10月26日（火）Zoom ブレイクアウトルーム
口頭発表１・２＜口頭発表１＞『計算化学（分子計算）／計算化学（分子認識）／構造生命科学／
新型コロナウイルス対策と感染症対策』 ............................................. 1
座長：石川 岳志（鹿児島大学）、岡田 晃季（日本たばこ産業㈱）
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Yoshiharu Mori (Kobe University)"Free energy profile of tRNA dissociation from ribosome studied bycoarse-grained molecular dynamics simulations"
Mohini Yadav (Department of Engineering, Chiba Institute ofTechnology)"Dynamic residue interaction network analysis of the neuraminidase
H274Y mutant conferring drug resistance in influenza virus"
Hikari Yoshimoto (Graduate School of Science and Engineering,
Kindai University)"Interaction analysis of AKR1C3-inhibitor complexes using fragment
molecular orbital method and molecular dynamics simulation"
Chiduru Watanabe (RIKEN BDR, JST PRESTO)"FMO-based Intermolecular Interaction Energy Analysis between
SARS-CoV-2 Spike Protein and Its Drug Candidate Molecules"
Hiroyuki Okamoto (Department of Biological Sciences, Graduate
School of Science, The University of Tokyo)"Cryo-EM structure of the MT1-Gi signaling complex"
Takashi Matsumoto (Rigaku Corporation)"SynergyUltra opens a new era with digits of different performancesand capabilities in protein crystallography; 4s-4m-40m"
Yuya Nishida (Department of Molecular Pharmacology, National
Cerebral and Cardiovascular Center)"Conserved allostery in heme-copper oxidases"

＜口頭発表２＞『計算化学（分子計算）／創薬応用』 ................................................... 8
座長：関嶋 政和（東京工業大学）、池上 貴史（㈱モルシス）、
渡邉 博文（(株)ウィズメーティス）
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Masatake Sugita (Tokyo Inst. Tech.)"Large-scale membrane permeability prediction of cyclic peptides
crossing a lipid bilayer based on enhanced sampling molecular
dynamics simulations"Genki Kudo (Physics Department, Graduate School of Pure andApplied Sciences, University of Tsukuba)"Prediction of the binding mechanism for DNA methyltransferase 3A
selective inhibitor using molecular simulation approach"
Kazuma Kaitoh (Kyushu Institute of Technology)"Omics-based Generation of Drug Candidate Molecules with Desired
Phenotypes by Machine Learning"
Hajime Takashima (PRISM BioLab Co., Ltd.)"Visualized and quantitative structural analysis of peptidomimetics
for PPI drug discovery"
Mutsuyo Wada (Fujitsu Japan Ltd.)"Redesigning the low-molecular weight SHP2 inhibitors with
Computational Chemistry"
Sota Inoue (Graduate School of Medicine, Kyoto Univ.)"Interpretable Deep Learning Using Multimodal Graph Convolutional
Network for Predicting Compound-Protein Interaction"
Ryuichi Sakate (National Institutes of Biomedical Innovation, Healthand Nutrition)"Drug Target Gene-based Analyses of Drug Repositionability in Rareand Intractable Diseases"

O1-1Free energy profile of tRNA dissociation from ribosome
studied by coarse-grained molecular dynamics simulations
Yoshiharu Mori

Shigenori Tanaka

ymori@landscape.kobe-u.ac.jp

tanaka2@kobe-u.ac.jp

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

Keywords: Protein synthesis, Molecular simulation, Free energy calculation
Ribosomes are large protein-RNA complexes that are responsible for protein synthesis in cells. The
process of protein synthesis in ribosomes mainly consists of three steps: initiation, elongation, andtermination. The elongation process is essential for protein synthesis, while the initiation andtermination processes are also essential for maintaining the high quality of protein synthesis. In
this study, we focused on the initiation process of translation in ribosomes. During the translation
initiation phase, a pre-translational complex consisting of ribosomes and initiation -related proteinsis formed to ensure the initiation of protein synthesis. These initiation -specific proteins are called
initiation factors (IFs), and in the case of bacteria, there are three initiation factor proteins; IF1,
IF2, and IF3. In bacteria, protein synthesis is initiated by methionine bound to a n initiator tRNA.
One of the roles of initiation factors is to enable the selective binding of such tRNAs. This study
aims to elucidate the roles of these factors and ribosomal proteins using computational methods.
Because the pre-translation complex consists of more than 20 protein and RNA molecules, it
is difficult to perform an all-atom molecular dynamics simulation. In this study, we performed
molecular dynamics simulations of the protein-RNA complex using a coarse-grained model toelucidate the roles of initiation factors and ribosomal proteins in the complex. The complex
consists of a small ribosomal subunit, a messenger RNA (mRNA), an initiator transfer RNA(tRNA), and initiation factors IF1, IF2, and IF3. The used structures of the complexes wereobtained by cryo-electron microscopy, and these structures were treated with the Martinicoarse-grained model. The pre-initiation complex is further classified into various states, and in
this study, we focused on the incomplete (state a) and nearly complete (state b) states of thecomplex. The coarse-grained pre-translational complex using the Martini model consisted of about
17,000 particles, and the total number of particles, including solvent, was about 173,000.
Molecular dynamics calculations were performed by GROMACS.
In order to understand how the presence of the initiation factors and the ribosomal proteins
contribute to the stability of the initiator tRNA, coarse-grained molecular dynamics simulations
using umbrella sampling were performed for each of the treated states a and b. The free energy
profile of tRNA dissociation from the complex was calculated. Furthermore, by analyzing thestructure of the ribosome-RNA complex, we revealed how the IFs and ribosomal proteins are
related to the accurate recognition of the initiator tRNA by the ribosome. Comparing the freeenergy profile of the complex in the incomplete state with that in the nearly complete state (state b),
the tRNA-bound state is stable and the activation barrier for dissociation is high in the nearlycomplete state. One of the reasons for these different free energy profiles is that the interactions
between ribosomal proteins and tRNAs are different. The interactions between the C-terminal
region of the ribosomal proteins and tRNA may stabilize the ribosome and tRNA. This interaction
could be necessary for the accurate recognition of tRNA by the ribosome. The structure of
ribosomal proteins and their interactions with tRNA will be discussed in the presentation.

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Dynamic residue interaction network analysis of theneuraminidase H274Y mutant conferring drug resistance ininfluenza virus
Mohini Yadav 1

Manabu Igarashi 2s1879602EC@s.chibakoudai.jp

igarashi@czc.hokudai.ac.jp

Norifumi Yamamoto 1

norifumi.yamamoto@p.chibakoudai.jp

12

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

Keywords: Residue interaction network, Molecular dynamics simulation, Drug resistance,
Influenza virus neuraminidase
Influenza is a highly contagious respiratory disease caused by influenza virus, resulting in mild tosevere illness and sometimes even death. Influenza virus glycoproteins, hemagglutinin (HA) andneuraminidase (NA), play essential roles in its replication process. HA mediates virus entry into
the host cell by binding to the terminal sialic acid (SA) of sugar chains on the host cell surface. NA
plays a role in the virus release, by cleaving the bond between SA and the sugar chain in the laststage of infection, which allows the progeny viruses to liberate to begin infecting the surrounding
host cells.
Oseltamivir (OTV) is a leading NA inhibitor used in the treatment and prevention of influenza.
However, the OTV resistant influenza virus strains are emerging rapidly. One of the major OTV
resistant influenza virus strain exhibits His-to-Tyr mutation at residue 274 in N1 NA. However, themolecular mechanism of the reduction in H274Y mutant N1 NA binding affinity to OTV have notbeen fully elucidated. Hence, in this study, we theoretically investigated the changes inresidue-residue and residue-ligand interactions associated with the H274Y mutation in N1 NA
bound to OTV using dynamic residue interaction network (dRIN) analysis based on molecular
dynamics (MD) simulation. 1
dRIN analysis revealed that the OTV binding site of N1 NA and its H274Y mutation site interact
via the three interface residues, S246, E276, and R292, connecting them. Due to H274Y mutationin N1 NA, the interaction between residue 274 and the three interface residues, S246, E276, andR292, significantly increased, thereby significantly decreasing the interaction between OTV andits surrounding 150-loop residues, D151, and R152. Such changes in residue interactions could
reduce the binding affinity of N1 NA to OTV, resulting in OTV resistance in influenza viruses. In
conclusion, using dRIN analysis, we succeeded in understanding the characteristic changes inresidue interactions due to H274Y mutation in N1 NA, which can elucidate the molecularmechanism of reduction in OTV binding affinity to N1 NA. Finally, the dRIN analysis used in thisstudy can be widely applied to various systems such as individual proteins, protein-protein
complexes, and protein-ligand complexes to characterize the dynamic aspects of the interactions.
[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, e11552.

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Interaction analysis of AKR1C3-inhibitor complexes using
fragment molecular orbital method andmolecular dynamics simulation
Hikari Yoshimoto 1

Norihito Kawashita 22033310131k@kindai.ac.jp

12

nkawashita@life.kindai.ac.jp

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

Keywords: AKR1C3, Fragment Molecular Orbital Method, Molecular Dynamics Simulation
AKR1C3 belongs to the aldehyde-reductase (AKRs) family and has high-homology with AKR1C1,
C2, and C4 [1]. Therefore, there is concern that side effects due to cross -inhibition may occur. In
AKR1C3, it is expected that a more potent and selective inhibitor can be obtained by binding theinhibitor to the SP1 region consisting of Ser118, Asn167, Phe306, Phe311, and Tyr319 [2].
In this study, we performed interaction analysis of AKR1C3 inhibitor complexes and AKR1C2
inhibitor complexes by fragment molecular orbital (FMO) method and molecular dynamics (MDs)simulations to develop inhibitors with high selectivity for AKR1C3.
The crystal structures of AKR1C3 and AKR1C2 inhibitor complex which IC 50 have been known
in the literature [3-5] were downloaded from Protein Data Bank (PDB). Structural modifications
and optimizations were performed using MOE. We also prepared the complexes that was notregistered in the PDB by MOE Dock. 100 ns MD-simulations were performed using GROMACS for
these structures, and a total of 10 structures were acquired in each complex, one for every 10 ns .
FMO calculations were performed for the acquired structures and the inter -fragment interactionenergies (IFIE) and PIEDA between each residue and inhibitor were calculated. ABINIT-MP wasused as the FMO calculation, and MP2/6-31G* was used as the calculation level.
MD-simulations indicated that the interaction between SP1 in the ligand binding pocket and theinhibitor was weak in AKR1C2, and the inhibitor tended to approach Lys84 in many cases, so theinteraction between the SP1 residue and the inhibitor is important. PIEDA also suggest that thedispersion energy (DI) of Phe306, Phe311 of SP1 contribute to the selectivity.
This research was done in activities of the FMO drug design consortium (FMODD). For FMO
calculations, RIKEN R-CCS Fugaku and Oakforest-PACS at JCAHPC were used (hp210130 quota).
[1] Michael, C. B.; et al. Inhibitors of Type 5 17β-Hydroxysteroid Dehydrogenase (AKR1C3):
Overview and Structural Insights, J. Steroid Biochem. Mol. Biol., 2011, 125, 95–104.
[2] Adegoke, O. A.; et al. AKR1C3 as a Target in Castrate Resistant Prostate Cancer, J. Steroid
Biochem. Mol. Biol., 2013, 137, 136–149.
[3] Jack, U. F.; et al., Crystal Structures of Three Classes of Non-Steroidal AntiInflammatory Drugs
in Complex with Aldo-Keto Reductase 1C3, PLOS ONE, 2012, 7.
[4] Chen, M.; et al., Crystal structures of AKR1C3 containing an N-(aryl)amino-benzoate inhibitor
and a bifunctional AKR1C3 inhibitor and androgen receptor antagonist. Therapeutic leads for
castrate resistant prostate cancer, Bioorg. Med. Chem. Lett., 2012, 22, 3492–3497.
[5] Stephen M. F. J.; et al., 3-(3,4-Dihydroisoquinolin-2(1H)-ylsulfonyl)benzoic Acids: highly
potent and selective inhibitors of the type 5 17 -β-hydroxysteroid dehydrogenase AKR1C3, J. Med.
Chem., 2012, 55, 7746–7758.

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FMO-based Intermolecular Interaction Energy Analysis
between SARS-CoV-2 Spike Protein and Its Drug
Candidate Molecules
Chiduru Watanabe 1,2
chiduru.watanabe@riken.jp

Yoshio Okiyama 3okiyama@nihs.go.jp

Kaori Fukuzawa 5

Shigenori Tanaka 4tanaka2@kobe-u.ac.j

Teruki Honma 1

k-fukuzawa@hoshi.ac.jp

honma.teruki@riken.jp

1

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

Keywords: SARS-CoV-2, PPI, antibody, FMODB
Due to the COVID-19 pandemic, researchers haveattempted to identify complex structures of thesevere acute respiratory syndrome coronavirus 2(SARS-CoV-2) spike glycoprotein (S-protein) with
angiotensin-converting enzyme 2 (ACE2), a
blocking antibody, or a drug candidate molecule.
We have performed fragment molecular orbital(FMO) calculations on the complexes of S-proteinwith ACE2 and antibodies to reveal key residues
for molecular recognition mechanism [1]. To
Fig. 1. PIEDA between S-protein and ACE2
quantitatively evaluate hydrogen bonds, XH/πinteractions (X = N, O, and C), and salt bridges, we
performed FMO-based epitope analysis with inter-fragment interaction energy (IFIE) and its
energy decomposition analysis (PIEDA). In addition, molecu lar recognition between S-protein andanother drug candidate molecule will be also repo rted by IFIE/PIEDA analysis. These FMO
calculation data will be registered in FMODB (https://drugdesign.riken.jp/FMODB/).
Acknowledgement
The authors thank Mr. Kazuki Watanabe for fruitful discussion, and Dr. Kikuko Kamisaka and Dr.
Daisuke Takaya for technical support. This research was done in activities of FMO Drug Design
Consortium (https://fmodd.jp/top-en/). The results of FMO calculations were partially obtained us ing
the Fugaku supercomputer (project ID: 210130) and the HOKUSAI supercomputer (RIKEN Advanced
Center for Computing and Communications, Saitama, Japan) . 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 AME D under Grant Number
JP21am0101113. Finally, CW acknowledges JST PRESTO grant (JPMJPR18GD) .
[1] Watanabe C et al., Molecular recognition of SARS-CoV-2 spike glycoprotein: quantum chemical
hot spot and epitope analyses, Chem. Sci., 12, 4722-4739, 2021.

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Cryo-EM structure of the MT 1 -G i signaling complex
Hiroyuki H Okamoto 1 Hirotake Miyauchi 1 Asuka Inoue 2 Francesco Raimondi 3
Hirokazu Tsujimoto 4 Tsukasa Kusakizako 1 Wataru Shihoya 1 Keitaro Yamashita 5
Ryoji Suno 6 Norimichi Nomura 4 Takuya Kobayashi 6 So Iwata 4,7
Tomohiro Nishizawa 8 Osamu Nureki 1
1

2

3

4

5
6
7
8

Department of Biological Sciences, Graduate School of Science, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3, Aoba, Aramaki, Aoba-ku,
Sendai, Miyagi, 980-8578, Japan
Laboratorio di Biologia Bio@SNS, Scuola Normale Superiore, Piazza dei Cavalieri, 7 -56126,
Pisa, Italy
Department of Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, 606 -8541,
Japan
MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB 2 0QH, UK.
Department of Medical Chemistry, Kansai Medical University, Hirakata 573 -1010, JapanRIKEN SPring-8 Center, Sayo, Hyogo, 679-5148, Japan
Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan.

Keywords: Cryo electron microscopy, G-protein coupled receptor, Melatonin
Melatonin (N-acetyl-5-methoxytryptamine) activates melatonin receptors (MT 1 and MT 2 ), which
are one of the G i -coupled class A GPCRs and transduce inhibitory signaling by inhibiting the
adenylyl cyclase (AC). Melatonin thus induces our sleep and modulates our circadian rhythm,
and melatonin receptors have long been regarded as an important therapeutic target for an
insomnia. Although melatonin itself serves as a sleep -inducing supplement, its property is notenough to use clinically, because it is rapidly cleared from o ur body. Therefore, a lot of
melatonin analogs with prolonged release properties have been devel oped so far, such as
ramelteon, agomelatine, tasimelteon.
Recently reported crystal structures of ligand-bound MT 1 and MT 2 elucidated the structural basisof ligand entry and recognition, but the molecular mechanism of the ligand -induced MT 1
structural change that would lead to G i -coupling remains unclear.
Here we report the cryo-EM structure of the MT 1 -G i signaling complex at 3.3 Å resolution. The
structure reveals the receptor activation mechanism, in which the ligand -induced conformationalchanges are propagated to the G-protein coupling interface. As compared to other G i -coupled
receptors, MT 1 exhibits a large outward movement of TM6, which is considered to be a specificfeature of G s -coupled receptors. The structural comparison among the G i - and G s -complexes
demonstrated the conformational diversity of the C-terminal entry of the G i protein, suggesting
the loose and variable interactions at the helix end. These notions, together with our biochemical
and computational analyses, highlight the different binding modes of Gα i and provide the basis
for the selectivity of G-protein signaling.

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SynergyUltra opens a new era with digits of different
performances and capabilities in protein crystallography;
4s-4m-40m
Takashi Matsumoto 1

Kazuaki Aburaya 1

Takashi Sato 1

t-matumo@rigaku.co.jp

aburaya@rigaku.co.jp

satow@rigaku.ac.jp

1

Rigaku Corporation, 3-9-12 Matsubara-cho, Akishima, Tokyo 196-8666, Japan

Keywords: SynergyUltra, 4 seconds whole measurement, 4s-4m-40m
The accurate protein structures and their complex targets compounds are necessary and criticalfor designing and developing effective medicines and potent substances. To date, massive numbersof these structures have been determined by single -crystal X-ray diffraction (SC-XRD) analysis.
Other new technologies like single-particle analysis using a Cryo-electron microscope or, more
recently, utilization of three-dimensional electron diffraction (3D-ED) are emerging to overcome
the difficulty of crystallization and hence to be thought the enhancement in the speed.
The geometry of the atoms defines interactions among t he molecules, and some of those arehighly sensitive to the directions and/or distances; thus, the accurate and precise structur alinformation of the molecule is an indispensable firm base for successful drug design processes. In
this context, SC-XRD is still the best approach if it could be performed as its necessity.
Meanwhile, it has still been widely believed that SC-XRD requires considerable time and effort tocrystalize the target molecules and other efforts to collect the datasets and phasing.
This presentation introduces our next-generation instrument that opens a new era of protein
SC-XRD with laboratory instruments formats. Ultra-fast and accurate measurements of protein
crystals using our latest system SynergyUltra will be presented. The following numbers
characterize the power of SynergyUltra.
-

4 seconds, the whole data collection for the molecular replacement phasing:The 4 seconds in total data collection fulfilling the 1.8Å resolution range. The obtained
results were accurate enough for molecular replacement phasing employing Alpha Fold 2
deduced structure as a searching model. As a result, detailed electron densities wereobtained, in which disorder of the sidechain can be modeled and refined for their
population. Utilizing the SynergyFlow robotics system enables to obtain the unique crystal
structures every several minutes that enable curtain bombing (carpet bombing) structure
analyses of mutant proteins and/or its s complexes with various candidates.

-

4 minutes, the whole data collection good for SAD phasing:The 4 minutes in total data collection fulfill ing the 1.75Å resolution range. Thismeasurement allows obtaining highly even and redundant data sets that are accurate enoughfor successful S-SAD phasing. 97% of residues were automatically modeled and refined toyield the detailed electron densities. 4 minutes measurements would be useful unless above
4 seconds MR experiments did not work well.

-

40 minutes, the whole data collection for ultra -high resolution data collection.
The 40 minutes in total data collection fulfillin g the 1.40Å resolution range. Thismeasurement allows obtaining further precise and highly accurate electron density thatshows the spherical shapes for each atom similar to those obtained in small -molecule
crystallography in that atom types can be identified by their density.

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Conserved allostery in heme-copper oxidases
Yuya Nishida 1,2

Sachiko Yanagisawa 3

nishida.yuya@ncvc.go.jp

sachi.y@sci.u-hyogo.ac.jp

Rikuri Morita 4

Hitomi Yuki 5

morita@ccs.tsukuba.ac.jp

hitomi.yuki@riken.jp

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

1
2

3
4
5

Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center
Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biological
Science
Graduate School of Life Science, University of Hyogo
Center for Computational Sciences, University of Tsukuba
RIKEN Center for Life Science Technologies

Keywords: Ligand-based virtual screening, X-ray crystallography, Cryo-EM,
molecular dynamics, Raman spectroscopy
Heme-copper oxidases (HCOs) are terminal components of the respiratory electron transport
chain. Recent studies have shown the enzymes in the respiratory chain containing HCOs as a targetof antibiotics.
Recently we identified novel inhibitors of mitochondrial cytochrome c oxidase (mtCcO), which isa mammalian HCOs, and determined mtCcO structure in complex with the inhibitors. These
structures showed two inhibitors bound to the same site of mtCcO. The inhibitor binding pocket isdistant from the ligand-binding sites, suggesting that the pocket is an allosteric site of mtCcO.
The allosteric site of mtCcO forms a narrow tunnel with four helices. Three of them areconserved in bacterial HCOs, but the other isn’t. These led us to hypothesize that the allostericinhibitory mechanism is conserved in bacterial HCOs and we could reasonably screen novel
antibiotics from derivatives of mtCcO inhibitor.
To this end, we established a custom library by using the ligand-based virtual screening system,
LAILAPS, with the mtCcO inhibitors as queries from commercially available compounds. The
custom library has a high probability of binding to not only the allosteric site of mtCcO but also
the homologous position in bacterial HCOs: E.coli ubiquinol oxidase (ecoUqO). As results of the
enzymatic analysis with the library, we identified both improved mtCcO inhibitors and importantly
ecoUqO specific inhibitors. Cryogenic electron microscopy shows the ecoUqO specific inhibitor
binds to the corresponding allosteric site in ecoUqO. Finally, antibacterial activity analysis showed
the inhibitor prevented the growth of UqO-dependent E. coli strain, whereas the effect was
abrogated in UqO-independent strain, thus the growth inhibition is not a nonspecific effect.
From these results, we conclude that the allosteric site is conserved among HCOs and can lead tothe development of specific antibiotics for bacterial HCOs. In this presentation, we will alsomention the mechanism of conserved allostery which was unveiled by molecular dynamics analysis
and resonance Raman spectroscopy.

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Large-scale membrane permeability prediction of cyclic
peptides crossing a lipid bilayer based onenhanced sampling molecular dynamics simulations
Masatake Sugita 1
sugita@bi.c.titech.ac.jp

Satoshi Sugiyama 1
sugiyama@bi.c.titech.ac.jp

Takuya Fujie 1
fujie@bi.c.titech.ac.jp

Yasushi Yoshikawa 1

Keisuke Yanagisawa 1

Masahito Ohue 1

yoshikawa@c.titech.ac.jp

yanagisawa@c.titech.ac.jp

ohue@c.titech.ac.jp

Yutaka Akiyama1
akiyama@c.titech.ac.jp
1

Tokyo Inst. Tech., W8-76, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan

Keywords: Cyclic peptides, Membrane permeability, Molecular dynamics
Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs.
However, the membrane permeation mechanism of cyclic peptides with high membrane
permeability is not yet understand. To investigate common features of permeable peptides, it isnecessary to reproduce the membrane permeation process of cyclic peptides through the lipid
bilayer for a large number of peptides. In this study, we first simulated the membrane permeation
process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular
dynamics (MD) and replica-exchange umbrella sampling (REUS) simulations, and predicted
membrane permeability using the inhomogeneous solubility-diffusion model (ISDM) and amodified version of it.[1] Furthermore, we confirmed the effectiveness of this protocol by
predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical
structures, including some confidential designs from a pharmaceutical company. As a result, areasonable correlation between experimentally assessed and calculated membrane permeability
was observed for the peptides, except for strongly hydrophobic peptides. Our analysis of the MD
trajectory demonstrated that most peptides were stabilized in the boundary region between bulk
water and membrane, and that for most peptides the process of crossing the center of the membraneis the main obstacle to membrane permeation. The height of this barrier is well correlated with theelectrostatic interaction between the peptide and the surrounding media. This feature of the result
emphasizes the importance of the smooth desolvation during penetration into the membrane.
[1] Sugita, M.; Sugiyama, S.; Fujie, T.; Yoshikawa, Y.; Yanagisawa, K.; Ohue, M.; Akiyama, Y.
Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer
Based on Enhanced Sampling Molecular Dynamics Simulations. Journal of Chemical
Information and Modeling. 2021, 61, 3681–3695.

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Prediction of the binding mechanism for DNA
methyltransferase 3A selective inhibitor using molecularsimulation approach
Genki Kudo 1
s2120165@s.tsukuba.ac.jp

Yasuteru Shigeta 2

Ryuhei Harada 2

shigeta@ccs.tsukuba.ac.jp

Takatsugu Hirokawa 3

ryuhei@ccs.tsukuba.ac.jp

Ryunosuke Yoshino 3

t-hirokawa@md.tsukuba.ac.jp

yoshino.r.aa@md.tsukuba.ac.jp1

Physics Department, Graduate School of Pure and Applied Sciences, University of Ts ukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
2
Center for Computational Sciences, University of Tsukuba, 1 -1-1 Tennodai, Tsukuba, Ibaraki,305-8577, Japan
3
Transborder Medical Research Center, University of Tsukuba, 1 -1-1 Tennodai, Tsukuba, Ibaraki,305-8577, Japan

Keywords: DNA Methyltransferase, Molecular Dynamics, Drug Selectivity
DNA methyltransferase(DNMT) catalyzes the methylation reaction in the DNA cytosine, andseveral isoforms have been reported, such as DNMT1 and DNMT3A. Although the overall
structure of DNMT1 and DNMT3A are different, the active sites of both proteins are similar toeach other [1]. DNA methylation is involved in the inhibition of gene expression, and DNMT
hypermethylation is related to the formation and progression of cancer cells since it causes
inhibition of the tumor suppressor gene [2]. To prevent hypermethylation, many DNMT inhibitors
have been developed. Especially, inhibitors which bind selectivity to specific a DNMT isoform are
attractive because selective drugs are expected to have few side effects [3].
Halby L et al. [4] have reported a selective inhibitor can bind to DNMT3A with about 100 times
more activity than DNMT1. However, the molecular mechanism of selectivity is still unknown
because the complex structure of DNMT3A and any selective inhibitor has not been clarified.
In this study, we predict the complex structure and the residues responsible for binding
selectivity by molecular simulation. Docking simulations were first carried to generate initialstructures of several candidate complexes, and MD simulations were performed to relax thestructures and analyze the stability of complexes. The most stable complex structure wasdetermined by RMSD analyze and the binding free energy using MM/PBSA method. Finally, we
analyzed the energetic contribution of each residue of the protein, interactions, and the structuraldifference with DNMT1. Binding free energy and interaction analysis are suggested that VAL665
and ARG688 of DNMT3A form an interaction with selective inhibitor. Because these residues are
replaced by MET1169 and ASN1192 in DNMT1, compounds that interact with VAL665 and
ARG688 may selectivity inhibit DNMT3A. In conclusion, we have predicted the complex structureof a DNMT3A–selective inhibitor and the cause of its binding selectivity.
[1] Xie , T.; et al., Insight into the selective binding mechanism of DNMT1 and DNMT3A inhibi tors: amolecular simulation study, Phys Chem Chem Phys, 2019, 21(24), 12931 – 12947.
[2] Saif , I.; et al., Prediction of DNA methylation in the promoter of gene suppressor tumor, Gene,
2018, 651, 166-173.
[3] Yu , J.; et al., DNA methyltransferases: emerging targets for the discovery of inhibitors as potent
anticancer drugs, Drug Discovery Today, 2019, 12, 2323-2331.
[4] Halby, L.; et al., Rational design of bisubstrate-type analogues as inhibitors of DNA
methyltransferases in cancer cells, J Med Chem, 2017, 60(11), 4665 – 4679.

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Omics-based Generation of Drug Candidate Moleculeswith Desired Phenotypes by Machine Learning

1

Kazuma Kaitoh 1

Yoshihiro Yamanishi 1

kkaitoh@bio.kyutech.ac.jp

yamani@bio.kyutech.ac.jp

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

Keywords: Structure generator, Deep generative model, Machine learning, Omics data
The efficient identification of small molecules with desired phenotypes such as bioactivities is achallenging task in the drug-discovery process. In recent years, machine learning techniques havebeen used for de novo drug design. In particular, molecular generative models are able to learn thecharacteristics of specific real-world data and generate the chemical structures of new moleculeswith similar properties [1] . However, the biological phenomena have not been taken into account inmolecular generation.
In this study, we present a novel machine learning method for de novo drug design using omics
data such as gene expression profiles. We investigated the correlation between chemically induced
gene expression profiles (reflecting cellular responses to compound treatment) and target
perturbation gene expression profiles (reflecting cellular responses to gene knock-down or gene
over-expression of target proteins) in terms of interactions between compounds and target proteins.
Then, we proposed novel machine learning methods to generate the chemical structures of newmolecules with desired gene expression profiles in the framework of variational auto-encoder (VAE)[2]using a new chemical representation, which does not depend on the SMILES strings. The noveltyof the proposed method comes from the bridge between the chemical space and the biological space,
the high chemical validity of the newly generated structures, the generation of drug candidate
compounds from omics data with high accuracy, and the applicability to target proteins without
information on ligands. We confirmed that the molecules that were newly generated using ourstructure generator were valid from the viewpoint of medicinal chemistry, comparing the structures
between the newly generated molecules and known ligands. It was observed that the newly
generated molecules were more similar to known ligands than those generated by the previous
method with similar goals [3] . Our proposed method for omics-based molecular generation is
expected to be useful for de novo drug design in practice.
[1] Gómez-Bombarelli, R.; et al., Automatic Chemical Design Using a Data-driven Continuous
Representation of Molecules. ACS Cent. Sci., 2018, 4, 268-276.
[2] Hinton, G. E.; Salakhutdinov, R.R. Reducing the Dimensionality of Data with Neural Networks.
Science, 2006, 313, 504 ‒ 507.
[3] Méndez-Lucio, O.; et al., De Novo Generation of Hit-like Molecules from Gene Expression
Signatures Using Artificial Intelligence. Nat. Commun., 2020, 11, 10.

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O2-4

Visualized and quantitative structural analysis of
peptidomimetics for PPI drug discovery
Hajime Takashima 1

Atsushi Yoshimori 2

Eiji Honda 1

takashima@prismbiolab.com

yoshimori@itmol.com

honda@prismbiolab.com

Tomonori Taguri 1

Jun Ozawa 1

Satoshi Shuto 3

taguri@prismbiolab.com

ozawa@prismbiolab.com

shu@pharm.hokudai.ac.jp

Shunji Suetaka 4
shunji-suetaka@g.ecc.u-tokyo.ac.jp

Nao Sato 4

sato-nao140@g.ecc.u-tokyo.ac.jp

Yuuki Hayashi 4

Munehito Arai 4,5

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

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

Dai Takehara 1
takehara@prismbiolab.com

1

PRISM BioLab Co., Ltd., 2-26-1 Muraoka-Higashi, Fujisawa, Kanagawa, 251-0012, Japan
Institute for Theoretical Medicine, Inc., 2-26-1 Muraoka-Higashi, Fujisawa, Kanagawa, 251-0012, Japan
3
Faculty of Pharmaceutical Science, Hokkaido University 160-8582, Kita 8, Nishi 5, Kita-ku, Sapporo,
Hokkaido, 060-0808, Japan
4
Department of Life Sciences, Graduate School of Arts and Scie nces, The University of Tokyo, 3-8-1
Meguro, Komaba, Tokyo 153-8902, Japan
5
Department of Physics, Graduate School of Science, The University of Tokyo, 3-8-1 Meguro, Komaba,
Tokyo 153-8902, Japan
2

Keywords: Peptidomimetics, Conformational analysis, PepMetics TM Library, PPIs
Protein–protein interactions (PPIs) are significant drug targets and many peptidomimetic
molecules have been developed for modulating PPIs [1]. Nevertheless, no analytical method has
been established to evaluate how precisely these molecules can mimic target peptide structures.
We developed new methods which enable visual, comprehensive, and quantitative analysis ofthree-dimensional structures of peptidomimetics: peptide conformation distribution (PCD) plot andpeptidomimetic analysis (PMA) map [2]. These methods are based on the conformational analysis
of multiple side-chain C α -C β bonds of a peptide fragment and their corresponding bonds(pseudo-C α -C β bonds) in a peptidomimetic molecule, instead of the Ramachandran plot [3] using φand ψ angles of a single amino acid. PCD-plot is an alignment-free method and visually clarifies
conformation distribution of peptidomimetic molecules in the chemical space and similarity with
its target peptide secondary structures. PMA-map is an alignment-based method and canquantitatively evaluate similarity between various types of peptidomimetic molecules and theirtarget peptide structures.
Results obtained from analysis using these two methods indicated that our PepMetics TM scaffold,multi-facial α-helix mimetics, is an excellent peptidomimetics that can mimic spatial positioningof the side-chains of α-helix precisely. We have developed a peptidomimetic compound library(PepMetics TM Library) using our PepMetics TM scaffolds and have found PPI hit compounds for theinteractions of KIX and c-Myb/MLL proteins known as targets for cancer and leukemia.
In summary, our methods and strategies are useful for development of new peptidomimetic
molecules and rational PPI drug discovery.
[1] Milroy, L. G.; et al., Modulators of protein-protein interactions, Chem. Rev., 2014, 114, 4695-4748.
[2] Takashima, H.; Yoshimori, A.; et al., Visualized and quantitative conformational analysis of
peptidomimetics, submitted.
[3] Ramachandran, G. N.; et al., Stereochemistry of polypeptide chain configurations, J. Mol. Biol.,
1963, 7, 95–99.

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Redesigning the low-molecular weight SHP2 inhibitorswith Computational Chemistry.
Mutsuyo Wada 1
mutsu@fujitsu.com

Shoji Hisada 2

Kentaro Takai 2

Atsushi Tomonaga

takai.kentaro@fujitsu.com

Akihiko Ueda 1

Seiji Fukui 2

Nozomu Kamiya 1

Azuma Matsuura

Hiroyuki Sato 2

Yoshiaki Tanida 2

Tatsuaki Nishiyama 3

Yoshiharu Miyake 3

Tatsuhiko Kodama 4

1
2
3

4

Fujitsu Japan Ltd., 1-5-2, Higashishimbashi, Minato-ku, Tokyo 105-7123, Japan
Fujitsu Ltd., 1-5, Omiya-Cho, Saiwai-ku, Kawasaki, Kanagawa 212-0014, Japan
Kowa Co., Ltd., Tokyo New Drug Research Laboratories, 2-17-43, Noguchi-cho,Higashimurayama, Tokyo 22, 189-0022, Japan
Cancer and Metabolism Project, Research Center for Advanced Science andTechnology, The University of Tokyo, Building 4, Komaba Research Campus,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

Keywords: de novo drug design, SH2-domain containing protein tyrosine phosphatase 2, in silico
FBDD, in silico screening, molecular dynamics (MD) simulation, binding free energy calculation
In recent years, thanks to the epoch-making improvement of structural analysis technology
supported AI, various protein-ligand complexes have been revealed and have been available forstructure-based drug design. In this study, we obtained novel active compounds based on theco-crystalized X-ray structures of anti-cancer target protein SHP2 which is related to the immune
checkpoint pathway associated with PD-1 [1].
Method is as follows:
1) Analyze the SHP2 protein-ligand
interactions of PDB structures [2-4]and design novel compounds with thesame interactions using OPMF TM [5],our in silico FBDD technology.
2) Check the synthetic accessibility andthe charged state of the experimentand calculation conditions.
3) Evaluate and select the compounds.
 The binding stabilities calculated
by MD simulation
 The differences of the conformation energies between the comp lex form and free form
calculated with molecular mechanics (MM) and quantum mechanics (QM) The binding free energy calculated by MAPLECAFEE TM V2
4) Synthesize the compounds and their analogues and evaluate SHP2 inhibitory activities.
We synthesized 10 of 18 selected compounds and 3 of 10 compounds showed IC 50 <approximately 3 μM. The molecular weight, activity value and ligand efficiency (LE) of the threecompounds were improved than the values of the PDB structure which we used for the drug design.
These results indicate that combination of the crystal structure of existing active compounds andour design technology could accelerate the development of novel low-molecular drugs.
[1] Marasco M, et al. Sci Adv. 2020;6(5):eaay4458[2] Zhang X, et al., J Med Chem. 2010;53(6):2482-93.
[3] Liu S, et al., Chem Biol.2011;18(1):101-110.
[4] Zeng LF,et al.,J Med Chem.2014;57(15):6594 -6609.
[5] T. Yamashita, et al., Chem Pharm Bull (Tokyo). 2014;62(7):661-7

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O2-6

Interpretable Deep Learning Using Multimodal Graph
Convolutional Network for Predicting Compound-Protein
Interactions
Sota Inoue 1

Yoshihisa Tanaka 2

inoue.sota.65r@st.kyoto-u.ac.jp

tanaka.yoshihisa.42n@st.kyoto-u.ac.jp

Shoichi Ishida 3
ishida.sho.nm@yokohama-cu.ac.jp

Ryosuke Kojima 1

Hiroaki Iwata 1

Yasushi Okuno 1

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

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

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

1

2

3

Graduate School of Medicine, Kyoto Univ., 53 Kawahara-cho, Syogoin, Sakyo-ku, Kyoto,606-8507, Japan
Graduate School of Pharmaceutical Sciences, Kyoto Univ., 46-29 Yoshida-Shimo-Adachi-cho,
Sakyo-ku, Kyoto, 606-8501, Japan
Graduate School of Medical Life Science, Yokohama City Univ., 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama, 230-0045, Japan

Keywords: in silico Screening, Compound-Protein Interaction, Graph Convolutional Network
Understanding compound-protein interactions (CPI) is important for identifying hit compounds inthe early stages of drug discovery. Recent advances in deep learning technology have enabled rapid
and accurate calculations[1], but it remains difficult to interpret the physicochemical andbiological aspects of the prediction results. For example, to visualize the prediction results,
mapping the weight of Attention mechanism to the 3D structure of the protein or using Grad-CAM
has been attempted[2,3]. However, this is still not enough to evaluate the results objectively, andno discussion has been obtained to validate the model. In our study, we propose a multimodal andinterpretable CPI prediction model by adapting Graph Convolutional Networks (GCN) for small
molecules and Convolutional Neural Networks (CNN) for proteins, and by using Integrated
Gradients (IG) to visualize the contribution of each atom in small molecules and each residue inproteins to the prediction results. As a result of visualization with our model, it is suggested thatthe prediction results are obtained by capturing the features of chemical structures of small
molecules and protein sequences. This indicates that when screening hit compounds using Deep
Learning-based CPI prediction models, biological reliability of the prediction results needs to becarefully considered.
[1] Hamanaka, M.; Taneishi, K.; Iwata, H.; Ye, J.; Pei, J.; Hou, J.; Okuno, Y.; CGBVS-DNN:
Prediction of Compound-protein Interactions Based on Deep Learning, Mol. Inf., 2017, 36,
1600045.
[2] Tsubaki, M.; Tomii, K.; & Sese, J.; Compound-protein interaction prediction with end-to-end
learning of neural networks for graphs and sequences, Bioinformatics, 2019, 35(2), 309–318.
[3] Verma, N.; Qu, X.; Trozzi, F.; Elsaied, M.; Karki, N.; Tao, Y.; Zoltowski, B.; Larson, E.C.;
Kraka, E. SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction, Int. J.
Mol. Sci., 2021, 22, 1392.

13

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Drug Target Gene-based Analyses ofDrug Repositionability in Rare and Intractable Diseases

1

Ryuichi Sakate 1

Tomonori Kimura 1

rsakate@nibiohn.go.jp

t-kimura@nibiohn.go.jp

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

Keywords: Drug repositioning, Clinical trials, Inter-disease analyses, Drug target genes
Drug development for rare and intractable diseases has been challenging for decades due to thelow prevalence and insufficient information on these diseases. Drug repositioning is increasingly
being used as a promising option in drug development. We aimed to analyze the trend of drug
repositioning and inter-disease drug repositionability among rare and intractable diseases. We
created a list of rare and intractable diseases based on the designated diseases in Japan. Drug
information extracted from clinical trial data were integrated with information of drug target genes,
which represent the mechanism of drug action. We obtained 753 drugs and 551 drug target genes
from 8,307 clinical trials for 189 diseases or disease groups. Trend analysis of drug sharing
between a disease pair revealed that 1,676 drug repositioning events occurred in 4,401 disease
pairs. A score, Rgene, was invented to investigate the proportion of drug target genes shared
between a disease pair. Annual changes of Rgene corresponded to the trend of drug repositioning
and predicted drug repositioning events occurring within a year or two. Drug target gene-based
analyses well visualized the drug repositioning landscape. This approach facilitates drug
development for rare and intractable diseases.
[1] Sakate, R.; Kimura, T. Drug target gene-based analyses of drug repositionability in rare andintractable diseases, Scientific Reports, 2021, 11, 12338.

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