi Chemical
Corporation

Ayaka Ojima

Chiba Institute ofTechnology

Ryoga Miyawaki

Chiba Institute ofTechnology

Noritaka Inoue

Schrödinger KK

Yuma Handa

School of Pharmacy
and Pharmaceutical
Sciences, Hoshi
University

Floor 4
P03-01 Development of in silico prediction method
Saki Katayama(F-4) for idiosyncratic drug-induced liver injury

Graduate School of
Pharmaceutical
Sciences, Nagoya City
University

Machine Learning-based Evaluation of
P03-02
Microscopic Images of Fukushima Patient- Kosuke Takagi(F-4)
derived organoids

Core Tech andStrategy Olympus
Corporation

A customizable benchmark dataset for
P03-03
evaluation of structure-based protein(F-4)
ligand binding prediction

Xiaoyao Zhang

Department ofComputer Science,
School of Computing,Tokyo Institute ofTechnology

P03-04 Design and Implementation of New(F-4) Methods for Odor Molecule Generation

Sijie Zhang

School of Computing,Tokyo Institute ofTechnology

Kyosuke Sato

Graduate School of
Natural Science andTechnology, Okayama
University

Improvement of Variational Auto-Encoder
P03-05
Design of Organic Molecules by(F-4)
Discriminator

P03-06 Appropriate Evaluation Measurements for
Tsuyoshi Esaki(F-4) Regression Models
Effect of integrating HTS prediction
P03-07
Shumpei
outputs on ability of fingerprints to(F-4)
Nemotodescribe biological properties of chemicals

2

Shiga University
The University ofTokyo

Development of a Prediction Method for
P03-08
Protein Cryptic Sites Using Machine(F-4)
Learning
Gentamicin-induced hearing loss: a
P11-01
toxicological study using drug−gene(F-4)
network analysis
Floor 5
Rational Elucidation of Weak Interaction
P04-01in Protein, A Model Study Utilized Copper(F-5)
ProteinDevelopment of Machine-learning based
P04-02
Prediction model for hERG inhibitory(F-5)
activities by chemical compounds
Analyzing Deep Neural Networks on
P04-03
Molecular Activity Prediction and(F-5)
Characteristics of the Created Network
Development of Biologically Interpretable
Prediction Models for Drug-Induced Liver
P06-01 Malignant Tumors Based on the Activity of(F-5) Molecular Initiating Events Using FAERS:
a Self-Reported Adverse Drug Events
Database
P05-01 Molecular basis of allosteric ERK2
(F-5) inhibitors discovered by in silico screening

Masahito
KumadaThe University ofTokyo

Mizuki Tanaka

Laboratory of Drug
Informatics, Gifu
Pharmaceutical
University

Takamitsu
Kohzuma

Ibaraki University

KazuakiIshihara

Yokohama Universityof Pharmacy

Yoshiki Kato

Toyohashi Univ. of
Tech.

Kota Kurosaki

Department of Medical
Molecular Informatics,
Meiji Pharmaceutical
University

Mayu Yoshida

Osaka Prefecture
University
RIKEN Center for

P05-02 Development of FMODB and recent(F-5) updates, 2021

Daisuke Takaya Biosystems Dynamics
Research

P05-03 Visualization of Antigen Binding on DNA(F-5) Origami Using Atomic Force Microscopy
P12-01 Degradation of DNA ribbons with
(F-5) complementary annihilation
Floor 6
P07-01 A gene set analysis method based on(F-6) network summary values

Yuki Minamide

Department ofChemistry andMaterial Engineering
Kansai University

Hiroki Sawada

Department of
Robotics, Graduate
School of Engineering,Tohoku University

Takashi Yoneya

Kyowa Kirin Co., Ltd.

Comparison of gene co-expression inference
P07-02
methods based on biological validity andIori Azuma(F-6)
diversity

Laboratory of
Molecular
Pharmacokinetics,
Graduate School of
Pharmaceutical
Sciences, The
University of Tokyo

P07-03 Analysis and visualization of single-cell(F-6) RNA sequencing data by Partek Flow

MOLSIS Inc.

Naofumi Seira

Predicting phase separation protein and its
P07-04experimental condition using machine
Ka Yin Chin
(F-6)
learning

3

Graduate School of
Medical Life Science,
Yokohama City
University

Dynamical FMO Interaction Analysis of
P08-01SARS-CoV-2 RNA dependent RNA(F-6)
polymerase and Remdesivir
P09-01 infiniSee - Similarity Searching in Ultra(F-6) Large Chemical Spaces
Development of SSDB Software
P09-02 Considering Empirical Scores with
(F-6) Hydrophobic Heteromeric Interactions toReproduce the ChEMBL Database

Shuhei
Miyakawa

Hoshi Univ.

Takashi Ikegami MOLSIS Inc.
Department of
Biological Sciences,
Graduate School of
Akihiro Masuda
Science andEngineering, Chuo
University

Identification of bitter-related compounds
P09-03against hT2R14 by structure-based virtual Kohei Kuriki(F-6)
screening

Kyushu Institute ofTechnology

A computational study for the development
P09-04
Daisukeof anti-prion compounds using the docking(F-6)
Arimurasimulation

Department ofChemistry,
Biotechnology, andChemical Engineering,
Graduate School of
Science andEngineering,Kagoshima University

Floor 7
Evaluation of Estrogen Receptor-Ligand
P09-05
Binding Properties Using Dynamical FMO Arisa Komiya(F-7)
AnalysisInteraction Analysis of GPCR in the
P09-06
Presence of Lipid Bilayer Using Fragment Taro Imahiro(F-7)
Molecular Orbital (FMO) Method
Evaluation of binding property of
P09-07
nitrocatechol inhibitors with COMT by the Satoko Shirai(F-7)
fragment molecular orbital method
P09-08 Inhibitor discovery targeting UHRF1 by(F-7) MD simulations and biochemical analyses

P09-09 Rational design of a helical peptide(F-7) inhibitor targeting c-Myb–KIX interaction
P09-10 PPAR subtype-dependent ligand binding(F-7) investigated by docking simulation study
In silico-guided design of growth factor
P13-01mutants with varied physicochemical(F-7)
parameters
P13-02 Application of Scilligence Software for(F-7) Peptide Drug Discovery

4

Hoshi University

Hoshi University

Hoshi Univ.

Graduate School of
Medical Life Science,
Yuki Shibahashi
Yokohama City
University

Shunji Suetaka

Department of Life
Sciences, Graduate
School of Arts andSciences, The
University of Tokyo

Yuka Yamashita

Grad. Sch. of Sci. &Tech., Shinshu Univ.

Yuga Okada

The University ofTokyo

Yasuhiro
Shinozaki

MOLSIS Inc.

Remo Venue Floor Guide【FLOOR GUIDE】 Poster Presentations / Enterprise Sessions (ES)

Floor 1

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P01-03(F-2)

P01-04(F-2)

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P01-07(F-2)
5

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ES-02(F-2)௻ᴗࢭࢵࢩࣙࣥ
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Floor 3

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P11-01(F-4)

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Floor8 and Floor 9 : Free【FLOOR GUIDE】 Poster Presentations / Enterprise Sessions (ES)

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P01-01(F-2)

Elucidation of binding interactions between neurotrophin
receptor TrkA domain 5 and its binding peptide
Maho Takahashi1
W205425d@yokohama-cu.ac.jp

Tsutomu Yamane 2
Tsutomu.yamane@riken.jp

12Toru Ekimoto1
ekimoto@yokohama-cu.ac.jp

Hideo Takahashi1
hidtak@yokohama-cu.ac.jp

Rika Suzuki1
rks0723@yokohama-cu.ac.jp

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

Yokohama City Univ., 1-7-29 Suehirocho, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-0045,
Japan
R-CCS, RIKEN, 1-7-22, Suehirocho, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-0045, Japan

Keywords: Molecular dynamics simulation, Ligand docking, Peptide modeling
The cell surface receptor tyrosine kinase A domain 5 (TrkAd5) is one of the neurotrophin receptors
and specifically binds to nerve growth factor (NGF). Developments of NGF inhibitors targeting
TrkAd5-NGF interaction are demanded for neurodegenerative diseases. As a candidate of theinhibitor, we discovered the TrkA-binding peptide 1 (TP1) using the phage display method. In thisstudy, to proceed a rational design of the peptide ligands, such as TP1, the molecular recognition
mechanism of TP1 was analyzed by a hybrid analysis of NMR method and all-atom molecular
dynamics (MD) simulation. According to a chemical shift (CS) perturbation experiment, the TP1binding site of TrkAd4 was identified. However, the structure of TrkAd5-TP1 complex was not
solved, and the interaction mode between them was not understood structurally.
To clarify the binding pose of TP1, the binding mode was searched using conventional MD
simulations and the enhanced sampling method, i.e., replica-exchange with solute tempering(REST) [1]. In the binding pose sampled by the conventional MD simulations, only one dominantpose was observed, and however, the binding pose could not explain the cause of the CS change
observed in the edge region of the binding surface. In contrast, another new binding pose was
sampled by REST, and the interaction pattern of the new pose could explain the CS data at the edge
region. Consequently, two binding poses sampled by REST were stable, and their interaction modes
were in good agreement with the experimental CS data. In both binding poses, the central part of
TP1 was stuck in a hydrophobic hole at the binding surface, and we found that the rigidity of thecentral part, where the conformation was limited by proline, was important for the TP1 binding.
[1] M. Kamiya and Y. Sugita, Flexible selection of the solute region in replica exchange with solutetempering: Application to protein-folding simulations, Journal of Chemical Physics, 2018, 149, 0723041-11.

9

P01-02(F-2)

QAEmap: Validation of low-resolution protein crystal
structures using deep learning
Hiroaki Hata 1

Miwa Sato 1

hata-hiroaki@mki.co.jp

sato-miwa@mki.co.jp

Akiko Kashima 2
kashima.akiko@mv.mt-pharma.co.jp

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

Biao Ma 3

Sigeyuki Matsumoto 4

biao.ma@riken.jp

sigeyuki.matsumoto@riken.jp

Atsushi Tokuhisa 4

Masateru Ohta 5

Mitsunori Ikeguchi 6

tokuhisa@riken.jp

masateru.ota@riken.jp

ike@tsurumi.yokohama-cu.ac.jp

1
2
3

4

5
6

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

Keywords: Protein structure, Deep learning, X-ray crystallography
X-ray crystallography has been dominantly used to elucidate protein structures, whichcorresponds to ~90% of protein structures registered in the Protein Data Bank (PDB). However,resolutions of the protein structures are not always high enough for detailed analysis of molecularinteractions. For drug discovery or simulation studies, the resolution of 2 Å or higher is necessary[1], while only ~40% of the PDB entities have resolutions higher than 2 Å [2].
In order to refine the low-resolution structures, we applied 3D-convolutional neural network forevaluation of protein structures determined by X-ray crystallography [3]. Our method, quality
assessment based on an electron density map ( QAEmap), estimates how well the structure fits theputative high-resolution structure by calculating a correlation between the coordinate structure tobe evaluated and the electron density map of the correct structure . The low-resolution structure canbe refined by modifying the structure to maximize the correlation.
[Acknowledgement] Part of this research was carried out as activities of Life Intelligence
Consortium (LINC).
[1] Ilatovskiy, A. V. & Abagyan, R. Computational Structural Biology for Drug Discovery. in
Structural Biology in Drug Discovery 347–361 (John Wiley & Sons, Ltd, 2020).
doi:10.1002/9781118681121.ch15.
[2] Burley, S. K. et al. Protein Data Bank: The single global archive for 3D
macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2019).
[3] Miyaguchi, I. et al. QAEmap: A Novel Local Quality Assessment Method for Protein Crystal
Structures Using Machine Learning. (2021) doi:10.21203/RS.3.RS -687363/V1.

10

P01-03(F-2)

Interaction analysis of cyclic artificial ion channel AmFF
Mayuko Nakagawa 1

Tsutomu Yamane 2Toru Ekimoto 1

w215421d@yokohama-cu.ac.jp

tsutomu.yamane@riken.jp

ekimoto@yokohama-cu.ac.jp

Kohei Sato 3

Kazushi Kinbara 3

Mitsunori Ikeguchi 1,2

koheisato@bio.titech.ac.jp

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

ike@yokohama-cu.ac.jp

1

2
3

Yokohama City Univ., 1-7-29 Suehiro-chou, Tsurumi-ku, Yokohama-shi, Kanagawa,230-0045,
Japan
RIKEN, 1-7-22 Suehiro-chou, Tsurumi-ku, Yokohama-shi, Kanagawa,230-0045, JapanTokyo Inst. of Tech., 4259 Nagatsuda-chou, Midori-ku, Tokyo,226-8503, Japan

Keywords: Quantum chemical calculation, Density Functional Theory, Artificial ion channel
The development of artificial molecular machines mimicking biomolecular functions has been
advanced by Kinbara et al. For example, an artificial ion channel 2mer [1] exhibits
agonist/antagonist-dependent ion permeation, and an artificial ionophore IMA [2] transports anions
across a membrane. To construct a guideline for designing artificial molecular machines rationally, we
have analyzed the mechanism of the ion permeation of 2mer and IMA from molecular dynamics
simulations and intermolecular interaction analyses. Recently, the cyclic artificial ion channel AmFF
has been developed by Kinbara et al. According to experimental results, AmFF transports cations
across the membrane and has potassium selectivity. AmFF is a cyclic molecule consisting of a linear"core part" and a flexible PEG part, and both ends of the two core parts are connected by PEG part. The
core part consists of three fluorinated aromatic rings connected by triple bonds, and then the core partis rigid. From experimental results on ion permeability, AmFF forms dimer conformation, and the pore
formed by the dimer is stable because of the steady current flow.
To understand the pore structure of AmFF at atomic resolution and the interaction mode with cations
and water in the pore, interaction analyses were performed using molecular modeling and quantum
mechanics calculations. The dimer of AmFF corresponds to the tetramer of the core part. We
developed several tetramer models: the four core parts were located symmetrically, and the orientationsof the aromatic rings in the core part were set to facing or perpendicular. From structural optimizationof each model, a tetramer model showed a stable conformation. Interestingly, the four core parts were
not located symmetrically and had a structure that was offset by one aromatic ring. Comparing thestable tetramer model with the crystal structure of a compound similar to the core part, the tetramer
model was in good agreement with the crystal structure. Using the tetramer model, the stability of thepore was examined by expanding pore size. The pore was stable even when the pore was expanded tothe size of cations and water. In addition, to clarify stable spots of cations and water in the pore,
single-point calculations were performed by placing a cation and a water molecule in the characteristic
parts of the tetramer model. According to the energy profiles, the location of the cation wasenergetically stable at the central part of the pore and at the triple bond part near the entrance. In
contrast, the energetically stable spots of the water molecule were at the places where the cation was
not energetically stable, indicating that the stable spots of the cation and water molecules werecomplementary. In the future, we would like to analyze the dynamics by conducting QM/MM anddeveloping a suitable force field that reflects the interaction mode.

[1] Muraoka, T.; Noguchi, D.; Kasai, S. R.; Sato, K.; Sasaki, R.; Tabata, V. K.; Ekimoto, T.;
Ikeguchi, M.; Kamagata, K.; Hoshino, N.; Noji, H.; Akutagawa, T.; Ichimura, K.; Kinbara, K. A
synthetic ion channel with anisotropic ligand response, Nature Communications, 2020, 11(1),2924.
[2] Mori, M.; Sato, K.; Ekimoto, T.; Okumura, S.; Ikeguchi, M.; Tabata, V. K.; Noji, H.; Kinbara,
K. Imidazolinium-based Multiblock Amphiphile as Transmembrane Anion Transporter,
Chemistry – An Asian Journal, 2021, 16,147-157.

11

P01-04(F-2)

Application of visualization of the interfacial electrostatic
complementarity (VIINEC) to antibodies targeting
PD-1/PD-L1 interaction

1

Hiroki Ozono 1

Takeshi Ishikawa 1

k8340102@kadai.jp

ishi@cb.kagoshima-u.ac.jp

Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of
Science and Engineering, Kagoshima University, 1 -21-40 Korimoto, Kagoshima, Kagoshima
890-0065, Japan

Keywords: VIINEC , Protein–Protein interaction, Fragment molecular orbital method
Analysis of protein-protein interactions (PPI) is an important issue for the development of antibody
drugs. Recently, we have developed a method named interfacial electrostatic complementarity(VIINEC) [1,2], which is based on the ab initio fragment molecular orbital (FMO) method. In thismethod, electrostatic complementarity at the protein–protein interface can be visually analyzed using
the electron density and electrostatic potential (ESP) obtained from the FMO calculations for thecomplex. We expect that VIINEC becomes a useful tool for the rational design of antibody drugs.
In this study, we applied VIINEC to seven antibodies that inhibit the interaction between
programmed cell death-1 (PD-1) and its ligand (PD-L1), which are known to be key molecules in the
immunotherapy of cancer. Two antibodies (nivolumab and pembrolizumab) are targeting PD-1, and five
antibodies (atezolizumab, avelumab, durvalumab, KN035, and BMS-936559) are targeting PD-L1. As
an illustrative example, ESP maps of PD-1 and nivolumab at the interface of them in Figure 1-a, whichclearly shows that a high degree of electrostatic complementarity is made. The electrostatic
complementarity can be quantified using R - value [2]. In this case, the R - value was 0.9106, meaningthat 91.06% of the total electrostatic interaction was attractive. Furthermore, the ΔR - value [2], whichis calculated from the R - value, provides the degree of the contribution of each amino acid to theelectrostatic complementarity. In Figure 1-b, ΔR - values are represented for amino acid residues locatedat the interface together with the inter fragment interaction energies (IFIEs). The overall trend of the
ΔR - values was similar to that of the IFIE analysis. The results for the other antibodies will be given in
our presentation, demonstrating the potential of VIINEC for rational antibody designs.

Figure 1: a) ESP maps for PD-1/nivolumab, b) ΔR - values together with the IFIEs[1] T. Ishikawa, A novel method for analysis of the electrostatic complementarity of protein-protein
interaction based on fragment molecular orbital method, Chem. Phys. Lett., 761 (2020) 138103.
[2] H. Ozono, T. Ishikawa, Visualization of the interfacial electrostatic complementarity: A method foranalysis of protein–protein interaction based on ab initio quantum chemical calculations, J. Chem.
Theory Comput. in press.

12P01-05(F-2)

Advanced methods to predict conformers ofcyclic peptides accurately by FRECCA
Sakiko Mori 1

Kentaro Takai 1

mori.sakiko@fujitsu.com

takai.kentaro@fujitsu.com

Azuma Matsuura
1

Yusuke Miki 1

Seiji Fukui 1
fukui.seiji@fujitsu.com

Atsushi Tomonaga

Fujitsu Ltd, 1-5 Omiya-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0014, Japan

Keywords: Conformation analysis, Cyclic peptides
Cyclic peptides, one of the middle-sized molecules, represent attracting drug modality thatcombines the benefits of small molecules, e.g., lower production cost and the potential to target
intracellular proteins, and antibodies, e.g., higher selectivity and lower toxicity. However, it isdifficult to recapitulate the possible conformers of cyclic peptides because a systematic method to
explore their large conformational space has not yet been established. In order to acceleraterational design of cyclic peptides, we are developing the conformation analysis technique forcyclic peptides named FRECCA (Fujitsu Ring Expansion method of Cyclic peptide Conformation
Analysis) [1-3].
To confirm the performance of our technique,
we analyzed 16 cyclic peptides of 4-11 residues(registered in Protein Data Bank [4] andCambridge Structural Database [5]) andcompared our results with those of Sindhikara etal. [6]. The minimum value of RMSD calculated
for all heavy atoms to the measured structure wasused as a measure of accuracy.
Figure 1 shows the extent to which obtained
conformers by each method contain ones close to
the measured structures. Our results of theminimum RMSD (labeled “FJ”) for all peptides
were less than 2.0 angstroms with the measured
structures. Details will be explained in thepresentation.
We achieved the accurate conformation analysis
Figure 1: The minimum RMSD (heavy atoms)
for 16 peptides of 4-11 residues. For the actual
between obtained conformers by each method
use of the drug discovery process, we are
and 16 measured structures are summarized
accelerating our technique.
by the number of backbone atoms.

[1] Tomonaga, A.; Ueda, A.; Matsuura, A. 254th ACS National Meeting & Exposition (2017/8)[2] Tomonaga, A.; Sugiyama, H.; Ueda, A. WO2018/154789A1.
[3] Tomonaga, A.; Sugiyama, H.; Ueda, A. JP2019/179304A.
[4] https://www.rcsb.org/[5] https://www.ccdc.cam.ac.uk/[6] Sindhikara, D.; Spronk, S. A.; Day, T.; et al., J. Chem. Inf. Model., 2017, 57(8), 1881-1894.

13

P01-06(F-2)

Comparison of molecular dynamics of
cyclosporin A and cyclosporin E
Akari Ito 1
W205406f@yokohama-cu.ac.jp

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

Tsutomu Yamane 2
tsutomu.yamane@riken.jp

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

Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan
2
Center for Computational Science, RIKEN, Yokohama 230-0045, Japan.
1

Keywords: Molecular dynamics, Cyclosporin, gREST
Cyclosporin A (CsA) is a naturally derived cyclic peptide used as an immunosuppressant drug.
CsA has high cell-membrane permeability despite its large molecular size. In addition, comparedto cyclosporin E (CsE), which is a metabolite of CsA, CsA has 10 times higher membrane
permeability than CsE, although the only difference between them is in one amino acid:N-methylated VAL in CsA just changes to VAL in CsE [1]. To understand the mechanism of themembrane permeation of CsA and the difference in the membrane permeability between CsA andCsE, structural samplings during the membrane permeation event were executed using all-atom
conventional molecular dynamics (cMD) simulations and the generalized replica-exchange withsolute tempering (gREST) simulations [2]. In cMD simulations, several initial structures of CsA or
CsE embedded in the membrane-water systems were used. Although no membrane-permeation
events occurred during the timescale of several micro-seconds, we found that CsA and CsE tended
to be located at the regions of lipid tail groups. In addition to the open and closed forms thatobserved in crystal structure, intermediate forms were also sampled during the cMD simulations.
To further sample the structural distribution of CsA, gREST simulations were performed with thecenter of mass of CsA fixed in water, at the membrane-water interface, and in the membrane near
the lipid tail groups, which are characteristic locations found in the cMD results. In each location,
the variety of conformations that CsA adopted was different, and the popular conformations
appeared more frequently were also different. In water, the conformation of CsA showed a wide
range of the structural distribution from open to closed-like forms. By contrast, in the interface andmembrane, the variety of the conformation was restricted to the closed-like form.

[1] Ahlbach L C, Lexa W K, Bockus T A, Chen V, Crews P, Jacobson P M, Lokey S R, Future Med.
Chem. 7, 2121-30 (2015).
[2] Motoshi Kamiya, Yuji Sugita, J Chem Phys, 149, 072304 (2018).

14

P01-07(F-2)

Dynamics of Orexin2 Receptor and G-protein Complex
with Molecular Dynamics Simulations

1

Shun Yokoi 1

Ayori Mitsutake 1

ce207021@meiji.ac.jp

ayori@meiji.ac.jp

Department of Physics, School of Science and Technology, Meiji University, 1-1-1,
Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan

Keywords: GPCRs, OX2R, MD simulation, Complex system, Dynamics
Orexin2 receptor (OX2R) is classified as a class A G-protein-coupled receptor (GPCR) andbelongs to the group of orexinergic systems that are composed of two peptide ligands (i.e.,
orexin-A and -B) and two GPCRs (i.e., OX1R and OX2R).[1] The OX2R is involved in theregulation of feeding behavior and sleep-wake rhythm to give some examples.[2-3] Such kinds ofneurological processes are caused by GPCR activation. Through the activation of GPCRs by ligand
binding, GPCRs change their conformation and then downstream signal transduction is carried out
via a trimeric protein called G-protein that consists of three subunits (Gα, Gβ, and Gγ) and via
G-protein-independent pathways through GPCR kinase-mediated phosphorylation and arrestin
coupling.[4] Although the structure of the OX2R has been clarified by structural analysis,[5-6] theatomic-level mechanisms of GPCR activation, G-protein activation, and biased signaling remain
unknown.
Here, we performed and analyzed several microsecond-scale molecular dynamics (MD)simulations of OX2R using AMBER MD software. We performed not only simulations of OX2R,
but also modeled the complex of OX2R and G-protein and performed simulations of the complex.
In the poster, we first show the results of the MD simulations and investigate the dynamics of
OX2R and G-protein complex. Then, we discuss implications for the activation mechanism of
OX2R and for the binding mechanism of G-protein.
[1] Sakurai, T.; et al., Orexins and orexin receptors: A family of hypothalamic neuropeptides and G
protein-coupled receptors that regulate feeding behavior, Cell, 1998, 92, 573 ‒ 585.
[2] Leonard, C. S.; Kukkonen J. P. Orexin/hypocretin receptor signaling: a functional perspective, Br. J.
Pharmacol., 2014, 171, 294 ‒ 313.
[3] Sakurai, T. The neural circuit of orexin (hypocretin): Maintaining sleep and wakefulness, Nat. Rev.
Neurosci., 2007, 8, 171 ‒ 181.
[4] Hilger, D.; et al., Structure and dynamics of GPCR signaling complexes , Nat. Struc. Mol. Biol.,2018, 25, 4 ‒ 12.
[5] Suno, R.; et al., Crystal structures of human orexin 2 receptor bound to the subtype-selective
antagonist EMPA, Structure, 2018, 26, 7 ‒ 19.
[6] Chuan, H.; et al., Structures of active-state orexin receptor2 rationalize peptide and small–moleculeagonist recognition and receptor activation , Nat. Commun., 2021, 12, 815.

15

P01-08(F-2)

Transport simulation of human LAT1-CD98hc complex
Natsumi Yoshida 1

Toru Ekimoto 1

w215436e@yokohama-cu.ac.jp

12

ekimoto@yokohama-cu.ac.jp

Tsutomu Yamane 2

Mitsunori Ikeguchi 1,2

tsutomu.yamane@riken.jp

ike@yokohama-cu.ac.jp

Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan.
Center for Computational Science, RIKEN, 1 -7-22 Suehiro-cho, Tsurumi-ku, Yokohama
230-0045, Japan.

Keywords: Molecular dynamics, Membrane protein, SLC transporter, Amino acid transporter,
Conformational change, Substrate transport
The L-type amino acid transporter 1 (LAT1) has broad substrate recognitions and transports large
neutral amino acids and their analogs across the membrane [1]. Because LAT1 is overexpressed intumor cells and contributes to the supply of essential amino acids [2], the development ofinhibitors is required for the treatment of cancers. Since LAT1 is also expressed in the
blood-brain-barrier [3], understanding the transport mechanism will lead to the development of
new drugs. Therefore, LAT1 is an important pharmaceutical target. LAT1 forms a heterodimer with
CD98 heavy chain (CD98hc), and carries out a substrate transport. Upon the substrate transport,
LAT1 is considered to undergo conformational changes between the outward -facing (OF) and theinward-facing (IF) states [4]. Recently, both the OF and IF conformations have been solved bycryo-EM [5-7], and however, the detailed mechanism underlying the transport coupled with theconformational changes has not been understood well.
To elucidate the transport mechanism coupled with the conformational changes, substratetransport simulations were performed using all -atom molecular dynamics (MD) simulations. We
analyzed the transport mechanism coupled with conformational changes of LAT1 . During thesimulations, the substrate spontaneously moved to the intracellular side coupled with theconformational change.
[1] Uchino, H.; et al., Transport of Amino Acid-Related Compounds Mediated by L-Type Amino
Acid Transporter 1 (LAT1): Insights Into the Mechanisms of Substr ate Recognition. Mol.
Pharmacol. 2002, 61 (4), 729–737.
[2] Kanai, Y.; et al., Expression Cloning and Characterization of a Transporter for Large Neutral
Amino Acids Activated by the Heavy Chain of 4F2 Antigen (CD98). J. Biol. Chem. 1998, 273(37), 23629–23632.
[3] Kageyama, T.; et al., The 4F2hc/LAT1 Complex Transports L-DOPA across the Blood-Brain
Barrier. Brain Res. 2000, 879 (1–2), 115–121.
[4] Drew, D.; Boudker, O. Shared Molecular Mechanisms of Membrane Transporters. Annu. Rev.
Biochem. 2016, 85, 543–572.
[5] Lee, Y.; et al., Cryo-EM Structure of the Human L-Type Amino Acid Transporter 1 in Complex
with Glycoprotein CD98hc. Nat. Struct. Mol. Biol. 2019, 26 (6), 510–517.
[6] Yan, R.; Zhao, X.; Lei, J.; Zhou, Q. Structure of the Human LAT1 –4F2hc Heteromeric Amino
Acid Transporter Complex. Nature 2019, 568 (7750), 127–130.
[7] Yan, R.; et al., Mechanism of Substrate Transport a nd Inhibition of the Human LAT1-4F2hc
Amino Acid Transporter. Cell Discov. 2021, 7 (1), 0–7.

16

P01-09(F-2)

Activation Pathway of IL-2-inducible T-cell kinase Exploredby Tree-Search Molecular Dynamics
Yukina Nakai 1i180482b@yokohama-cu.ac.jp

Naoki Ogawa 2w195501f@yokohama-cu.ac.jp12

3Toru Ekimoto 2
ekimoto@yokohama-cu.ac.jp

Kei Terayama 2
terayama@yokohama-cu.ac.jp

Tsutomu Yamane 3
tsutomu.yamane@riken.jp

Mitsunori Ikeguchi 2,3
ike@yokohama-cu.ac.jp

Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan.
Graduate School of Medical Life Science, Yokohama City University, 1 -7-29 Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan.
Center for Computational Science, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 2300045, Japan.

Keywords: Molecular Dynamics, Conformational Change, Protein Kinase
Interleukin-2 (IL-2) -inducible T cell kinase (ITK) is a non -receptor tyrosine kinase that plays anessential role in T cell receptor signaling [1]. Because ITK also mediates the secretion of Th2
cytokines, including IL-4, IL-5, and IL-13, and regulates the development of effective Th2
responses during allergic asthma and parasitic infections, ITK is considered as a potential
therapeutic target for the treatment of the Th2-mediated inflammatory diseases [1]. Several crystal
structures of the kinase domain of human ITK in the inactive and active states have been solved so
far [1], and show the structural features in the two endpoint states: the unfolded/folded
conformations of A-loop and the inward/outward positions of αC-helix. However, the crystal
structure alone does not provide the details of the activation mechanism coupled with theconformational changes between the endpoint states. In particular, the structural information of theintermediate state is necessary to understand the activation mechanism structurally.
To understand the activation mechanism of ITK, we studied conformational changes between theinactive and active states using all-atom molecular dynamics (MD) simulations. Because the timescale of the conformational change underlying the activation event exceeds the conventional MD
simulations, an enhanced pathway-search method based on MD and a tree-search algorithm, termed
the Tree-Search Molecular Dynamics simulation (TS-MD) [2] was employed for the pathway searchduring the activation event. We constructed evaluation functions that can describe the structuralchanges of αC-helix and A-loop, respectively, and conducted multiple rounds of TS -MD. First, we
conducted TS-MD from the active conformation using an evaluation function that represents only
the conformational change of αC-helix, and succeeded in obtaining the intermediate structure andthe pathway to the intermediate structure, which has the structural features reported for otherkinases. Second, TS-MD from the intermediate structure was conducted using an evaluation
function that represents only the conformational change of A -loop. As a result, we succeeded in
obtaining a pathway leading to a structure close to the inactive conformation. In conclusion, using
TS-MD, the conformational changes during the activation event of ITK were successfully sampled.
[1] Kutach AK et al. Crystal structures of IL-2-inducible T cell kinase complexed with inhibitors:
insights into rational drug design and activity regulation: crystal structures of ITK-inhibitor
complexes. Chemical Biology & Drug Design. 2010, 76(2), 154-163.
[2] Shin K et al. Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search
Molecular Dynamics Simulation Method. ACS Omega. 2019, 4(9), 13853-13862.

17

P01-10(F-3)

Molecular dynamics simulations of GABA A receptor andgeneral anesthetics
Shuya Nakata 1

Yoshiharu Mori 1

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

ymori@landscape.kobe-u.ac.jp

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

1

Graduate School of System Informatics, Kobe University, Japan

Keywords: General anesthetics, Ion channels, Molecular simulation, Molecular dynamics
GABA A receptor is a pentameric ligand-gated ion channel that is a target molecule for several
general anesthetics. Recently, the 3D structures of the GABA A receptor with the generalanesthetics propofol and etomidate bound to the subunit interfaces at the transmembrane domain
were determined by cryo-electron microscopy. (PDB ID: 6x3t, 6x3v) [1]. Comparison of theobtained structures provided valuable insights into the molecular mechanisms of generalanesthetics [2], while the full picture of the mechanism of action is still unclear.
To investigate the effects of the anesthetic-bindings on the structural change of GABA A receptor
during activation, we carried out 100 ns all-atom molecular simulations for a GABA A receptor in
the POPC bilayer and explicit waters using the pre-open structure (PDB ID: 6x40) as initialstructure. We investigated the following three conditions: (i) no anesthetic bound (ii) propofol
bound (iii) etomidate bound. Anesthetics were inserted according to their binding structures, andmultiple simulations (at least five for each condition) were performed by changing the initial
compositions of the lipids. The CHARMM36m force field, the CGenFF force field, and the TIP3P
model were used for protein, ligand, and water, respectively.
Computing the distribution of the pore radius using HOLE [3], we found that pore radius isincreased around the 9’ leucine residue which is called activation gate when anesthetics are bound.
This is consistent with the observations in the anesthetic-bound structures obtained by the
cryo-electron microscopy and may be related to the mechanism of anesthetic action. Subsequent
analysis of the trajectories suggested that the binding of the anesthetics at the subunit interfaces
affects the relative positioning of the subunits and contributes to the expansion of the pore. The
details of the methods and the results will be shown in the presentation.
[1] Kim, J.J.; Gharpure, A.; Teng, J. et al. Shared structural mechanisms of general anaesthetics
and benzodiazepines, Nature, 2020, 585, 303-308.
[2] Kim, J.J.; Ryan E. Hibbs. Direct Structural Insights into GABAA Receptor Pharmacology,
Trends Biochem Sci, 2021, 46, 502 – 517.
[3] Oliver S. Smart et al. HOLE: A program for the analysis of the pore dimensions of ion channel
structural models, J Mol Graph, 1996, 14, 354 – 360.

18

P01-11(F-3)

Free energy analysis of co-solvent effect on insulin dimer
dissociation
Simon Hikiri

Nobuyuki Matubayasi

simon.hkr3i@cheng.es.osaka-u.ac.jp

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

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

Keywords: Co-Solvent Effect, Theory of Liquid, Free Energy Calculation, Molecular Dynamics
We analyze the co-solvent effect on insulin dissociation from the viewpoint of free energy. Treating
insulin as an all-atom model, we combine molecular dynamics simulation and energy representation
theory [1] to analyze the free energy of the co-solvent effect. Correlation analysis of the interacting
components of the free energy (van der Waals interaction, electrostatic interaction, etc.) are performed
to identify the dominant components of the dissociation control.
The solvation free energy changes of insulin from dimer to monomer in pure water and in co-solvent
solutions (a binary mixture of water molecules and co-solvent molecules) are calculated. Comparisonof solvation free energy changes of insulin dissociation in pure water and in co-solvent solution are
reveal the co-solvent effects and their physical origin. We focus on the acquisition of co-solvent effects
from a fundamental point of view, and dealt with co-solvent molecules with simple structures. Basedon the water molecule structure, we considered methanol with one of the hydrogens changed to amethyl group, ethanol with an ethyl group, phenol with a phenyl group and ethylene glycol with ahydroxyethyl group which is a unit of polyethylene glycol, as the co-solvent molecules to be calculated.
And urea that actually exists in vivo is also considered as a co-solvent molecule. Detailed results will
be reported on the day.

[1] Sakuraba, S.; Matubayasi N. Ermod: Fast and versatile computation software for solvation freeenergy with approximate theory of solutions J. Comput. Chem., 2014, 35, 1592–1608.

19

P01-12(F-3)

MDContactCom: A tool to identify differences of protein
molecular dynamics from two MD simulation trajectories
in terms of residue-residue contacts
Chie Motono 1,2
c-motono@aist.go.jp

Miwa Sato 3
sato-miwa@mki.co.jp

1

Shunsuke Yanagida 3
yanagita-shunsuke@mki.co.jp

Takatsugu Hirokawa 1,4,5
t-hirokawa@md.tsukuba.ac.jp

Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced
Industrial Science and Technology (AIST), Tokyo 135-0064 Japan. 2 Computational Bio
Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Waseda University, Tokyo
169-0072, Japan. 3 Bioscience & Healthcare Engineering Div.,Mitsui Knowledge Industry Co.,
Ltd, Tokyo 135-0064 Japan. 4 Division of Biomedical Science, Faculty of Medicine,
University of Tsukuba, Ibaraki 305-0064, Japan. 5 Transborder Medical Research Center,
Faculty of Medicine, University of Tsukuba, Ibaraki 305-8575, Japan.

Keywords: Protein, MD simulation, contact
Comparing results from multiple MD simulations performed under different conditions isimportant during the initial stages of analysis. We propose a tool called MD Contact Comparison(MDContactCom) that compares residue-residue contact fluctuations of two MD trajectories,
quantifies the differences, identifies sites that exhibit large differences, and visua lizes those sites
on the protein structure [1]. Using this method, it is possible to identify sites affected by varying
simulation conditions and reveal the path of propagation of the effect even when differences
between the 3D structure of the molecule and the fluctuation RMSF of each residue is unclear.
MDContactCom can monitor differences in complex protein dynamics between two MD
trajectories and identify candidate sites to be analyzed in more detail. The software supports
trajectories from five major MD engines (Amber, CHRMM, Desmond, GROMACS, and NAMD)
as well as multiple pdb format. As such, MDContactCom is a versatile software package foranalyzing most MD simulations.
We applied MDContactCom to analyze the MD trajectories of Cyclophilin A and its allostericmutant [2,3]. MDContactCom detected residues in the pathways where the mutation effects
propagate
MDContactCom is freely available for download on GitLab. The software is implemented in
Python3. https://gitlab.com/chiemotono/mdcontactcom[1] Motono, C. et al. MDContactCom: a tool to identify differences of protein molecular dynamics
from two MD simulation trajectories in terms of interresidue contacts, Bioinformatics, 2021,
btab538. https://doi.org/10.1093/bioinformatics/btab538.
[2] Doshi,U. et al. Dynamical network of residue–residue contacts reveals coupled allostericeffects in recognition, catalysis, and mutation, Proc. Natl. Acad. Sci., 2016, 113, 4735–4740.
[3] Holliday,M.J. et al. Networks of Dynamic Allostery Regulate Enzyme Function, Structure.,
2017, 25, 276–286.

20

P01-13(F-3)

Molecular Dynamics Simulations of DNA Exonuclease
Hiroki Otaki 1
otaki@nagasaki-u.ac.jp

1

Center for Bioinformatics and Molecular Medicine, Graduate School of Biomedical Sciences,
Nagasaki University, 1-14 Bunkyo-machi, Nagasaki, 852-8521, Japan

Keywords: Molecular dynamics simulation, Network analysis, DNA exonuclease
The three prime repair exonuclease 1 (TREX1) gene encodes a nuclear protein (called TREX1)
with 3´ to 5´ DNA-specific exonuclease activity. Mutations in TREX1 gene are associated with awide spectrum of disorders, including systemic lupus erythematosus (SLE), Aicardi–Goutières
syndrome (AGS), familial chilblain lupus (FCL), and retinal vasculopathy with cerebral
leukodystrophy (RVCL) [1]. Despite a lot of reports of mutations with the diseases, the molecularmechanism of how these mutations affect enzyme activity of TREX1 is still unclear.
In order to clarify the effect of mutations on the TREX1 system, we have modeled the complexesof TREX1 mutants with double-stranded DNA (dsDNA) in the presence of two Mg 2+ ions andconducted molecular dynamics (MD) simulations. By using a network graph to visualizeinteraction network in the TREX1 system, we have found the differences in the interaction aroundthe active site between the mutants and wild type (WT). For example, in the D130N mutant, theinteraction between the magnesium ions and 3´-end of dsDNA becomes stronger than WT. Thissuggests that the mutant is unable to successfully release hydrolyzed nucleic acids, which may
result in the accumulation of nucleic acids and the activation of the innate immune response [2].
The detailed results including other mutants will be discussed in this presentation.
This research is partially supported by Initiative on Promotion of Supercomputing for Young or
Women Researchers, Information Technology Center, The University of Tokyo. This work was
supported by JSPS KAKENHI Grant Number 19K16058.
[1] Rice, G. I.; Rodero M. P.; Crow Y. J. Human disease phenotypes associated with mutations in
TREX1, J. Clin. Immunol., 2015, 35, 235–243.
[2] Endo, Y.; Koga, T.; Otaki, H.; Furukawa K.; Kawakami A. Systemic lupus erythematosus
overlapping dermatomyositis owing to a heterozygous TREX1 Asp130Asn missense mutation, Clin.
Immunol., 2021, 227, 108732.

21

P02-01(F-3)

Insight into Allosteric ERK2 Inhibitors by using Fragment
Molecular Orbital Method
Hajime Sugiyama 1

Mayu Yoshida 2sugiyama.hajime.ma@m-chemical.co.jp

yoshida19@b.s.osakafu-u.ac.jp

Takayoshi Kinoshita 2
kinotk@b.s.osakafu-u.ac.jp

1

2

Mitsubishi Chemical Corporation, 1000 Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa
227-8502, Japan
Osaka Pref. Univ., 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

Keywords: ERK2, Allosteric site, Fragment molecular orbital method
Extracellular signal-regulated kinase 2 (ERK2) belongs to mitogen-activated protein kinase
family, which regulates cell growth, differentiation, and a variety of other cellular responses byphosphorylating several substrates including signal transducer and activator of transcription 3(STAT3), which has essential roles for normal glucose homeostasis [1].
The authors focused on ERK2/STAT3 pathway as a novel diabetes target as a result of text
mining technology. Their previous studies confirm ed that a peptide designed to inhibit ERK2
phosphorylation to STAT3 showed physiological effects in vitro and vivo [2]. A subsequent insilico screening rendered low-molecular-weight compounds with inhibitory activity [3].
We here investigated interactions between ERK2 and the in silico hit compounds. Fragment
molecular orbital method and Pair interaction energy decomposition analysis extracted key
interactions between the protein and the ligand from X-ray crystallography structures. Furthermore,this calculation conferred an energetically favorable ligand conformation.
These results would greatly enhance to develop highly potent and selective ERK2 inhibitors.
[1] H. Inoue, et al., Nature Medicine 10: 168-174 (2004).
[2] T. Kinoshita, et al., Chem. Biol. Drug Des. 78: 471- 476 (2011).
[3] T. Kinoshita, et al., Bioorg. Med. Chem. Lett. 26: 955- 958 (2016).

22

P02-02(F-3)

Dynamic Residue Interaction Network Analysis ofSecondary Mutations in Protease that Promote Drug
Resistance in HIV-1
Ayaka Ojima

Mohini Yadav

s17a6025jk@s.chibakoudai.jp

s1879602ec@s.chibakoudai.jp

Norifumi Yamamoto

norifumi.yamamoto@p.chibakoudai.jp

Department of Applied Chemistry, Faculty of Engineering,
Chiba Institute of Technology, Narashino, Japan

Keywords: Dynamic Residue Interaction Network, Drug Resistance
The human immunodeficiency virus (HIV) is the pathogen of the Acquired Immune Deficiency
Syndrome (AIDS). AIDS has become a disease that can be controlled in the long term by anti-HIV
drugs. However, there are serious concerns about the emergence of viral mutants that are resistantto anti-HIV drugs. Some amino acid mutations in HIV-1 protease promotes development of drug
resistance caused by primary mutations, even without directly affecting drug efficacy against
anti-HIV drugs. These mutations are referred to as "secondary mutations". In this study, weinvestigated the dynamic correlation between the drug binding site and its secondary mutation sitein HIV-1 protease using dynamic residue interaction network (dRIN) analysis [1] based onmolecular dynamics simulations.
[1] Yadav, M., Igarashi, M., Yamamoto, N.; Dynamic residue interaction network analysis of theoseltamivir binding site of N1 neuraminidase and its H274Y mutation site conferring drug
resistance in influenza A virus, PeerJ, 2021, 9, e11552.

23

P02-03(F-3)

Dynamic Residue Interaction Network Analysis ofPrimary Mutations in Protease that Promote Drug
Resistance in HIV-1
Ryoga Miyawaki

Mohini Yadav

s17a6107yj@s.chibakoudai.jp

s1879602ec@s.chibakoudai.jp

Norifumi Yamamoto

norifumi.yamamoto@p.chibakoudai.jp

Department of Applied Chemistry, Faculty of Engineering,
Chiba Institute of Technology, Narashino, Japan

Keywords: Dynamic Residue Interaction Network, Drug Resistance
The human immunodeficiency virus (HIV) is the pathogen of the Acquired Immune Deficiency
Syndrome (AIDS). AIDS has become a disease that can be controlled in the long term by anti -HIV
drugs. However, due to prolonged treatment, there are serious concerns about the emergence ofviral mutants that are resistant to anti-HIV drugs. Some amino acid mutations in the HIV-1
protease are known to directly and substantially reduce drug efficacy against anti -HIV drugs.
These mutations are referred to as "primary mutations". In this study, we investigated the dynamic
correlation between the drug binding site and its primary mutation site in HIV-1 protease using
dynamic residue interaction network (dRIN) analysis [1] based on molecular dynamics
simulations.
[1] Yadav, M., Igarashi, M., Yamamoto, N.; Dynamic residue interaction network analysis of theoseltamivir binding site of N1 neuraminidase and its H274Y mutation site conferring drug
resistance in influenza A virus, PeerJ, 2021, 9, e11552.

24

P02-04(F-3)

Study of the Binding Mechanism of Inhibitor toInterLeukin-2 by Free Energy Calculations
Noritaka Inoue 1
noritaka.inoue@schrodinger.com

1

Schrödinger KK, Marunouchi Trust Tower North build. 13F, 1-8-1 Marunouchi, Chiyoda-ku,
Tokyo,100-0005, Japan

Keywords: Free energy perturbation, Binding free energy, Molecular simulation
Over the last 10 years, there has been tremendous progress in enhanced sampling algorithms forbinding free energy calculations such as FEP/REST [1] and force field development [2] together
with the advent of GPU architectures. In addition, these have been compiled with user-friendly
GUI as a product FEP+ released by Schrödinger Inc. [3], which enables even non-experts toperform binding free energy calculations. As a result, highly accurate prediction of binding affinity
between the ligand and the receptor has become a reality [4] and nowadays many pharmaceuticalcompanies have adopted these methods in their drug designs.
The full physical mechanisms of the process from unbound state to bound state such as
desolvation penalty or ligand strain are incorporated in the binding free energy calculation, so thatit provides not only the prediction values but also the chance to interpret the binding mechanisms.
The latter is also beneficial especially for difficult drug target proteins.
In this study we have applied FEP+ to InterLeukin-2(IL-2) complexed with its inhibitor to predict
the binding affinity and detect the important residues that account for the ligand selectivity. IL-2 isan immunoregulatory cytokine and known as a difficult drug target because the inhibitor binds on
the protein-protein interaction interface which has the inherent plasticity [5]. Our study has
demonstrated that the FEP+ simulation can successively predict the binding affinity of IL-2
inhibitors (R 2 = 0.94, MUE = 0.82) and is also helpful to elucidate the binding mechanism.
[1] Wang, L. ; et al., Protein-Ligand Binding Free Energy Calculations with FEP, Methods Mol Biol.,2019, 201 – 231.
[2] Lu, C. ; et al., OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space ,J. Chem. Theory Comput., 2021, 17, 7, 4291 – 4300.
[3] https://www.schrodinger.com/products/fep[4] Wang, L. ; et al., Accurate and Reliable Prediction of Relative Ligand Binding Potency in
Prospective Drug Discovery by way of a Modern Free Energy Calculation Protocol and Force Field ,J. Am. Chem. Soc., 2015, 137, 2695 – 2703.
[5] Thanos, C. D.; et al., Potent Small-Molecule Binding to a Dynamic Hot Spot on IL-2 , J. Am. Chem.
Soc., 2003, 125, 15280 – 15281.

25

P02-05(F-3)

Dynamic FMO analysis for RNA sequence specificity ininhibitor recognition of translation initiation factor
complexYuma Handa 1 Yusuke Kawashima 1 Takayuki Furuishi 1 Etsuo Yonemochi 1 Takuhiro Ito 2
Hironori Saito 3,4 Shintaro Iwasaki 3,4 Chiduru Watanabe 5 Teruki Honma 5 Kaori Fukuzawa 1,6
d2002@hoshi.ac.jp y-kawashima@hoshi.ac.jp t-furuishi@hoshi.ac.jp e-yonemochi@hoshi.ac.jp
takuhiro.ito@riken.jp hironori.saito@riken.jp shintaro.iwasaki@riken.jp
chiduru.watanabe@riken.jp honma.teruki@riken.jp k-fukuzawa@hoshi.ac.jp
1

2

3

4

5

6

School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, ShinagawaKu, Tokyo 142-8501, Japan
Laboratory for Translation Structural Biology, RIKEN Center for Biosystems Dynamics
Research (BDR) W215 West Research Building,1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama,JapanRIKEN Cluster for Pioneering Research (CPR) S205 Bioscience Bldg. 2-1, Hirosawa
Wako,Saitama 351-0198, Japan
Department of Computational Biology and Medical Sciences, Graduate School of Frontier
Sciences, The University of Tokyo, Kashiwa, Chiba 277 -8561, Japan
Laboratory for Structure-Based Molecular Design, RIKEN Center for Biosystems Dynamics
Research, Tsurumi-ku, Yokohama 230-0045, Japan.
Tohoku University Graduate School 6-6-11 Aoba, Aramaki, Aoba-ku, Sendai-shi, Miyagi-ken
980-8579

Keywords: eIF4A, RNA, Rocaglamide A, Fragment molecular orbital method, Molecular dynamics
Rocaglamide A (RocA), a natural compound found in Aglaia genus plants, has potent anticancer
activity and acts as a translation inhibitor in the RNA -binding cavity of the initiation factor eIF4A.
Although it is known to exhibit purine sequence specificity in RNA binding [1], the detailed
molecular mechanism remains unclear. In this study, we analyzed the molecular recognition
mechanism of elF4A by dynamic FMO calculations combined with classical MD and FMO methods.
Using the quaternary complex structure of elF4A, RNA, AMP-PNP, and RocA (PDBID:5zc9) as theinitial structure, we prepared wild-type and four mutant structures (G6U, A7U, G8U, and A9U) andperformed MD calculations using AMBER14:EHT force fields of 50 ns for each complex. Fromeach MD trajectory, 10 structures were extracted every 3 ns, and FMO calculations at th e MP2/631G* level were performed.
The interaction energy analysis of RocA with the surrounding residues showed that the wild type
was the most stable, with π/π interactions mainly with A7 and G8, and a hydrogen bond with Gln195 ,which were always maintained (Fig. I). In the G8U mutation (Fig. Ⅱ), U8 was inverted and formed
an interaction with different sites of RocA while maintaining the stacking structure with A9;
however, the interaction was weaker than the wild type. On the other hand, i n the G6U, A7U, andA9U mutations, the fluctuation of A7 was increased, and the hydrogen bond and CH/π interaction
between RocA and Gln195 and A7 could not be maintained, resulting in weaker interaction.
The FMO calculations were partiallyperformed using the Fugaku supercomputer(HPCI project ID: hp210130). This study waspartially supported by the AMED-BINDS
project (grant number JP21am0101113).
[1] Shintaro. I. et al., The Translation
Inhibitor Rocaglamide Targets a Bimolecular
Cavity between eIF4A and Polypurine RNA
Mol Cell. 2019;73(4):738-748.e9.
Main Interaction around RocA (I:Wild Type, Ⅱ:G8U mutant)26

P03-01(F-4)

Development of in silico prediction method for
idiosyncratic drug-induced liver injury
Sarara Doi 1

Saki Katayama 1

c212710@ed.nagoya-cu.ac.jpc172035@ed.nagoya-cu.ac.jp

Takayuki Tachiki 1

Kaori Ambe 1

c182528@ed.nagoya-cu.ac.jp

ambek@phar.nagoya-cu.ac.jp

Toshimasa Jindo 2

Masahiro Tohkin 1

tohkin@phar.nagoya-cu.ac.jp

12

jindo.toshimasa@daiichisankyo.co.jp

Graduate School of Pharmaceutical Sciences, Nagoya City University,
3-1, Tanabe-dori, Mizuho-ku, Nagoya-shi, Aichi 467-8063, Japan
Daiichi Sankyo Co., Ltd.,
1-2-58, Hiromachi, Shinagawa-ku, Tokyo 140-8710, Japan

Keywords: idiosyncratic Drug-Induced Liver Injury , machine learning method,
Drug-induced liver injury (DILI) is an adverse drug reaction that is a serious problem in clinicalpractice, and many of them are idiosyncratic. Since it is difficult to detect the idiosyncratic DILI(iDILI) in animal experiments, it is strongly desired to develop a prediction method at the drug
development stage. It is also expected to use machine learning modeling to predict the adverse
drug reaction. We aimed to construct iDILI prediction methods based on the mechanism of toxicity
by machine learning from the viewpoint of chemical structure and in vitro experimental data
information of drugs in this study.
We made a dataset to classification DILI using Drug Induced Liver Injury Rank (DILIrank) which
is mainly based on the information of the package insert [1], and a dataset to classification iDILI
using LiverTox that provides up-to-date clinical patterns and management of liver injury [2]. First,
we made a model to classification DILI, and then a model to classification iDILI. The performanceof generated classification models was evaluated in terms of accuracy, sensitivity, specificity, andarea under the curve (ROC-AUC) using 5 x 4 nested cross validation, maintaining thepositive/negative ratio. We constructed classification models using a XGBoost algorism withmolecular descriptors calculated by AlvaDesc [3] and in vitro experimental data information
extracted from TOX21 [4].
The established DILI classification model showed 0.79, and iDILI classification model showed
0.69 as the average ROC-AUC.
In this study, we were able to construct an iDILI classification model that can provide more
specific information such as the mechanism by using in vitro information.
This work was supported by research funds from Daiichi Sankyo TaNeDS grant program.
[1] Minjun Chen, Ayako Suzuki, Shraddha Thakkar, Ke Yu, Chuchu Hu, Weida Tong. DILIrank:
the largest reference drug list ranked by the risk for developing drug-induced liver injury inhumans. Drug Discovery Today Volume 21, Issue 4, April 2016, Pages 648-653.
[2] https://www.ncbi.nlm.nih.gov/books/NBK547852/[3] https://www.affinity-science.com/alvadesc/[4] https://ncats.nih.gov/tox21

27

P03-02(F-4)

Machine Learning-based Evaluation of Microscopic Images ofFukushima Patient-derived organoids
Kosuke Takagi 1

Motoki Takagi 2

kosuke.takagi@olympus.com

motokitakagi02@gmail.com

Kazuhito Goda 3
kazuhito.goda@olympus.com

1

2

3

Core Tech and Strategy, Olympus Corporation, 67-4 Takakura-cho, Hachioji, Tokyo,192-0033,
Japan
Medical-Industrial Translational Research Center, Fukushima Medical University, 1
Hikarigaoka, Fukushima,960-1295, Japan
Biological Evaluation Technology, Olympus Corporation, 2-3 Kuboyama-cho, Hachioji,
Tokyo,192-0023, Japan

Keywords: Machine-learning, Fukushima Patient-derived organoids, Microscopic images
Patient-derived organoids (PDOs) have a variety of potential applications in pre-clinical studiesof the tumor response. PDOs have highly complicated multicellular structures, reflecting the
genotypic and phenotypic diversity of their origins. Microscopic images enable us to capture
various features and processes; however the complexity of PDOs increases difficulty of effective
analysis with images. Here, we evaluate the microscopic images of Fukushima PDOs (F-PDOs)
with a machine learning based approach, in order to extract effective information by reducing thecomplexity. At first, we extracted image features and assessed relations to other datasets with aconvolutional neural network (CNN) trained for images and gene expressions. The result indicatesthat morphological features in images of PDOs correctly reflect the differences between origins.
Also, the correlation to the gene expression indicated by the results suggests the possibility toconstruct an analyzing system that integrates information about PDOs' features including
microscopic images of them. Finally, we discussed the predictability of the drug responses byintegrating these data. It suggests the usefulness of imaging data in analyzing the drug response
profiles of the tumor models.

28

P03-03(F-4)

A customizable benchmark dataset for evaluation of
structure-based protein-ligand binding prediction
Xiaoyao Zhang 1

Mari Matsumura 1zhang.x@cb.cs.titech.ac.jp

matsumura@cb.cs.titech.ac.jp

Takashi Ishida 1
ishida@c.titech.ac.jp

1

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Room
E505, West 8E building, W8-85 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan.

Keywords: Structure-based virtual screening, Machine learning, Deep learning.
With the development of deep learning as well as the growth of protein structure and compounds
libraries, high performance and successful applications of protein-ligand binding prediction using
machine learning methods have been reported. However, several studies [1] have shown that theperformances of these machine-learning based binding prediction methods were overestimated dueto the hidden bias and the insufficient data size of the evaluation datasets, such as DUD-E [2].
Moreover, the generalizability of these methods was not correctly evaluated.
Therefore, we here present a new dataset designed for a benchmark of machine learning based
virtual screening methods. The dataset, named CUE, is based on ChEMBL activities data and canbe customized in several aspects. With a loosened restriction on target selection, CUE has arelatively large amount of data that around 280 targets, 20K actives, and 15K inactives depend on
the parameters selected. So that the dataset contains diverse target-ligand information, which
provides more ability to assess the generalizability of structure-based methods. We also provide
several customizable parameters such as affinities threshold for active ligand, to match different
research needs.
To ensure that the data set is capable of machine learning based methods. Active and inactive
ligands are filtered to remove bias. Targets with X-ray structures are chosen from single protein
targets with low sequence homology. We also evaluated our dataset with weak classifiers. Thisshows that models can’t learn to discriminate between actives and inactives on ligand information
only.
[1] Sieg, J.; Flachsenberg, F.; Rarey, M. In Need of Bias Control: Evaluating Chemical Data for
Machine Learning in Structure-Based Virtual Screening. J. Chem. Inf. Model. 2019, 59, 947–
961.
[2] Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys,
Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. J. Med. Chem. 2012,
55, 6582– 6594.

29

P03-04(F-4)

Design and Implementation of New Methods
for Odor Molecule Generation
Sijie Zhang 1

Masaya Otake 2zhang.s.ak@m.titech.ac.jp

Hideki Miyazaki 2
hideki_miyazaki@takasago.com
1

2

masaya_otake@takasago.com

Kunihide Hoshino 2

Takashi Ishida 1

kunihide_hoshino@takasago.com

ishida@c.titech.ac.jp

School of Computing, Tokyo Institute of Technology. W8-85, 2-12-1 Ookayama, Meguro-ku,
Tokyo,152-8550, Japan
Corporate Research & Development Division, Takasago International Corporation. 1-4-11
Nishiyawata, Hiratsuka City, Kanagawa, 254-0073, Japan

Keywords: Molecular generation, Deep learning, Reinforcement learning, Genetic algorithm
Although computer-aided design (CADD) [1] has made great progress in the field of molecular
generation [2, 3] and the advancements in deep learning have encouraged its application in CADD[4], it is still difficult for some specific features of molecular generation tasks, such as generation
molecules with specified odors [5].
To solve this problem, we introduce two molecular generation methods: (1) A molecular
generation method based on genetic algorithm (GA)[6], which decomposes the molecules intofragments and reconstruct the molecules by these fragments using GA; (2) A deep generativemodel with reinforcement learning and based on generative adversarial network (GAN)[7], for a
specific odor label, the model could generate molecules with the specified odor. We employ thereinforcement learning to encourage the model to generate molecules that are more likely to havethe specified odor.
We applied the model to the odor molecules dataset with certain odor labels and train the odor
prediction model to evaluate our methods, to prove the effectiveness of the proposed methods.
This abstract is based on results obtained from a project, JPNP14004, commissioned by the New
Energy and Industrial Technology Development Organization (NEDO).
[1] Narayan, K. Lalit. Computer Aided Design and Manufacturing. New Delhi: Prentice Hall of
India. 2008.
[2] Rao VS., Srinivas K., Modern drug discovery process: an in silico approach. J. Bioinform. Seq
Anal, 2(5):89–94, 2011.
[3] Bajorath J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov.
1(11):882–894, 2002.
[4] Chen H., Engkvist O., Wang Y., Olivecrona M., Blaschke T. The rise of deep learning in drug
discovery. Drug Discov Today. 2018.
[5] Sánchez-Lengeling, B., Wei, J.N., Lee, B.K., Gerkin, R.C., Aspuru-Guzik, A., Wiltschko, A.B.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small
Molecules, ArXiv abs/1910.10685, 2019.
[6] Mitchell, Melanie. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. 1996.
[7] Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair,
Sherjil; Courville, Aaron; Bengio, Yoshua. Generative Adversarial Nets. Proceedings of theInternational Conference on Neural Information Processing Systems (NIPS 2014). pp.
2672–2680. 2014.

30

P03-05(F-4)

Improvement of Variational Auto-Encoder Design ofOrganic Molecules by Discriminator
Kyosuke Sato 1

Ptva0fz0@s.okayama-u.ac.jp

1

Masaaki Misawa 1

misawa@okayama-u.ac.jp

Kenji Tsuruta 1

tsuruta@okayama-u.ac.jp

Graduate School of Natural Science and Technology, Okayama University., 3-1-1 Tsushimanaka,
Kita-ku, Okayama-shi, Okayama,700-8530, Japan

Keywords: Molecular Design, SMILES, Deep Learning, Variational Auto-Encoders
Efficient search for optimum molecular structure contributes to an essential speedup of thedevelopment of organic devices and, in turn, to the improvement of their characteristics. In thepresent study, we focus on the deep learning variational auto -encoder (VAE) model, wheremolecules represented by SMILES strings can be efficiently converted to multivariable continuous
space [1,2].
The VAE consists of two neural networks: an encoder and a decoder. The one-hot representationof SMILES is input to the encoder and mapped to the l atent variable space. We use QM9 dataset[3,4] for training the VAE. After applying a molecular-mechanics method to obtaining 3D
structures from SMILES, we can optimize physical properties of the molecules by other simulation
methods such as density-functional-theory calculations even when there is not enough preexistingdata set. The range of physical property space covered by the SMILES representation is thereb y
expanded and the data-driven optimization using Kernel Ridge Regression method can be
performed within the search space [5].
However, there is a dead space where the SMILES string generated by the Decoder from thecontinuous variable space is chemically or grammatically incorrect, which degrades the efficiencyof the optimization process. Therefore, we further improve the output rate of valid SMILES of
decoder by introducing a “discriminator” attached to the VAE stream. The discriminator is themodel to predict SMILES validity by calculating loss function for improvement of the VAE.
Details of the method and the efficiency improvement will be discussed in the presentation.
[1] Gómez-Bombarelli, R.; et al., Automatic Chemical Design Using a Data-Driven Continuous
Representation of Molecules, ACS Central Science, 2018, 4, 268-276[2] Kusner, M. J.; Paige, B.; Hernández-Lobato, J. M. Grammar Variational Autoencoder, (2017).
http://arxiv.org/abs/1703.01925[3] Ruddigkeit, L.; Van Deursen, R.; Blum, L. C.; Reymond, J. L. Enumeration of 166 billionorganic small molecules in the chemical universe database GDB-17, Journal of Chemical
Information and Modeling, 2012, 52, 2864-2875.
[4] Ramakrishnan, R.; Dral, P. O.; Rupp M.; Von Lilienfeld, O. A. Quantum chemistry structuresand properties of 134 kilo molecules, Scientific Data, 2014, 1, 1–7.
[5] Sato, K.; Tsuruta, K. Optimization of Molecular Characteristics via Machine Learning Basedon Continuous Representation of Molecules, Materials Science Forum, 2021, 1016, 1492-1496.

31

P03-06(F-4)

Appropriate Evaluation Measurements
for Regression Models
Tsuyoshi Esaki 1
tsuyoshi-esaki@biwako.shiga-u.ac.jp
1

The Center for Data Science Education and Research, Shiga University, 1 -1-1 Banba, Hikone,
Shiga 522-8522, Japan

Keywords: Regression model, in silico prediction, Data science, Model Evaluation
In recent years, accelerating the speed of finding seed compounds and reducing the cost ofpharmaceutical research has become a necessity. The contribution of in silico drug discovery methods,
which predict candidates as new drugs using physicochemical features and substructure fingerprints ofcompounds, is thus expected. Selecting the seed compounds without conducting experiments could
reduce the time and cost required for drug development. However, estimating the characteristics ofcompounds in our body using a simple linear model alo ne is unsatisfactory because the effects anddistribution of compounds are determined by the environment in our body and their interactions withother molecules. More complex models have been prepared to estimate compound characteristics withhigh predictive accuracy than simple models. Thus, it is increasingly important to correctly evaluate thepredictive performance when selecting the models appropriate for research purposes.
The determinant coefficient, famous as R2, is one of the most prominent statistical measures forevaluating regression models. However, this measure cannot be used for nonlinear regression models.
Furthermore, the determinant coefficient cannot be employed to assess the predictive accuracy of models.
It is necessary to separate “goodness of fit” and “predictive accuracy” and objectively evaluate thepredictive capability. The goodness of fit provides a statistical measure for the data points used in thefitting parameters and shows the explainability of the data used for training. The determinant coefficientis used to measure the goodness of fit of models.
On the contrary, predictive accuracy is a measure that shows how a model can predict new data
correctly and shows the predictive capability of data that were not used for training. Predictive accuracy
is only calculated against the observed and predicted values, and the number of descriptors is notconsidered. The mean squared error (MSE) thus is the most appropriate measure for calculating
predictive accuracy.
This study employed published data to confirm the statistical measures regarding the differences
between linear and nonlinear regression models . The difficulty of using the determinant coefficient is
explained. In practical machine learning problems, the proper statistical measures were suggested under
the following two conditions: MSE for cross-validation, and MSE along with correlation coefficientsfor the observed and predicted values of external test data.
Understanding statistical measures and using them appropriately is necessary. The suggested measures
will support the practical selection of promising seed compounds and accelerate drug discovery.
This work was supported in part by a research grant for priority areas from Shiga University.

32

P03-07(F-4)

Effect of integrating HTS prediction outputs on ability offingerprints to describe biological properties of chemicals
Shumpei Nemoto 1

Tadahaya Mizuno 1, †Hiroyuki Kusuhara 1, †Nemo88@g.ecc.u-tokyo.ac.jp

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

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

1

Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan†
Author to whom correspondence should be addressed

Keywords: fingerprint, chemoinformatics, machine learning, high-throughput screening
Development of high-throughput screening (HTS) assay systems has led to the accumulation of avast amount of data on biological properties of chemicals. Such biological properties have improved
the accuracy of chemoinformatics tasks such as toxicity prediction. Many of machine learning
approaches utilize the combination of chemical structures and HTS data for end-to-end purposes
while collecting each model output and integrating them into the original fingerprints in acomparable manner would be multivariate that describes both structural and biological propertiesof chemicals and can be applied to a variety of chemoinformatics tasks. In addition, accumulationof such fingerprints and analyses of their differences are expected to lead to the discovery of new
aspects of chemicals and the understanding of chemical spaces. However, the simple collection ofmachine learning model outputs of HTS data would not be desirable fingerprints because theactivities of compounds in HTS data are generally imbalanced and the probability values obtainedby machine learning are not scaled. In this study, we evaluated the effects of integrating model
outputs of HTS data prediction with the original fingerprints, introducing SMOTE, a method ofoversampling, in modeling, and calibration of each model on the ability of the generated
multivariate to describe a wide range of biological properties of chemicals.
We obtained the ToxCast dataset as an HTS dataset and employed mol2vec fingerprint and
XGBoost as model input and the machine learning method for each feature generation, respectively.
Initially, 244 out of total 1569 assays were selected for feature candidates based on prediction
accuracy. Each HTS data was trained with SMOTE 1 , calibration, and the combination of them, anda multivariate transformer was generated by summarizing the predictors. The ability to describe
biological properties of chemicals was evaluated by comparing the intra-dataset similarity ofchemicals calculated in transcriptome profiles, transcriptome data of cultured cells treated with achemical, and the generated multivariate sets of chemicals, based on a hypothesis that transcriptome
profiles well reflect biological properties of chemicals. The transcriptome profiles were obtained
from the connectivity map) database, and 292 chemicals derived from MCF7 cells were analyzed.
From a macroscopic perspective, the integration was not effective in bringing the intra-dataset
similarity in the generative multivariate sets closer to that in the transcriptome profiles. On the other
hand, several pairs of chemicals (e.g., Monorden-Geldanamycin and Ionomycin-Thapsigargin), aresimilar in transcriptome profiles but not in structure, exhibited the closer patterns of the intradataset similarity by the integration of model outputs, and introducing SMOTE and calibration
brought additional improvements. It should be noted that the picked up chemicals are not present in
the HTS dataset used for training.
These results indicate that the ability of fingerprints to describe biological properties can be
improved by integrating model outputs of HTS data prediction into the original fingerprints, andutilization of oversampling and calibration methods enhanced the ability, in several chemicals.
Further analyses are necessary to clarify the properties of chemicals that benefit from this approach.
(1)

Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; Kegelmeyer, W. P. SMOTE: Synthetic
Minority Over-Sampling Technique. J. Artif. Intell. Res. 2002, 16 (Sept. 28), 321–357.

33

P03-08(F-4)

Development of a Prediction Method for Protein Cryptic Sites
Using Machine Learning
Masahito Kumada 1
5445197440@edu.k.u-tokyo.ac.jp

1

Master's Program in Medical Informatics and Biotechnology, Graduate School of Frontier Sciences, The
University of Tokyo

Keywords: Structural Biology, Drug Discovery Support, Machine Learning
1. Background
Recently, it has been known that proteins contain hidden ligand (drug) binding sites called "cryptic
sites", which are normally closed (apo-structure) but are formed when a drug binds to them(holo-structure), and are expected to be applied as new drug targets [1]. However, most of the cryptic
sites discovered so far have been identified by chance by comparing the holo- and apo-structures ofligands and target proteins determined by structural biology analysis. If we can predict cryptic site
containing proteins from their apo-structures, we will be able to discover new target proteins anddevelop new drug discovery research. Currently, efforts are being made to develop a method forpredicting cryptic sites by using fragment molecules that induce cryptic sites as co-solvents inexperiments and molecular dynamics simulations [1-3].
2. Objective
The purpose of this study is to develop a machine learning model to classify the presence or absence of
cryptic sites using the protein structure of apo structure as input. We also attempted to evaluate thefactors of cryptic sites from the generated machine learning model, and to find a guideline fordeveloping new software to detect cryptic sites.
3. Results
The constructed dataset was divided into 175 training data and 36 test data. Random Forest, XGBoost,LightGBM, and SVM were used as machine learning models to train and compare the performance. As
a result, SVM had the best performance, achieving F1 Score: 71.0% for the test data. We checked thesurface structure around the protein pocket when the machine learning model answered correctly andincorrectly. As it stands now, when the model predicts incorrectly, the apo-structure has shallow
indentations that could be cryptic sites, and the model misjudges other indentations. We also visualized
the features that the SVM judged to be important in learning. The results show that the model considers
the hydrophobicity score, alpha sphere density, and polarity score as important features. In the future,
based on the above findings, we would like to customize Fpocket [4] to improve the accuracy of cryptic
site detection.

[1] PeterCimermancicetal.,JMolBiol.428,709-719(2016)[2] KimuraSRetal.,JChemInfModel.57,1388-1401(2017).
[3] AntonijaKuzmanicetal.,Acc.Chem.Res.53,654-661(2020).
[4] LeGuillouxetal.,BMCBioinformatics10,168(2009).

34

P11-01(F-4)

Gentamicin-induced hearing loss: a toxicological study
using drug−gene network analysis
Mizuki Tanaka 1

Kiyoka Matsumoto 1

165050@gifu-pu.ac.jp

165079@gifu-pu.ac.jp

Riko Satake 1

Yu Yoshida 1

175058@gifu-pu.ac.jp175127@gifu-pu.ac.jp

Misaki Inoue 1

Shiori Hasegawa 1

175016@gifu-pu.ac.jp

135056@gifu-pu.ac.jp

Takaaki Suzuki 2

Mari Iwata 3

t07tor5489@hi3.enjoy.ne.jp

1

2
3
4

m-iwata@kifune.info

Kazuhiro Iguchi 4

Mitsuhiro Nakamura 1

iguchi@gifu-pu.ac.jp

mnakamura@gifu-pu.ac.jp

Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4, Daigaku-Nishi, Gifu,
501-1196, Japan
Gifu Prefectural Government, 2-1-1 Yabutaminami, Gifu, 500-8570, Japan
Kifune Pharmacy, 2-23-2 Hasuike, Yanaizu-cho, Gifu, 501-6103, Japan
Laboratory of Community Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigaku-Nishi,
Gifu, 501-1196, Japan

Keywords: Hearing loss, Gentamicin, Aminoglycoside, Drug−gene interactions
Many drugs, especially aminoglycoside (AG) antibiotics, can cause sensorineural hearing loss,
leading to deafness. The objectives of the study were to elucidate the potential toxicologicalmechanism of AG-induced hearing loss through a drug–gene network analysis.
Our analysis demonstrated an association between several AGs and hearing loss using the Foodand Drug Administration Adverse Event Reporting system (FAERS) database. We extracted
Gentamicin (GEN)-associated genes (seed genes) and analyzed drug−gene interactions using the
ClueGO plug-in in the Cytoscape software. We retrieved 17 seed genes related to GEN from thePharmGKB and Drug Gene Interaction databases. In total, 1018 human genes interacting with GEN
were investigated using ClueGO. Through Molecular Complex Detection (MCODE) analysis, we
identified 17 local gene clusters. The nodes and edges of the highest-ranked local gene cluster named“Cluster 1” were 30 and 433, respectively. According to the ClueGO analysis using the Kyoto
Encyclopedia of Genes and Genomes (KEGG), Cluster 1 genes were highly enriched in “oxidative
phosphorylation.” According to the ClueGO analysis using ClinVar, Cluster 1 genes were highly
enriched in “mitochondrial diseases,” “mitochondrial complex I deficiency,” “hereditary hearing loss
and deafness,” and “Leigh syndrome.” Our results demonstrated an association between several AGs
and hearing loss. Drug−gene network analysis demonstrated that GEN may be associated withoxidative phosphorylation-associated genes, which may be genes associated with hearing loss.
We believe that our study makes a significant contribution to the literature because our findings
may provide insight into the toxicological mechanism of AG-induced hearing loss and aid thedevelopment of drugs that prevent AG-induced hearing loss.
[1] Tanaka M, Hasegawa S, Nakao S, Shimada K, Mukai R, Matsumoto K, Nakamura M. Analysis
of drug-induced hearing loss by using a spontaneous reporting system database. PLOS ONE,2019, 14, e0217951

35

P04-01(F-5)

Rational Elucidation of Weak Interaction in Protein, A
Model Study Utilized Copper Protein
Takamitsu Kohzuma 1

Takahide Yamaguchi 1

Attila Taborosi 1

Seiji Mori 1

takamitsu.kohzuma.qbs@vc.ibaraki.ac.jp

Inst. Quantum Beam Sci., Ibaraki Univ., 2-1-1 Bunkyo, Mito, Ibaraki 310-8512, Japan

Keywords: Weak Interaction, X-ray crystal structure, Spectroscopy, Protein Stability, Electronic
structure calculation
Noncovalent interactions (NCI) with the smaller interaction energy, < 10 kJ/mol originated fromthe dispersion force have been discussed as the important factor in many biological molecules [1].
The NCI plays key roles in the biological molecular recognition [2, 3].
The spectroscopic [4-7], electrochemical [4], crystallographic, and computational studies of ablue copper protein, pseudoazurin (PAz) and its Met16X (X = Phe, Leu, Val, Ile) variants gave
clear functional meanings of NCI through the second coordination sphere. The stability andspectroscopic properties of Met16X PAz demonstrated significantly affected by the substitution of
Met16. The combination analysis of X-ray structures and computational calculation in the Met16X
mutants showed the side chains of Met16Phe, Met16Leu, Met16Val, and Met16Ile PAz areinteracted with the His81 imidazole. The clear NCI of S-π/CH-π, π-π, double CH-π, and single
CH-π were identified in the second coordination sphere of Wild Type, Met16Phe, Met16Leu, andMet16Ile PAz, respectively. The protein stability series of those Met16X mutants are also clearlyreflected the strength of NCI in protein structure. Those rational NCI information through thechemistry of Met16X PAz will be expected to apply the protein design for the developments ofbio-drugs.
[1] Watson, J. D.; Baker, T. A.; Bell, S. P.; Grann, A.; Levine, M.; Losick, R. Molecular Biology of
the Gene, Cold Spring harbor Laboratory Press, 2015.
[2] Harel, M.; Schalk, I.; Ehret-Sabatier, L.; Bouet, F.; Goeldner, M.; Hirth, C.; Axelsen, P. H.;
Silman, I.; Sussman, J. L. Quaternary ligand binding to aromatic residues in the active-sitegorge of acetylcholinesterase, Proc. Natl. Acad. Sci. U. S. A., 1993, 90, 9031–9035.
[3] Sussman, J. L.; Harel, M.; Frolow, F.; Oefner, C.; Goldman, A.; Toker, L.; Silman, I. Atomic
structure of acetylcholinesterase from Torpedo californica: A prototypic acetylcholine-binding
protein, Science. 1991, 253, 872–879.
[4] Abdelhamid, R. F.; Obara, Y.; Uchida, Y.; Kohzuma, T.; Dooley, D. M.; Brown, D. E.; Hori, H.;
Interaction between Aromatic Ring and Copper Coordinated His81 Imidazole Regulates the Blue
Copper Active Site Structure, J. Biol. Inorg. Chem., 2007, 12, 165-173.
[5] Yamaguchi, T.; Yano, J.; Vittal, Y.; Nihei, Y.; Togashi, H.; Szilagyi, R. K.; Kohzuma, T. The
Allosteric Regulation of Axial/Rhombic Population in a "Type 1" Copper Site: Multi-Edge X-ray
Absorption Spectroscopic and Density Functional Studies of Pseudoazurin, Bull. Chem. Soc. Jpn.,
2015, 88, 1642-1652.
[6] Abdelhamid, R. F.; Obara, Y.; Kohzuma, T.; Alkaline transition of pseudoazurin Met16X mutant
proteins: Protein stability influenced by the substitution of Met16 in the second sphere coordination,
J. Inorg. Biochem. 2008, 102, 1373–1379.
[7] Yamaguchi, T.; Nihei, Y.; Sutherland, D. E. K.; Stillman, M. J.; Kohzuma, T. Stabilization ofprotein structure through π-π interaction in the second coordination sphere of pseudoazurin,
Protein Sci. 2017, 26, 1921–1931.

36

P04-02(F-5)

Development of Machine-learning based Prediction modelfor hERG inhibitory activities by chemical compounds
Kazuaki Ishihara 1

Hideko Sone 1

184001_tf@yok.hamayaku.ac.jp

Akihide Sumino 1

Kohsuke Hayamizu 1

akihide.sumino@hamayaku.ac.jp

1hideko.sone@yok.hamayaku.ac.jp

k.hayamizu@hamayaku.ac.jp

Yokohama Univ. Pharm., 601 Matano-cho, Totsuka-ku, Yokohama, Kanagawa, 245-0066, Japan

Keywords: hERG, machine learning, molecular descriptor, QSAR
The human-ether-à-go-go-related gene (hERG) encodes potassium channel is one of the major
critical components associated with QT interval prolongation and arrhythmia called Torsades dePointes. Several drugs such as astemizole, terfenadine and vardenafil h ave been withdrawn fromthe market because of the inhibition of cardiac activity. Recently, it has been reported that only
chemicals do not inhibit hERG activity, but also do natural chemicals. Therefore, hERG is a key
target as part of preclinical toxicity screening in drug discovery work. Previously, we reported theresults of a machine learning classification model using a dataset of hERG inhibition on 3721
compounds [1]. However, the model was insufficient in sensitivity as hERG prediction, and needs
to be more sensitivity. To address this issue, we study with the improvement model by combining
data preprocessing and discrimination model using a larger dataset. For the dataset, hERG
inhibition assay data was extracted from an integrated database consisti ng of CheMBL, GOSTAR,
NCGC, and hERG Central, and assay results for 13994 compounds were used [2]. After
preprocessing descriptor selection and parameter tuning, discrimental models were built with using
artificial neural network (ANNE), random forest (RF) , xgboost, support vector machine (SVM)
and naïve Bayes. The model constructed with xgboost was the most fitted. The sensitivity,
specificity, MCC, and ROC-AUC in the ensemble model with the top 5 models were 0.90, 0.82,
0.72, and 0.93, respectively. In addition, the importance of molecular descriptors was calculated by
SHAP [3, 4] to explain the chemical feature for the hERG inhibitory activity, and VSA_EState2,
MolLogP, TPSA, fr_C_O, and BalabanJ were selected as the top five molecular descriptors. The
result suggests that selection of chemical descriptors was the most important to extract the featuresof the hERG inhibitory compound.
[1] Ishihara et al., 65th Conference, The Pharm. Soc. of Japan Kanto Branch, 2021.
[2] Sato T, Yuki H, Ogura K, Honma T. Construction of an integrated database for hERG blocking
small molecules. PLoS One. 2018 Jul 6;13(7):e0199348.
[3] Lundberg, S.M., Erion, G., Chen, H. et al. From loc al explanations to global understanding
with explainable AI for trees. Nat Mach Intell 2, 56 –67 (2020).
[4] Lundberg, Scott M. and Lee, Su-In. A Unified Approach to Interpreting Model Predictions.
NIPS'17: Proceedings of the 31st International Conference o n Neural Information Processing
Systems. 2017 Dec. Pages 4768–4777.

37

P04-03(F-5)

Analyzing Deep Neural Networks on Molecular Activity
Prediction and Characteristics of the Created Network
Yoshiki Kato 1

Shinji Hamada 1

katoh@cch.cs.tut.ac.jp

hamada.shinji.hs@tut.jp

Hitoshi Goto 1
gotoh@tut.jp

1

Toyohashi Univ. of Tech., 1-1 Hibarigaoka, Tempaku-cho, Toyohashi, Aichi, 441-8580, Japan

Keywords: Quantitative Structure-Activity Relationship, Drug Discovery, Deep Neural Networks,
Machine Learning

We are developing our own molecular activity prediction system to further improve the prediction
accuracy. We are optimizing the hyperparameters (fixed parameters that do not change withtraining) of a large number of Deep Neural Networks (DNNs), and analyzing the characteristics of
DNNs when they are used for this task [1,2].
Our predictions use 15 datasets of the Merck Molecular Activity Chall enge [3,4], and we havecreated prediction networks for each. In a previous paper, results were presented mainly for
hyperparameter comparisons, and found that mini-batch size (how much information about
individual input molecules is learned when training) was particularly effective in improving
predictions [1].
Therefore, we increased the number of conditions for the hyperparameters that were found to beimportant, and also analyzed the prediction network that was created. As a result, we found that theaccuracy was improved when the mini-batch size was further reduced. However, decreasing thisparameter means increasing the frequency of error back propagation. This is a trade -off for theincrease in computation time, so we will discuss this point as well.
This work was partly supported by JSPS KAKENHI Grant Number JP20J12786 from the Japan
Society for the Promotion of Science. The numerical calculations were carried out on the
TSUBAME3.0 supercomputer at Tokyo Institute of Technology.
[1] Y. Kato; S. Hamada; H. Goto, Validation Study of QSAR/DNN Models Using the Competition
Datasets, Molecular Informatics, Volume 39, Issue 1-2, 1900154, 2020
[2] Y. Kato; S. Hamada; H. Goto, Analyzing Deep Neural Networks on Molecular Activity
Prediction using Cross Validation Approach, CBI Annual Meeting 2018[3] J. Ma; R. P. Sheridan; A. Liaw; G. E. Dahl; V. Svetnik, Deep neural nets as a method forquantitative structure-activity relationships, J. Chem. Info. Model. 2015, 55 (2), 263-274[4] Y. Xu; J. Ma; A. Liaw; R. P. Sheridan; V. Svetnik, Demystifying Multitask Deep Neural
Networks for Quantitative Structure-Activity Relationships, J. Chem. Inf. Model. 57, 10,2490-2504

38

P06-01(F-5)

Development of Biologically Interpretable Prediction
Models for Drug-Induced Liver Malignant Tumors Basedon the Activity of Molecular Initiating Events Using
FAERS: a Self-Reported Adverse Drug Events Database
Kota Kurosaki

Yoshihiro Uesawa

d196955@std.my-pharm.ac.jp

uesawa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1 Noshio,
Kiyose, Tokyo 204-8588, Japan

Keywords: Quantitative structure–toxicity relationship ,Molecular initiating events, Adverse outcome pathways

Computational

toxicology,

FAERS,

LMTs (liver malignant tumors) have recently been identified as a type of drug-induced liver injury(DILI). DILIs are the most serious adverse drug events and can result in the withdrawal ofpharmaceutical products or major regulatory changes. As a result, various quantitative
structure–toxicity relationship (QSTR) models have been proposed to predict DILI based on thechemical structure to reduce drug discovery failure and cost. However, there is a lack of understandingof the method for evaluating LMT-inducing drugs to be included as an objective variable in the QSTR
model and biological predictors for LMT-inducing drugs. As a result, no biologically interpretable
LMT-inducing drugs prediction models have been developed. Previously, we proposed a method for
drug annotation by assessing the risk of drug-induced LMTs using the Food and Drug Administration
Adverse Event Reporting System (FAERS): a self-reported adverse events big database [1]. Moreover,
we developed Toxicity Predictor [2]: a QSTR tool to predict the activity of molecular initiating events(MIEs) which is defined as the initial interaction between a molecule and biosystem [3], based on its
chemical structure. Here, we hypothesized that activities of MIEs contribute to the prediction for
LMT-inducing drugs, constructed the machine learning models to classify LMT-inducing drugs basedon the activity of MIEs to build biologically interpretable in silico models. We constructed machinelearning models based on the five algorithms (i.e., LightGBM, XGBoost, random forest, neuralnetwork, and support vector machine) and evaluated their predictive performances. Among the models,LightGBM outperformed the others. When we looked at the MIEs’ contributions, we discovered thatgenomic instability, lipid accumulation, immune response, and estrogen function modulation were themost significant predictors of drug-induced LMT. These findings back up previous in vitro and in vivo
research. Our findings could be useful in drug development, research, and regulatory decision-making.
We will build more accurate and biologically interpretable DILI prediction models by increasing ourunderstanding of biological predictors.
[1] Kurosaki, K. and Uesawa, Y. Molecular Initiating Events Associated with Drug-Induced Liver
Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity
Predictions. Biomolecules. 2021, 11(7), 944.
[2] Kurosaki, K. and Uesawa, Y. A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities
in Molecular Initiating Events Based on Chemical Structures. Int. J. Mol. Sci. 2020, 21(21), 7853.
[3] Allen, T.E.; Goodman, J.M.; Gutsell, S. and Russell, P.J. Defining Molecular Initiating Events in
the Adverse Outcome Pathway Framework for Risk Assessment. Chem. Res. Toxicol. 2014, 27,
2100–2112.

39

P05-01(F-5)

Molecular basis of allosteric ERK2 inhibitors discoveredby in silico screening
Mayu Yoshida 1

Hajime Sugiyama 2

Haruna Nagao 3

Yoshida19@b.s.osakafu-u.ac.jp

Masaaki Sawa 3

Takayoshi Kinoshita 1
1Osaka Pref. Univ., 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan2

Mitsubishi Chemical Corporation, 1000 Kamoshida -cho, Aoba-ku, Yokohama, Kanagawa 227-

8502, Japan
3

Carna Biosciences, Inc., 1-5-5 Minatojima-Minamimachi, Chuo-ku, Kobe 650-0047 Japan

Keywords: ERK2, Allosteric site, MAPK, Crystal structure, Selectivity
Extracellular signal-regulated kinase 2 (ERK2) controls vital physiological processes such as cellproliferation and differentiation via phosphorylation of various proteins including downstreamkinases and transcription factors. ERK2 is a drug target for m any diseases such as cancers. The
disruption of ERK2 phosphorylation of signal transducer and activator of transcription 3 (STAT3)
is efficacious against type 2 diabetes mellitus [1]. To obtain highly selective inhibitors, we payedattention to the allosteric substrate binding site (KIM site) but not the ATP binding site of ERK2.
An inhibitor (PEP) with the KIM-site-binding sequence of STAT3 exerts inhibitory activity andpositive effect on db/db mouse, a type 2 diabetes model [2]. Further, crystal structure showed thatPEP bind to the KIM site of ERK2. Subsequently, an in silico screening provided the low molecularweight inhibitors (Hit-1 and -2) which are competitive with PEP [3].
To deeply understand molecular basis of these compounds, we evaluated them by biochemical
experiments and structural dissections. The ELISA assays revealed that Hit-1 selectively inhibited
ERK2 against p38α MAPK, but Hit-2 inhibited ERK2 and p38α MAPK at a comparable level.
Crystal structures showed that Hit-1 and 2 bound to the KIM site with distinct manners. These
results coincides with the PEP-competitive behavior of Hit-1, 2. Comparison of the binding manners
illustrates the difference between these inhibitors in ERK2 selectivity. Furthermore, molecular
dynamics simulations of ERK2 and p38α MAPK provided a structural basis for the high selectivityof Hit-1. The finding of this study would greatly enhance to develop highly potent and selective
ERK2 inhibitors.
[1] H. Inoue, et al., Nature Medicine 10: 168-174 (2004).
[2] T. Kinoshita, et al., Chem. Biol. Drug Des. 78: 471- 476 (2011).
[3] T. Kinoshita, et al., Bioorg. Med. Chem. Lett. 26: 955- 958 (2016).

40

P05-02(F-5)

Development of FMODB and recent updates, 2021
Daisuke Takaya 1,* Chiduru Watanabe 1,2 Kikuko Kamisaka 1
Yuma Handa 3 Kazuki Watanabe 4 Kaori Fukuzawa 3 Teruki Honma 1
1
3

RIKEN Center for Biosystems Dynamics Research 2 JST, PRESTO
Hoshi University 4 Chiba University*Corresponding author. E-mail: daisuke.takaya@riken.jp
Keywords: FMODB, database, FMO, IFIE/PIEDA, COVID-19
Understanding of bio-molecular interactions such as protein-ligand, protein-protein, and nucleic
acid interactions are important for structure-based drug design. Our group has been focusing on not
only molecular mechanics but also quantum mechanics (QM) which incorporate the effects ofdonating and withdrawing electrons and can appropriately deal with the CH-π, π-π, and cation-πinteractions. Fragment molecular orbital (FMO) method [1] enables us to efficiently perform ab
initio QM calculations for large-biomolecules. The benefit of this fragmentation scheme is theavailability of inter-fragment interaction energy (IFIE) and pair interaction energy decomposition
analysis (PIEDA). The IFIE/PIEDA data provides useful information for analyzing protein-ligand
interactions. To analyze IFIE data by a statistical approach, the accumulation of a huge number of
FMO calculation data is essential. Therefore, we are continually updating the contents of FMODB[2] (Fig 1.) that is a database for providing results of FMO calculations and user-friendly web
interfaces to access IFIE/PIEDA data. Currently, the interface is constructed for analyzing a ligandbinding interaction; however, the interface will be improved to analyze various molecularinteractions in our plan. In this poster, we introduce new features of FMODB and recent applications
for analyzing bio-molecules interactions of COVID-19 related biomolecules. [3]Fig 1. Top page of the FMODB (https://drugdesign.riken.jp/FMODB/)

Acknowledgement
This research was done in activities of FMO Drug Design Consortium, https://fmodd.jp/top-en/ .
The results of FMO calculations were partially obtained using the Fugaku supercomputer (project
ID: 210130). This research was partially supported by Platform Project for Supporting Drug
Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life
Science Research (BINDS)) from AMED under Grant Number JP21am0101113. DT and CW
acknowledge JSPS KAKENHI Grant Number 18K06619. Finally, CW acknowledges JST PRESTO
grant (JPMJPR18GD).
1. K. Kitaura et al., Chem. Phys. Lett. 1999, 313, 701-706
2. D. Takaya et al., J Chem Inf Model 2021, 61(2), 777-794
3. K. Fukuzawa et al., accepted41P05-03(F-5)

Visualization of Antigen Binding on DNA Origami Using
Atomic Force Microscopy
Yuki Minamide 1

Kentaro Nishiyama 3

k119802@kansai-u.ac.jp

nishiyama.kentarou.83n@st.kyoto-u.ac.jp

Tomoya Niki 1

Hiroki Akiba 2, 3

k349342@kansai-u.ac.jp

1

2

3

hakiba@pharm.kyoto-u.ac.jp

Satoshi Nagata 2

Haruhiko Kamada 2

Satoshi-Nagata@nibiohn.go.jp

kamada@nibiohn.go.jp

Hiroaki Ohno 3

Akinori Kuzuya 1

hohno@pharm.kyoto-u.ac.jp

kuzuya@kansai-u.ac.jp

Department of Chemistry and Material Engineering, Kansai University, 3-3-35 Yamate-cho,Suita-shi, Osaka, Japan
Center for Drug Design Research, National Institutes of Biomedical Innovation, Health andNutrition (NIBIOHN), 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, Japan
Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29
Yoshida-Shimo-Adachi-cho, Sakyo-ku, Kyoto, Japan

Keywords: DNA Origami, Antigen-antibody reaction, Molecular Imaging, AFM
Antibodies which are also known as immunoglobulins are Y-shaped protein used by the immune system.
They specifically bind to antigen, and this reaction is widely used in medical diagnosis as "antigen
test". There still are some problems in antigen test such as false positive error and false negative error.
Requirement of relatively large amount of sample is another disadvantage compared to PCR test. In
this study, we focused on DNA Origami. 1 DNA Origami is a technique to create any desired
nanostructure by folding long single-stranded circular DNA strand called "scaffold" with the aid of
more than 200 short single-stranded DNA called "staples". We have developed 2D DNA Origami
device called "DNA Nanostick", which has nine nanometer-scales wells in 260 nm-long stick-likestructure. 2 The wells can accommodate exactly one nano-sized molecules such as proteins to provide
nanoarray of organic/inorganic nanoparticles. We attached an antibody fragment called Fab, which is aregion on an antibody that binds to antigens, 3 to specific well of DNA Nanostick to develop"single-molecule antigen test" utilizing atomic force microscopy (AFM) imaging. Two kinds of Fab,those from Pembrolizumab and Nivolumab, clinically approved immunotherapy anti-cancer drugs, 4were used to detect their specific target PD-1, immune checkpoint protein. Each azide-modified Fab
was conjugated with dibenzocyclooctyne (DBCO)-modified staple strand via Cu-free click reaction,and immobilized at the central well. AFM imaging of this Fab-bearing DNA Nanosticks proved
successful attachment of Fab to DNA Nanostick, and specific binding of PD-1 to the well as significant
increase of the height around the well.
[1] P. W. K. Rothemund, Nature 2006, 440, 297-302.
[2] A. Kuzuya, et al., ChemBioChem 2009, 10, 1811-1815.
[3] D. A. Richards, et al., Chem. Sci. 2017, 8, 63-77.
[4] A. B. M. L. A. Tavares, et al., New J. Chem. 2019, 43, 7185-7189

42

P12-01(F-5)

Degradation of DNA ribbons with complementaryannihilation
Hiroki Sawada 1

Ibuki Kawamata 1,2
ibuki.kawamata@tohoku.ac.jp

hiroki.sawada.q2@dc.tohoku.ac.jp

Shin-ichiro M. Nomura 1

Satoshi Murata 1

shinichiro.nomura.b5@tohoku.ac.jp

12

satoshi.murata.a4@tohoku.ac.jp

Department of Robotics, Graduate School of Engineering, Tohoku University, Aramaki -azaAboba 6-6-01, Abobaku, Sendai, Miyagi 980-8579, Japan.
Graduate School of Natural Science and Technology, Ochanomizu University, Otsuka 2-1-1,
Bunkyouku, Tokyo 112-8610, Japan.

Keywords: Molecular self-assembly, Molecular Cybernetics, complementary annihilation, DNA tile
Various nanostructures in living organisms are constantly being replaced by their constituent molecules
through metabolism. In other words, the molecules that assemble the nanostructures are always in dangerof being damaged or degraded, but new molecules are synthesized to replace them, so that thenanostructures as a whole are always maintained in a healthy state. In order to achieve this function in
artificial nanostructures, a method to decompose the nanostructure is required [1]. DNA tiles are a typeof motif (building block) used for self-assembly of nanostructures [2]. The simplest DNA tiles are called“single stranded tiles” (SSTs), which can be used to assemble a variety of 2D or 3D nanostructures [ 3].
SSTs are composed of four domains, which combine with domains of other SSTs to form a desired
structure. For instance, by repeatedly joining a limited number of different SSTs, it is possible to form
one-dimensional structures such as tubes and ribbons [4].
Here, we propose a degradation method that utilizes the interaction between DNA n anostructures
composed of SSTs (Fig. 1). We use DNA ribbons which is assembled by repeated binding of 7 types of
SSTs. When wo types of ribbons designed to be complementary to each other meet, each SST in the
structure forms a double strand with the complementary one in the other structure, and consequently theentire structure is degraded. We named this reaction “complementary annihilation”.

Fig. 1

Schematics of complementary annihilation. Arrows indicate DNA.

We evaluated the formation of two types of ribbon structures and the annihilation reaction between
complementary ribbons using electrophoresis, real-time PCR, and fluorescence microscopy. We
successfully confirmed that degradation based on the above principles does indeed occur. This method
will be able to be used for decomposition of more complex structures in the future. If such a method of
decomposition can be combined with a method of assembly, we will be one step closer to the artificial
realization of functions such as biological metabolism.
[1] Green, L. N. ; Subramanian, H. K. ; Mardanlou, V. ; Kim, J.; Hariadi, R. F. ; Franco, E. Autonomous dynamic control of DNA nanostructure self -assembly, Naturechemist ry, 2019, 11, 510-520.
[2] Wei, B.; Dai, M.; Yin, P. Complex shapes self -assembled from single-stranded DNA tiles, Nature, 2012, 485, 623-626.
[3] Ke, Y.; Ong, L. L.; Shih, W. M.; Yin, P. Three -dimensional structures self -assem bled from DNA bricks, science, 2012, 338, 1177-1183.
[4] Yin, P.; Hariadi, R. F.; Sahu, S.; Choi, H. M.; Park, S. H.; LaBean, T. H.; Reif, J. H. Programming DNA tube circumferences. science, 2008, 321, 824-826.

43

P07-01(F-6)

A gene set analysis method based on
network summary values
Takashi Yoneya 1takashi.yoneya.sk@kyowakirin.com

1

Kyowa Kirin Co., Ltd. Otemachi Financial City Grand Cube, 1-9-2 Otemachi, Chiyoda-ku,
Tokyo,100-0004, Japan

Keywords: Gene set analysis, Network analysis, Transcriptome
High throughput transcriptome methods such as DNA microarray and RNA-seq are widely usedfor various studies and many data analysis methods were developed for such data. Gene set
analysis (GSA) is a category of such methods. Malelki et. al. reviewed and classified the methods
into three types, over-representation analysis (ORA), functional class scoring methods (FCS) andpathway topology-based methods (TP) [1]. Gene set enrichment analysis (GSEA) is one of themost popular GSAs [2] and categorized as an FCS.
We introduce a GSA method, gene set network analysis (GSNA). The difference between
GSNA and popular GSA methods is whether it is univariate or multivariate, and the advantage ofthis analysis is that it can be scored by the similarity of fluctuation patterns within the group.
Concretely, GSNA builds a gene network in each gene set by correlation coefficients and uses
common network summary values for the gene set score.
We applied GSNA to time course data of vitamin D (VD) treated cell lines. GSNA detected the
suppression of the “Interferon alpha beta signaling” gene set by the VD treatment. Negative
correlations between VD and SLEDAI scores or interferon-alpha levels in SLE patients have beenreported [3] and it indicated that GSNA is useful.
[1] Maleki, F., Ovens, K., Hogan, D. J., & Kusalik, A. J. (2020). Gene Set Analysis: Challenges,
Opportunities, and Future Research. Frontiers in genetics, 11, 654.
[2] Subramanian A., Tamayo P., Mootha V. K., Mukherjee S., Ebert B. L., Gillette M. A., et al.
(2005). Gene set enrichment analysis: a knowledge-based approach for interpreting
genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550.
[3] Mandal M, Tripathy R, Panda AK, Pattanaik SS, Dakua S, Pradhan AK, Chakraborty S,
Ravindran B, Das BK. Vitamin D levels in Indian systemic lupus erythematosus patients:
association with disease activity index and interferon alpha. Arthritis Res Ther. 2014 Feb
10;16(1):R49.

44

P07-02(F-6)

Comparison of gene co-expression inference methods
based on biological validity and diversity
Iori Azuma 1
azuma-iori@g.ecc.u-tokyo.ac.jp

Tadahaya Mizuno 1, †Hiroyuki Kusuhara 1, †tadahaya@mol.f.u-tokyo.ac.jp

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

1

Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan†
Author to whom correspondence should be addressed

Keywords: Gene Co-expression Network, Network analysis, Transcriptomics
Co-expression patterns derived from omics data such as transcriptomics form a complex network. The
modules, sub-networks, in a gene co-expression network (GCN) contain not only information aboutgrouping but also the relationships between individual genes, which indicates that a module in GCN
retains more information than that of a gene ontology (GO) and contributes to the understanding of
diseases. However, while many methods for estimating co-expression relationships indispensable for
GCNs have been developed, their performances of extracting biologically meaningful modules remains
to be evaluated and optimized in transcriptome data derived from clinical specimens. In this study, we
proposed novel metrics to evaluate the biological validity and diversity of modules in humantranscriptome data, and compared relationship inference methods to obtain biologically meaningful
modules from human transcriptome data.
First, we evaluated prediction accuracy of the relationships between transcription factors (TFs) andtheir target genes to compare correlation coefficients and various machine learning m ethods in
inference using the artificial benchmark data used in the DREAM5 competition. Those derived fromhuman transcriptome data (TCGA glioblastoma and GSE4271) were compared as well, althoughranking-based enrichment analysis was employed as an evaluation metric instead of prediction
accuracy due to low reliability of negative examples in real world data. Next, the constructed
inferences were converted to GCNs using planar filtered maximally graph (PMFG) and prediction
accuracy was evaluated in the same way. Finally, we evaluated the biological validity and diversity ofthe modules extracted from the constructed GCNs with novel evaluation approaches based on
ChIP-Atlas, a database of ChIP-seq data, and GO.
For the DREAM5 data, we achieved better prediction accuracy of TFs and their target genes
relationships by using Random Forest and Elastic Net than using correlation coefficients. This shows
the usefulness of machine learning methods in predicting the inference of GCN in these data sets,
which agrees with previous works. Regarding human transcriptome data, ranking-based enrichment
analysis using the relationships between TFs and regulatory genes in the ChIP-Atlas as ground truth
data showed the consistent results with the artificial data sets. On the other hand, the correlation
coefficient was superior in label-based enrichment analysis when the ground truth was divided into twolabels, top and bottom.
Next, we transformed these inferences into PMFGs and gave the properties of complex network. For
the constructed networks, we evaluated the enrichment of target genes in the vicinity of TFs, theshortest path distance (SPD) from TFs to their target genes. We found that the networks using thecorrelation coefficient corresponded better than those using machine learning methods such as Random
Forest and Elastic Net in both cases. Furthermore, when the correspondence between the detected
modules and GO was evaluated by the diversity of assigned GO, based on ontology structure, thenetwork derived from the correlation coefficient inference reflected the most diverse ontology.
We proposed novel metrics to evaluate the biological validity and diversity of modules obtained fromhuman GCN. The analyses using these metrics suggested that, unlike benchmark data such as artificial
data, co-expression inference with correlation coefficients is more useful with regard to extracting
biologically meaningful modules from human data than those with machine learning methods.

45

P07-03(F-6)

Analysis and visualization of single-cell RNA sequencing
data by Partek Flow
Naofumi Seira 1 , Ivan K. Lukic 2 , Alison Hargreaves 2
seira.naofumi@molsis.co.jp

12Life Science Department, MOLSIS Inc., 3-19-9 Hatchobori, Chuo-ku, Tokyo 104-0032, Japan
Partek Incorporated., 624 Trade Center Blvd., Suite E Chesterfield, Missouri 63005, U .S.A.

Keywords: Next generation sequencing, Single-cell RNA sequencing, Partek FlowIn recent years, popularity of single cell based next generation sequ encing has expanded at an
exponential rate due to the ability of single-cell RNA sequencing (scRNA-seq) technologies tointerrogate gene expression levels of individual cells. As such, scRNA-seq allows the user toelucidate cellular heterogeneity and cellular states of various cell types within a heterogenous
populations. For these reasons, scRNA-seq is an indispensable tool in a numerous fields including
cancer biology, immunology, neuroscience, and so forth. However, data analysis is challenging andposes a bottleneck for many research teams. Reasons include data size and complexity,
computational requirements, volume of the data, insufficient availability of bioinformatics
expertise, and existence of innumerable analysis tools with no “industry standard”.
Our solution for the challenge is Partek Flow [1], bioinformatics software which provides acomplete, start-to-finish solution from data import to biological interpretation. Partek Flow
supports data file types generated by all major vendors and does not require programming
knowledge or a bioinformatics background to use . It combines powerful statistics with rich andinteractive visualizations in an intuitive, visual interface (Fig.1) with context-sensitive tools to
guide the user through the analysis process. Partek Flow can process scRNA-seq data fromsequencing files down to gene-barcode matrix, normalize gene expression levels, remove batch
effect and perform exploratory analysis by interactive 2D or 3D plots such as UMAP(Fig.2). Next,
Partek Flow enables detection of novel cell types and their phenotyping by identification of
biomarkers, offers various statistical tools (e.g. hurdle model or DESeq2), biological interpretation
tools (pathways), and enables visualization of results by heat maps, violin plots, hierarchical pie
charts and more. Moreover, cell differentiation can be assessed by trajectory analysis.
In this poster, we present the methods of scRNA-seq data analysis and visualization to identify
different blood cell populations in Peripheral blood mononuclear cell samples in Partek Flow.

Fig.1. Visual interface in Partek Flow.

Fig.2. UMAP plot

[1] Partek Flow; Partek Incorporated; https://www.partek.com/partek-flow/46

P07-04(F-6)

Predicting phase separation protein and its experimental
condition using machine learning
Ka Yin Chin 1
w215418a@yokohama-cu.ac.jp

Shoichi Ishida 1
ishida.sho.nm@yokohama-cu.ac.jp

Tomohiro Sato 1
sato.tom.wa@yokohama-cu.ac.jp

Kei Terayama 1
terayama@yokohama-cu.ac.jp

1

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

Keywords: Liquid-liquid phase separation, Phase separation protein, Machine learning, Biomolecularcondensates

Membraneless organelle is generally formed by a physical phenomenon called liquid-liquid
phase separation (LLPS), which has been intensively studied since the 2010s [1]. LLPS is mainly
mediated through molecular interactions between phase-separation proteins (PSPs). In addition,
environmental conditions such as pH and temperature are essential factors that can alter the
behavior of LLPS [2]. Screening of PSPs and their conditions on a proteome-scale requires
numerous experiments combining multiple conditions, which are very time-consuming and costly.
Recently, various machine learning models have been developed to predict PSPs, and most of themodels were trained based on protein sequences of PSPs and their biochemical features, regardlessof the environmental conditions [3,4]. One of the models, Droppler [5], represented an attempt topredict the LLPS behavior using a protein sequence and given experimental conditions. However,its prediction accuracy was lower than other models, and Droppler requires input of experimental
conditions, which are difficult to estimate. Here, we developed a machine learning model that can
predict both PSPs and their experimental conditions. Our model was based on a Light Gradient
Boosting Machine (LightGBM) strategy and was trained using protein sequences andexperimental conditions obtained from LLPSDB [6]. As a result, our model showed higher
prediction performance than Droppler. Furthermore, we calculated feature importances for ourmodel and analyzed the relationship between PSPs and experimental conditions. We expect that
our model would contribute to the efficient discovery of new PSPs and phase-separating
conditions.
[1] Banani, S. F.; Lee, H. O.; Hyman, A. A.; Rosen, M. K. Biomolecular Condensates: Organizersof Cellular Biochemistry. Nat. Rev. Mol. Cell Biol, 2017, 18 (5), 285–298.
[2] Gomes, E.; Shorter, J. The Molecular Language of Membraneless Organelles. J. Biol. Chem.,
2019, 294 (18), 7115–7127.
[3] van Mierlo, G.; Jansen, J. R. G.; Wang, J.; Poser, I.; van Heeringen, S. J.; Vermeulen, M.
Predicting Protein Condensate Formation Using Machine Learning. Cell Reports, 2021, 34 (5),108705.
[4] Sun, T.; Li, Q.; Xu, Y.; Zhang, Z.; Lai, L.; Pei, J. Prediction of Liquid-Liquid Phase Separation
Proteins Using Machine Learning. SSRN Journal, 2020.
[5] Raimondi, D.; et al., In silico prediction of in vitro protein liquid-liquid phase separation
experiments outcomes with multi-head neural attention. Bioinformatics. 2021, btab350.
[6] Li, Q.; et al., LLPSDB: a database of proteins undergoing liquid–liquid phase separation invitro. Nucleic Acids Res. 2019, 48, D320-327.

47

P08-01(F-6)

Dynamical FMO Interaction Analysis of
SARS-CoV-2 RNA dependent RNA polymerase andRemdesivir
Shuhei Miyakawa 1

Koji Okuwaki 1,2

s181232@hoshi.ac.jp

okuwaki@rikkyo.ac.jp

Yuma Handa 1

Takayuki Furuishi 1

d2002@hoshi.ac.jp

t-furuishi@hoshi.ac.jp

Koichiro Kato 2

Yusuke Kawashima 1
y-kawashima@hoshi.ac.jp

Etsuo Yonemochi 1
e-yonemochi@hoshi.ac.jp

Kaori Fukuzawa 1

kato.koichiro.957@m.kyushu-u.ac.jp k-fukuzawa@hoshi.ac.jp

1
2

3

Hoshi Univ., 2-4-41 Ebara, Shinagawa-ku, Tokyo, 142-8501, Japan
Department of Chemistry and Research Center f or Smart Molecules, Faculty of Science,
Rikkyo Univ., 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
Kyushu Univ., 744 Motoka, Nishi-ku, Fukuoka, 819-0395, Japan

Keywords: SARS-CoV-2, RNA dependent RNA polymerase (RdRp), Molecular Dynamics,
Fragment Molecular Orbital Method (FMO)
Remdesivir (Rem), one of the COVID-19 therapeutic agents, inhibits RNA elongation when it islocated at the -3 position on the RNA recognized by RNA-dependent RNA polymerase (RdRp) of
SARS-CoV-2 virus. In this study, we aim to elucidate the effect of different positions of Rem in the
RNA sequence on the RNA strand elongation process.
Based on the cryo-electron microscopy structure of the RdRp-RNA-Rem complex(PDBID:7BV2), five structures were prepared by replacing the bases at positions +1 to -3 on the
RNA with Rem/Adenine, and molecular dynamics (MD) calculations using the Amber program
were performed for 50 ns each. Then Fragment Molecular Orbital (FMO) calculations were
performed on the 50 extracted structures using the ABINIT-MP program to analyze the interaction
around Rem.
MD calculations revealed that the structure at the end of the elongated strand (+1 position) is
disordered when Rem is located at the position -3, and FMO calculations suggested reduction of its
interaction energy by about 10%. On the other hand, the interaction of Rem at the -3 position withthe surrounding residues were more unstable than that of Adenine. Although a local cation-πinteraction was formed between the cyano-substituted sugar and LYS563, which is characteristic of
the Rem structure, the interaction energy between Rem and whole complex was weakened. These
results suggest that the dynamic behavior and stability of Rem at the -3 position may affect thearrest of RNA elongation.
This research was performed in the activities of the FMO dru g design consortium (FMODD).
The FMO calculations were partially performed using the Fugaku supercomputer (HPCI project
ID: hp210130).
[1] Kitaura et. al.: Fragment molecular orbital method: an approximate computational method forlarge molecules. Chem. Phys. Lett. 313, 701-706 (1999).
[2] Mochizuki Y, Tanaka S, Fukuzawa K, eds.: Recent Advances of the Fragment MolecularOrbital Method: Springer, Singapore; 2021.

48

P09-01(F-6)

infiniSee – Similarity Searching in
Ultra Large Chemical Spaces
Takashi Ikegami 1 , Shinji Amari 1 , Ryoichi Kataoka 1 , Marcus Gastreich 2
ikegami.takashi@molsis.co.jp

1
2

MOLSIS Inc., 3-19-9 Hatchobori, Chuo-ku, Tokyo 104-0032, Japan
BioSolveIT GmbH, An der Ziegelei 79, 53757 St. Augustin, Germany

Keywords: Chemical Space, Similarity Search, Virtual Screening, New Purchasable Compounds
infiniSee[1] is a Chemical Space navigation platform. Based on similarity, in finiSee finds moleculesof interest by screening libraries or Chemical Spaces of almost infinite size. infiniSee opens thepossibility to screen billions of compounds through its similarity search technology. Search in
BioSolveIT partners’ chemical spaces (WuXi’s GalaXi, 2 billion molecules; Enamine’s REAL Space,
20 billion molecules) and literature-based virtual chemical spaces (KnowledgeSpace, 10 14 molecules)
to obtain commercially available and synthesizable compounds. In version 3, OTAVA Chemical’s
CHEMriya has been integrated to further expand the chemical space with another 11 billion molecules
generated by 30,000 building blocks and 44 reactions. Desired compounds of partners can be purchasedand delivered to your laboratory within one month at the earliest, e.g. from Enamine.
The underlying concept of molecular similarity and on-the-fly solution generation use FTrees[2] andFTrees-FS[3] techniques, respectively (Fig. 1). Instead of searching already “assembled” molecules,
infiniSee performs a combinatorial build-up of compounds from “fragments” in Chemical Spaces. The
bonds between fragments are formed according to defined chemical reactions. It forms bonds between
fragments according to defined chemical reactions and outputs unexpected similar compounds thatmatch the chemical characteristics (similar to pharmacophore) of the query.
infiniSee has a graphical user interface that is easy for anyone to use (Fig. 2), and it can be easily
combined with other calculations, such as docking simulation . We talk about an overview of infiniSee
and examples of its applications.

Fig.1. Similarity Search from Chemical Space by infiniSee

Fig. 2. infiniSee GUI[1] infiniSee ver. 3.0; BioSolveIT GmbH; 2021, https://www.biosolveit.de/infiniSee/ (accessed2021-7-27)[2] Rarey, M.; Dixon, J. S. Feature trees: a new molecular similarity measure based on tree
matching. J. Comput. Aided Mol. Des. 1998, 12, 471-490. DOI: 10.1023/A:1008068904628[3] Rarey, M.; Stahl, M. Similarity searching in large combinatorial chemistry spaces. J. Comput.
Aided Mol. Des. 2001, 15, 497-520. DOI: 10.1023/A:1011144622059

49

P09-02(F-6)

Development of SBDD Software Considering Empirical
Scores with Hydrophobic Heteromeric Interactions toReproduce the ChEMBL Database
Akihiro Masuda 1

Hideaki Umeyama 2

a15.783r@g.chuo-u.ac.jp

umeyama@bio.chuo-u.ac.jp

Mitsuo Iwadate 1,2

Takumi Hirao 2

a18.ynm7@g.chuo-u.ac.jp12iwadate@bio.chuo-u.ac.jp

Department of Biological Sciences, Graduate School of Science and Engineering, Chuo
University, 1-13-27, Kasuga, Bunkyo-ku, Tokyo,112-8551, Japan
Department of Biological Sciences, Faculty of Science and Engineering, Chuo University,
1-13-27, Kasuga, Bunkyo-ku, Tokyo,112-8551, Japan

Keywords: Structure-based drug design(SBDD), Hydrophobic Interaction, Empirical Potential
The Structure-based drug design(SBDD) software was developed with both database searching andphysical interactions, mainly hydrophobic interactions. It was developed using a combination of
Pyhon and RDkit libraries in order to easily add new features to the existing ChooseLD program. It
is a tremendously time-consuming task to recreate a large database, but time saving has been
attempted. Many of the internal variables of the software have been optimized in the ChEMBL
database. We examined the characteristics of these results in detail. We then performed a SCOP
classification on the protein side and discussed when the application in this study would be more
useful.
[1] Landrum G. RDKit http://www.rdkit.org[2] Takaya, D., Takeda-Shitaka, M., Terashi, G., Kanou, K., Iwadate, M., and Umeyama, H.,
Bioinformatics based Ligand-Docking and in-silico screening, Chem Pharm Bull(Tokyo),
56:742-744, 2008.
[3] Gaulton, A., Hersey, A., Nowotka, M., Bento, AP., Chambers, J., Mendez, D., Mutowo, P.,
Atkinson, F., Bellis, LJ., Cibrián-Uhalte, E., Davies, M., Dedman, N., Karlsson, A., Magariños,
MP., Overington, JP., Papadatos, G., Smit, I., and Leach, AR. The ChEMBL database in 2017.
Nucleic Acids Res., 45:D945-D954, 2017.

50

P09-03(F-6)

Identification of bitter-related compounds against hT2R14
by structure-based virtual screening
Kohei Kuriki 1

Ryo Matsumoto 2

kuriki.kohei947@mail.kyutech.jp

12

ryo_matsumoto@ajinomoto.com

Chiori Ijichi 2

Shunsuke Aoki 1chiori_ijichi@ajinomoto.com

aokis@bio.kyutech.ac.jp

Depertment of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, japan
Ajinomoto Co., Inc., 1-1, Suzuki-Cho, Kawasaki-Ku, Kawasaki-Shi 210-8681, Japan

Keywords: structure-based virtual screening, G-protein coupled receptor, bitter taste receptor,
calcium imaging assay
Human recognizes tastes of sweet, umami and bitter through G-protein coupled receptor (GPCR)
on the tongue [1-2]. The bitterness among these tastes is detected by about 25 human Taste 2
Receptors (hT2Rs) [3] and is related to the acceptance of foods and drugs. In addition, hT2Rs are
expressed in extra-oral tissues and their physiological roles have received a lot of attention [4-5].
In this study, we focused on hT2R14 in hT2R family members, widely related to bitterness andexpressed in extra-oral tissues such as bronchus and testis [6-8]. HT2R14 recognizes structurally avariety of chemical compounds and plays important physiological roles, thus hT2R14 is thought tobe an attractive research target. We performed hierarchical structure-based virtual screening(SBVS) and subsequent experimental assay to identify the bitter and bitter blocker compounds ofhT2R14. The binding affinities of 154,118 compounds against hT2R14 were estimated by rigid
docking simulations in the first screening and further docking simulations with genetic algorithm
in the second screening. The selected 10 compounds through the SBVS were experimentally
evaluated the efficacy of hT2R14 activation by calcium imaging assay. As a result, we identified
three novel bitter blockers and one bitter compound. The SBVS method is effective way forsearching novel bitter-related compounds.
[1] Hoon, M. A.; et al., Putative Mammalian Taste Receptors: A Class of Taste-Specific GPCRs
with Distinct Topographic Selectivity. Cell 1999, 96 (4), 541–551.
[2] Zhao, G. Q.; et al., The Receptors for Mammalian Sweet and Umami Taste. Cell 2003, 115 (3),
255–266.
[3] Go, Y.; et al., Lineage-Specific Loss of Function of Bitter Taste Receptor Genes in Humans andNonhuman Primates. Genetics 2005, 170 (1), 313–326.
[4] Trivedi BP. Neuroscience: hardwired for taste. Nature. 2012 Jun 20;486(7403):S7-9.
[5] Clark, A. A.; et al., TAS2R Bitter Taste Receptors Regulate Thyroid Function. FASEB J. 2015,
29 (1), 164–172.
[6] Behrens, M.; et al., The Human Taste Receptor HTAS2R14 Responds to a Variety of Different
Bitter Compounds. Biochem. Biophys. Res. Commun. 2004, 319 (2), 479–485.
[7] Deshpande, D. A.; et al., Bitter Taste Receptors on Airway Smooth Muscle Bronchodilate by a
Localized Calcium Flux and Reverse Obstruction. Nat. Med. 2010, 16 (11), 1299–1304.
[8] Governini, L.; et al., Expression of Taste Receptor 2 Subtypes in Human Testis and Sperm. J.
Clin. Med. 2020, 9 (1), 264.

51

P09-04(F-6)

A computational study for the development of anti-prion
compounds using the docking simulation
Daisuke Arimura 1
K2297171@kadai.jp

1

Takeshi Ishikawa 1

ishi@cb.kagoshima-u.ac.jp

Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of
Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima, Kagoshima
890-0065, Japan

Keywords: Docking simulation, prion disease, anti-prion compounds
Prion protein (PrP) is a key molecule in prion diseases, including transmissible spongiform
encephalopathies in humans and mammals. Two different conformations of PrP are currently
recognized at least, cellular form (PrP C ) and scrapie form (PrP Sc ), and the conformational conversion
from PrP C into PrP Sc is considered to cause prion diseases. We had attempted to develop new
therapeutic agents for prion diseases by identifying new compounds that bind strongly to PrP C andinhibit its conformational change [1-3]. Through these studies, however, it was found to be difficult toobtain a sufficiently high correlation between the results of docking simulation and experimental
assays only by the simple comparison of them. Thus, it is necessary to make a new model forconnecting them. In this study, to improve the correlation between the docking simulation andexperimental results, we tried to make a model for ranking the compounds, which specialized foranti-prion compounds.
Firstly, all the three-dimensional structures of PrP C deposited in Protein Data Bank were downloaded
and carefully examined, resulting that we obtained 86 structures which are able to be used as thereceptor for the docking simulations. Next, the docking simulations using these PrP C structures were
performed for 96 compounds whose binding affinity and anti-prion activity were reported in theprevious studies [1]. In this study, AutoDock Vina [4] was used for the docking simulation. Finally, we
made rankings of the compounds using different ways than the ranking by the typical docking
simulation with a single receptor structure. As a result, it was found that a ranking method where not
only the energies but also the docking structures of the compounds are considered could provide a morecorrelated ranking with the experimental assays.

[1] D. Ishibashi, et al., Structure-based drug discovery for prion disease by using a novel binding
simulation, EBioMedicine, 9 (2016) 238.
[2] Y. Miyazaki, et al., Identification of alprenolol hydrochloride as an anti-prion compound using
surface plasmon resonance imaging, Mol. Neurobiol. 56 (2019) 367.
[3] D. Ishibashi, et al., Novel compounds identified by structure-based prion disease drug
discovery using in silico screening delay the progression of an illness in prion-infected mice,
Neurotherapeutics, 17 (2020) 1836.
[4] O. Trott, et al., AutoDock Vina: improving the speed and accuracy of docking with a newscoring function, efficient optimization, and multithreading. Journal of computational chemistry, J.
Comput. Chem., 31, 455.

52

P09-05(F-7)

Evaluation of Estrogen Receptor-Ligand Binding
Properties Using Dynamical FMO Analysis
Arisa Komiya 1

Minami Suzuki 1 Yuma Handa 1 Koji Okuwaki 1,2
Kaori Fukuzawa 1 Etsuo Yonemochi 1

Takayuki Furuishi 1

S171094@hoshi.ac.jp s181125@hoshi.ac.jp d2002@hoshi.ac.jp okuwaki@rikkyo.ac.jp
t-furuishi@hoshi.ac.jp k-fukuzawa@hoshi.ac.jp e-yonemochi@hoshi.ac.jp
12

School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan

Keywords: Estrogen receptors, Fragment molecular orbital method, Molecular dynamics,
protein-ligand interaction
Estrogen receptors (ERs) have α/β subtypes. The endogenous hormone 17β -Estradiol (EST) binds
to both ERα and ERβ, but flavonoid ligands such as Prinaberel (PRI), Genistein (GEN), andDaidzein (DAI) are known to be highly β-selective. PRI is about twice as likely to bind to ERβ as
GEN, while DAI does not bind to ERβ as much. In this study, we use both molecular dynamics(MD) and the fragment molecular orbital (FMO) [1] methods to clarify the β-selectivity andbinding properties of flavonoid ligands.
We performed 50 ns molecular dynamics (MD) simulations of eight ER-ligand complexes
combining four ligands (EST, PRI, GEN, and DAI) and ERα/β using the Amber10:EHT force field.
Subsequently, FMO calculations at the MP2/6-31G* level were performed for each 5ns structure of
the MD calculation results to analyze ligand interaction energies and their components.
Comparing the ligand binding pocket sizes of ERα and ERβ, the volume of ERα was found to be
1.1 times larger than that of ERβ. The residues around the ligand were also more flexible in ERα. It
was suggested that the flavonoid ligands are not readily bound to ERα because of a rotatable bond
between A and C rings. The differences between the ligands were analyzed using inter-fragmentinteraction energies (IFIEs). The sum of IFIE between PRI and ERβ suggests that binding of PRI
was the most stable, and the electrostatic interaction between the fluorine atom of PRI and Arg346
contribute to the stability. For GEN, the interaction of the hydroxyl group of the D ring with Ile376,
Phe377, and the oxygen atom in the C ring with Thr299 was involved in the binding to ERβ. No
specific interaction was observed for DAI with surrounding amino acid residues due to the absenceof a vinyl or hydroxyl group on the D ring, suggesting that DAI is weakly bound to ERβ.
This research was performed in the activities of the FMO drug design consortium (FMODD).TheFMO calculations were partially performed using the Fugaku supercomputer (HPCI project ID:
hp210130).
[1] Mochizuki Y, Tanaka S, Fukuzawa K, eds.: Recent Advances of the Fragment MolecularOrbital Method: Springer, Singapore; 2021.

53

P09-06(F-7)

Interaction Analysis of GPCR in the Presence of Lipid
Bilayer Using Fragment Molecular Orbital (FMO) Method
Taro Imahiro 1

Yuma Handa 1

S181025@hoshi.ac.jpd2002@hoshi.ac.jp

Yusuke Kawashima 1

Koji Okuwaki 1, 2

y-kawashima@hoshi.ac.jp

okuwaki@rikkyo.ac.jp

Etsuo Yonemochi 1

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

Kaori Fukuzawa 1

e-yonemochi@hoshi.ac.jp

k-fukuzawa@hoshi.ac.jp

1

Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Department of Chemistry and Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
2

Keywords: G protein-coupled receptor (GPCR), Lipid bilayer, CHARMM-GUI, Fragment
Molecular Orbital (FMO) Method, PIEDA, Protein-Ligand Interaction
G protein-coupled receptors (GPCRs), which are membrane proteins, regulate a variety ofphysiological functions in vivo and are an important family of protein targeting for drug discovery.
Since GPCRs function in lipid bilayer in vivo, it is essential to elucidate the interaction between
GPCRs and drug candidates in the presence of lipid bilayer in structure -based drug design.
In this study, we used the fragment molecular orbital (FMO) method [1, 2] to analyze theinteraction of the GPCR and the lipids that make up the bilayer and the ligand. The human β2adrenergic GPCR (PDB ID: 2RH1) was modeled in POPC lipid
bilayer using CHARMM-GUI [3], relaxed, and then we carried out
FMO calculations at the MP2/6-31G* level using Fugaku. We also
analyzed the interaction around the receptor by partitioning theinteraction energy components.
The interaction between the phospholipid heads and the GPCR
showed strong electrostatic interactions with charged side chainamino acids such as Arg, Lys, Tyr, and Gln, while the phospholipid
tails was sterically sandwiched by Leu of the GPCR, where strong
dispersion interactions were observed. In addition, the interaction
between the ligand and the GPCR was enhanced for Ser, while theinteraction was decreased at the place of Asp and Asn due to theFig. 2RH1 modeled in lipid bilayer
presence of the lipid bilayer.
This research was performed in the activities of the FMO drug design con sortium (FMODD).
The FMO calculations were partially performed using the Fugaku supercomputer (HPCI project ID:
hp210130)[1] Kitaura et al. (1999): Fragment molecular orbital method: an approximate computational method
for large molecules. Chem. Phys. Lett. 313, 701-706.
[2] Mochizuki et al. (2021): Recent Advances of the Fragment Molecular Orbital Method: Springer,
Singapore.
[3] S. Jo et al. (2008): CHARMM-GUI: A Web-based Graphical User Interface for CHARMM. J.
Comput. Chem. 29:1859-1865.

54

P09-07(F-7)

Evaluation of binding property ofnitrocatechol inhibitors with COMT
by the fragment molecular orbital method
Satoko Shirai 1

Ami Yamamoto 1

S181114@hoshi.ac.jp

Takayuki Furuishi 1

Etsuo Yonemochi 1

s161273@hoshi.ac.jp t-furuishi@hoshi.ac.jp e-yonemochi@hoshi.ac.jp

Katsuki Takebe 2

Narikazu Uzawa ²

takebek@dent.osaka-u.ac.jp

Hiroshi Iijima 3

uzawa@dent.osaka-u.ac.jp

Kaori Fukuzawa 1

iijima.hiroshi@nihon-u.ac.jp k-fukuzawa@hoshi.ac.jp

1
2
3

Hoshi Univ.,2 4 41 Ebara, Shinagawa-ku, Tokyo, 142-8501, Japan
Osaka Univ.,1 8 Yamadaoka, Suita-shi, Osaka,565-0871, Japan
Nihon Univ.,7 7 1 Narashinodai, Funabashi-shi, Chiba, 274-8555, Japan

Keywords: Catechol-O-methyltransferase, Fragment Molecular Orbital
structure-based drug design (SBDD), binding affinity, pharmacophore(FMO)

Method,COMT inhibitors for the treatment of Parkinson's disease are known to be hepat otoxic due to thenitrocatechol (NC) skeleton. Development of inhibitors with non-NC skeletons is expected, but has
not yet realized. In this study, we have evaluated the effects of nitro substituents on the binding
and reactivity of COMT inhibitors by structure-based drug design (SBDD) using quantum
chemical calculation to provide information for the design of non-NC inhibitors.
Molecular modeling was performed for six COMT-inhibitor complexes based on their crystal
structures. First, the AMBER 10: EHT force field was used to optimize hydrogen atoms and watermolecules. Second, the positions of hydrogen atoms in the side chains of K144 and E199 were
optimized at the B3LYP/6-31G* level using small model structure. Third, fragment molecularorbital (FMO) [1] calculations at the MP2/6-31G* level were performed for the whole
COMT-inhibitor complexes.
In the COMT-inhibitor complexes, M40, D141, K144, D169, N170, and E199 commonly interact
with the catechol skeleton of the inhibitors. Among them, D141, D169, and N170 play the role ofMg-coordinated ligands. W38, P174, L198 and M2 01 interact with the non-nitro functional groupsof the inhibitors. Some inhibitors form CH/π interactions with W38 and L198, and dispersion
interactions with M201. P174 exhibits various types of dispersion interactions depending on theinhibitor. In addition, charge distribution analysis showed that the sum of the charges of six carbon
atoms of the catechol ring varied with the number of the nitro group substitutions. Comparison of
the net charge of the inhibitors with that of substrate suggested that the electron-withdrawing
nature of the nitro group was essential for the inhibition of the enzymatic reaction. Substitution of
the nitro group with a fluorine atom resulted in a charge distribution similar to that of
nitrocatechols, suggesting that a fluorine atom can be a substitute for nitro groups.
This research was performed in the activities of the FMO drug design consortium (FMODD).
FMO calculations were partially performed using the Fugaku supercomputer (HPCI project ID:
hp210130). This study was partially supported by the AMED-BINDS project (grant number
JP21am0101113).
[1] Mochizuki Y, Tanaka S, Fukuzawa K, eds.: Recent Advances of the Fragment MolecularOrbital Method: Springer, Singapore; 2021.

55

P09-08(F-7)

Inhibitor discovery targeting UHRF1 by
MD simulations and biochemical analyses
Yuki Shibahashi 1
w195418g@yokohama-cu.ac.jp

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

Satomi Kori 1
w195502g@yokohama -cu.ac.jp

Tsutomu Yamane 2

Kyohei Arita 1

Mitsunori Ikeguchi 1,2

tsutomu.yamane@riken.jp

aritak@yokohama-cu.ac.jp

ike@yokohama-cu.ac.jp

12

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

Keywords: in Silico high-throughput screening, Molecular dynamics, UHRF1
The accumulation of epigenetic alternation is one of the major causes of tumorigenesis. Because
aberrant DNA methylation patterns cause genome instability and silencing of tumor suppressor
genes in various types of tumor, drug discovery targeting the DNA methylation -regulating factors
has a potential to develop a cancer treatment drug. In mammals, ubiquitin-like containing PHD and
RING finger domain 1 (UHRF1) plays an essential factor for DNA m ethylation maintenance [1,2].
In addition, UHRF1 is overexpressed in various cancer cells , and down-regulation of UHRF1
reactivates the expression of tumor suppressor genes, suggesting inhibitor of UHRF1 is promisingfor drugs. UHRF1 consists of multi doma ins. In particular, the peptide binding groove in tandem
Tudor domain (TTD-groove) functions as a binding platform for inter- or intra-molecularinteractions, and the recruitment of UHRF1 to replication sites is triggered by the interaction of
DNA ligase 1 di/trimethylated on Lys126 (LIG1K126me3) to the TTD-groove. Therefore, we
focused on inhibiting the interaction between TTD-groove and LIG1K126me3.
To find candidate inhibitors, in-silico high-throughput screening cycle was developed andperformed using over 200,000 compounds. This cycle consisted of docking simulations, molecular
dynamics (MD) simulations, binding free -energy calculations, and dynamical pharmacophore
screening. The binding affinity was evaluated by the docking score, MM -GBSA score, and thebinding free energy, and the pose stability was evaluated by RMSD using three independent
all-atom 10-ns MD simulations. Because it is generally not known which of the computationalscores and/or indicators in the in-silico scheme correlate with the affinity evaluated experimentally,
the top-ranked compounds were selected from multiple scores and indicators. As a results, weselected 130 candidates by this cycle, and succeeded in obtaining two hit compounds confirmed byexperimental thermal stability assay and isothermal titration calorimetry. The hit compound was
the top performer in three of the four indicators, i.e., all except for the docking score. In particular,
the binding pose of the hit compound was quite stable in the 10 -ns MD simulation. As a validation,
we solved a crystal structure of the hit compound bound UHRF1 complex. The binding pose in the
crystal structure was in good agreement with that observed in the MD simulations. In retrospective
evaluation, the two hit compounds were evaluated as promising by MD-based metrics, and the hit
rate among the compounds was 10.5%, which was higher than a commonly mentioned rate, 1%,
indicating that MD simulation was useful to screen the compound s which bind to the target
protein.
[1] Sharif, J.; et al., The SRA Protein Np95 Mediates Epigenetic Inheritance by Recruiting Dnmt1
to Methylated DNA, Nature, 2007, 450, 908-912.
[2] Bostick, M.; et al., UHRF1 Plays a Role in Maintaining DNA Methylation in Mammalian Cells,
Science, 2007, 317, 1760-1764.

56

P09-09(F-7)

Rational design of a helical peptide inhibitor targetingc-Myb–KIX interaction
Shunji Suetaka 1

Yoshiki Oka 1

shunji-suetaka@g.ecc.u-tokyo.ac.jp

oka-yoshiki333@g.ecc.u-tokyo.ac.jp

Tomoko Kunihara 1

Yuuki Hayashi 1

symbol.of.upbeat.life@gmail.com

hayashi@bio.c.u-tokyo.ac.jp

Munehito Arai 1,2
arai@bio.c.u-tokyo.ac.jp

12

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo,
3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
Department of Physics, Graduate School of Science, The University of Tokyo, 3-8-1 Komaba,
Meguro, Tokyo, 153-8902, Japan

Keywords: Peptides, Protein design, Intrinsically disordered protein, Drug discovery
The transcription factor c-Myb promotes the proliferation of hematopoietic cells by interacting with
the KIX domain of CREB-binding protein. However, its aberrant expression causes serious diseases
including leukemia [1]. Therefore, inhibiting the c-Myb–KIX interaction is a promising strategy fordeveloping novel drugs. Here, we rationally designed a helical peptide inhibitor of the c-Myb–KIX
interaction using the c-Myb transactivation domain (TAD) peptide fragment. Since the c-Myb TAD is
intrinsically disordered and binds KIX via a conformational selection mechanism where helix
formation precedes binding [2], stabilizing the helical structure is expected to increase the binding
affinity to KIX. We introduced mutations into the c-Myb TAD peptide to stabilize the helical structure
based on theoretical predictions using AGADIR. Three of the four initially designed peptides each had
a different Lys-to-Arg substitution on the helix surface opposite the KIX -binding interface.
Furthermore, the triple mutant with three Lys-to-Arg substitutions, named an RRR inhibitor, showed ahigh helical propensity and achieved a high affinity to KIX with a dissociation constant of 80 nM.
Moreover, the RRR inhibitor efficiently competed out the c -Myb–KIX interaction. Interestingly, thehelical contents of designed peptides and their bind ing affinities showed a good correlation. These
results suggest that stabilizing the helical structure based on theoretical predictions, especially by
conservative Lys-to-Arg substitutions, is a simple and useful strategy for designing helical peptide
inhibitors of protein-protein interactions.

[1] Oh, I.H.; Reddy, E.P. The myb gene family in cell growth, differentiation and apoptosis ,Oncogene, 1999, 18, 3017 – 3033.
[2] Arai, M.; et al., Conformational propensities of intrinsically disordered proteins influence themechanism of binding and folding, Proceedings of the National Academy of Sciences of the UnitedStates of America, 2015, 112, 9614 – 9619.

57

P09-10(F-7)

PPAR subtype-dependent ligand binding investigatedby docking simulation study
Yuka Yamashita 1

Miyuki Tanaka 2

20bs117j@shinshu-u.ac.jp

Eriko Misawa 2

Koji Umezawa 1,3

e_misawa@morinagamilk.co.jp

123

m_tanaka@morinagamilk.co.jp

koume@shinshu-u.ac.jp

Grad. Sch. of Sci. & Tech., Shinshu University, Minami-minowa 8304, Kami-ina, Nagano,399-4598, Japan.
Morinaga Milk Industry Co., Ltd., Higashihara 5-1-83, Zama, Kanagawa, 252-8583, Japan.
Institute for Biomedical Sciences, Shinshu University, Minami-minowa 8304, Kami-ina,
Nagano, 399-4598, Japan.

Keywords: Ligand docking, Nuclear receptor, Aloe sterol
Peroxisome proliferator-activated receptor (PPAR) is a nuclear receptor to regulate lipid andcarbohydrate metabolism via gene expression. The three subtypes of PPAR are known and are
named as PPARα, PPAR and PPAR. The PPARα,  and  contain a ligand-binding domain (LBD).
The structures of LBDs are similar in PPARα,  and , which have a pocket for ligand binding. A
lot of ligands for PPAR LBDs were reported: endogenous compounds of fatty acids, synthetic
compounds of drugs and food-derived compounds. The specific and non-specific compounds for
PPARα,  and  have been developed. Although the complex structures of LBD-ligand have been
determined experimentally, the recognition mechanism related to ligand specificity are still
ambiguous.
Then, to elucidate the ligand binding specificity for PPAR LBDs, we performed docking
simulation for all pairs of the PPAR-LBD structures and the ligands taken from the complex
structures. The 55 PPARα, 44 PPAR and 232 PPAR structures in Protein Data Bank were used.
We took total of 270 compounds from these complex structures, and we built these compoundmodels for docking simulation. The results suggested that the predicted binding energy depend on
the docking site. So, the exploring region for docking should be important to predict the correct
specificity.
Furthermore, we investigated the compounds whose complex structures with PPAR LBD have notbeen determined. Then, we focused on sterols derived from Aloe vera. The Aloe sterols are
assigned into two groups of lophenol and cycloartanol. The lophenol can activate PPAR and
PPARα more strongly than the cycloartanol do. The molecular models of lophenol and cycloartanol
were built and were used for docking with the PPAR-LBD structures. The results show that thepredicted binding energy of lophenol is lowest in complex with PPAR. The docking pose oflophenol was different in PPARα,  and . The details of docking poses and energy will be
discussed by comparing natural and synthetic compounds.

58

P13-01(F-7)

In silico-guided design of growth factor mutants
with varied physicochemical parameters
Okada Yuga 1
okada-yuga112@g.ecc.u-tokyo.ac.jp

Akihiro Eguchi 1
320akihiro0806@gmail.com

Ryosuke Ueki 1

Shinsuke Sando 1

r.ueki@chembio.t.u-tokyo.ac.jp

1

Daisuke Kuroda 1
dkuroda@g.ecc.u-tokyo.ac.jp

ssando@chembio.t.u-tokyo.ac.jp

The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Keywords: In silico protein design, Growth factor, Signal transduction
Receptor tyrosine kinases (RTKs) are activated through the dimerization induced by growth
factor and mediate the signal transduction with multifaceted effects such as cell proliferation,differentiation, and migration. The cell signaling mediated by RTKs has been attracting attention
in regenerative medicines and pharmaceutical applications. Thus, it is important to understand thesignaling mechanisms of RTKs at molecular levels. Heparin, a naturally occurring
glycosaminoglycan, is associated with the regulation of RTK signaling. Previous studies proposedseveral possible roles of heparin in RTK signaling, such as promoting oligomerization of growth
factors and RTKs [1] and improving the thermal stability and proteolysis resistance of growth
factors [2,3]. To elucidate the role of heparin in the signal transduction, several growth factor
mutants with enhanced or decreased heparin affinity, receptor affinity, and thermal stability havebeen designed based on the crystal structure analysis and consensus approaches [4-7]. However,these approaches still have limitations in designing growth factor mutants with the desired
physicochemical parameters. In the present study, we aimed to design mutants of a growth factor
with varied heparin affinity and thermal stability based on in silico design approach, which would
allow the development of novel mutants with desired properties. In this presentation, the design
outline of growth factor mutants, expression of the designed mutants, and evaluation of their
physicochemical properties will be discussed.
[1] Harmer, N. J.; et al., Multimers of the fibroblast growth factor (FGF)-FGF receptor-saccharide
complex are formed on long oligomers of heparin, Biochem. J., 2006, 393, 741–748.
[2] Nguyen, T. H.; et al., A heparin-mimicking polymer conjugate stabilizes basic fibroblast
growth factor, Nat. Chem., 2013, 5, 221‒227.
[3] Caldwell, M. A.; et al., Heparin stabilizes FGF-2 and modulates striatal precursor cell behavior
in response to EGF, Exp. Neurol., 2004, 188, 408–420.
[4] Koledova, Z.; et al., Fibroblast growth factor 2 protein stability provides decreased dependence
on heparin for induction of FGFR signaling and alters ERK signaling dynamics, Front. Cell Dev.
Biol., 2019, 7, 1–15.
[5] Zakrzewska, M.; et al., Increased protein stability of FGF1 can compensate for its reduced
affinity for heparin, J. Biol. Chem., 2009, 284, 25388–25403.
[6] Zakrzewska, M.; et al., Highly stable mutants of human fibroblast growth factor-1 exhibit
prolonged biological action, J. Mol. Biol., 2005, 352, 860–875.
[7] Huang, Z.; et al., Uncoupling the Mitogenic and Metabolic Functions of FGF1 by Tuning
FGF1-FGF Receptor Dimer Stability, Cell Rep., 2017, 20, 1717–1728.

59

P13-02(F-7)

Application of Scilligence Software
for Peptide Drug Discovery
Yasuhiro Shinozaki 1
shinozaki.yasuhiro@molsis.co.jp
12

Jonathan Buttrick 2
jonathan.buttrick@scilligence.com

MOLSIS Inc., 3-19-9, Hatchobori, Chuo-ku, Tokyo 104-0032, Japan
Scilligence Corporation, 625 Mount Auburn St., Suite 203, Cambridge, MA 02138, U.S.A.

Keywords: Data Management, HELM
Nowadays, modality in drug discovery has been diversified, and peptides, antibodies,oligonucleotides and their conjugates are targeted as pharmaceutical R&D in addition toconventional small molecules. For that reason, Scilligence Corporation [1] has developed products
of informatics system that supports various drug modalities. In this poster presentation we would
like to introduce an application case of Scilligence products for the research on peptide drug
discovery.
In case of peptide drug discovery, properties of peptides (binding to the target, stability, etc.) are
optimized by changing some amino acid residues to develop more efficient peptides. But it is an
issue that this study takes a long time, because a huge number of experiments by combination ofamino acid residues. This application case shows that introduction and customization of RegMol,
one of Scilligence products, made generation and filtering of thousands of mutated peptides easier,and the efficiency of R&D was greatly improved as a result.
RegMol is the software for registration of sample information and assay data using the samples. It
supports HELM ( Hierarchical Editing Language for Macromolecules ) [2] and can handle
peptide easily. In addition, in this application case RegMol was customized as follows to suite theuser's research:- adding the enumerator that can quickly create a wide variety of peptides with varied mutations
based on a template peptide- configuring natural/unnatural amino acids and chemical monomers to be used with theenumerator- configuring monomer scan, deletion scan and the use of multi-chain peptides to work with theenumerator- integration with Pipeline Pilot to filter undesired peptides during enumeration process- improving efficiency of CSV output of thousands of mutated peptides[1] https://www.scilligence.com/web/[2] Zhang, T.; Li, H.; Zi, H.; Stanton, R. V.; Rotstein, S. H. HELM: A Hierarchical Notation
Language for Complex Biomolecule Structure Representation J. Chem. Inf. Model. 2012, 52(10), 2796–2806.

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