Oral Presentation 7

日時： 2020 年 10 月 30 日 13:00-14:30
チャンネル： １

口頭発表７『新型コロナ等ウィルス対策/計算化学（分子認識）』
Drug Discovery/Clinical application (Focused on COVID-19)
座長： 片倉 晋一 （慶應義塾大学）
Shinichi Katakura (Keio University)
福澤 薫 （星薬科大学）
Kaori Fukuzawa (Hoshi University)
広川 貴次 （産業技術総合研究所）
Takatsugu Hirokawa (AIST)
O1-08"Interaction analyses between SARS-CoV-2 main protease and inhibitor N3 by using
fragment molecular orbital method and molecular dynamics simulation"
Ryo Hatada(Department of Chemistry & Research Center for Smart Molecules, Faculty ofScience, Rikkyo University)
O1-09"Interaction analyses on SARS-CoV-2 spike proteins by using fragment molecularorbital method"
Kazuki Akisawa(Department of Chemistry & Research Center for Smart Molecules, Faculty ofScience,Rikkyo University)
O1-10"Predicting inhibitors for SARS-CoV-2 RNA- dependent RNA polymerase using
machine learning"
Nazim Medzhidov(Elix, Inc.)
O1-11"Theoretical insights into the molecular mechanism of NA-I117V-MediatedOseltamivir Resistance in H5N1 Avian Influenza Virus"
Mohini Yadav(Department of Engineering, Chiba Institute of Technology)

O1-08Interaction analyses between SARS-CoV-2 main proteaseand inhibitor N3 by using fragment molecular orbital
method and molecular dynamics simulation
Ryo Hatada 1 Koji Okuwaki 1 Kazuki Akisawa 1 Yuji Mochizuki 1,2 Yuma Handa 3
Kaori Fukuzawa 2,3 Yuto Komeiji 4 Yoshio Okiyama 5 Shigenori Tanaka 6hatada@rikkyo.ac.jp 17cc044m@rikkyo.ac.jp okuwaki@rikkyo.ac.jp
fullmoon@rikkyo.ac.jp d2002@hoshi.ac.jp k-fukuzawa@hoshi.ac.jp
y-komeiji@aist.go.jp okiyama@nihs.go.jp tanaka2@kobe-u.ac.jp123456

Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,
Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
Institute of Industrial Science, University of Tokyo, 4 -6-1 Komaba, Meguro-ku, Tokyo
153-8505, Japan
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,Shinagawa-Ku, Tokyo 142-8501, Japan
Health and Medical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305 -8566,
Japan
Division of Medicinal Safety Science, National Institute of Health Sciences, 3 -25-26
Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 201-9501, Japan
Graduate School of System Informatics, Department of Computational Science, Kobe
University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan

Keywords: SARS-CoV2, Fragment molecular orbital (FMO) method, Molecular dynamics
Many research groups have engaged a variety of investigations against the worldwide issue ofSARS-CoV-2. Here, we have performed a combination approach of fragment molecular orbital(FMO) calculation and molecular dynamics (MD) simulation to analyze the interactions between
the SARS-CoV-2 Main protease (Mpro) and the N3 inhibitor in a statistical manner.
Prior to the MD-assisted investigation, a detailed analysis with inter-fragment interaction energy(IFIE) was performed for the crystal structure (PDB ID: 6LU7) [1] of complex between Mpro andN3 inhibitor, based on the FMO-MP2(PR)/6-31G* calculation by using ABINIT-MP [2,3]. Using
AMBER [4], MD simulations for 100 ns were then performed from the 6LU7 initial structure, anda total of 100 structures were extracted from the trajectory. These sampled structures weresubjected to a series of FMO calculations, and statistical evaluation of IFIEs was made.
Through these studies, several amino acid residues such as His163 and Glu166 was identified to
be important in interacting with N3 with hydrogen bond. Interestingly, the structural fluctuation
via MD sampling could alter the relative importance among the residues. We will report on thedetails of these results on the presentation day.
[Acknowledgment] The present work was supported by MEXT as a social and scientific priority
issue #6 to be tackled by using post-K computer (FS2020), AMED-BINDS (JP20am0101113), andRikkyo SFR. MD simulations were performed using TSUBAME 3.0 (BINDS quota) at the Tokyo
Institute of Technology. For FMO calculations, computational resources of FX-100 at Nagoya
University, RIKEN R-CCS Fugaku (priority trial use against coronavirus) and Oakforest-PACS(hp200146 quota) at JCAHPC were used.
[ 1]Jin, Z., et al., Structure of Mpro from SARS-CoV-2 and Discovery of Its Inhibitors. Nature,582 (7811): 289-293, 2020.
[2] Tanaka, S., et al., Electron-Correlated Fragment-Molecular-Orbital Calculations for
Biomolecular and Nano Systems. Phys. Chem. Chem. Phys.,16 (22): 10310–10344, 2014.
[3] Hatada, R., et al., Fragment Molecular Orbital Based Interaction Analyses on COVID -19
Main Protease − Inhibitor N3 Complex (PDB ID: 6LU7). J. Chem. Inf. Model., 60 (7):3593–3602, 2020.
[4] <https://ambermd.org/>.

O1-09Interaction analyses on SARS-CoV-2 spike proteins by using
fragment molecular orbital method
Kazuki Akisawa1 Ryo Hatada1 Koji Okuwaki1 Yuji Mochizuki1,2 Kaori Fukuzawa2,3
Yuto Komeiji4 Shigenori Tanaka5
17cc044m@rikkyo.ac.jp hatada@rikkyo.ac.jp okuwaki@rikkyo.ac.jp
fullmoon@rikkyo.ac.jp k-fukuzawa@hoshi.ac.jp
y-komeiji@aist.go.jp tanaka2@kobe-u.ac.jp1Department of Chemistry & Research Center for Smart Molecules, Faculty of Science,Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
2
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan3
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara,
Shinagawa-Ku, Tokyo 142-8501, Japan
4
Health and Medical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan
5
Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1
Rokkodai, Nada-ku, Kobe 657-8501, Japan

Keywords: SARS-CoV2, Spike protein, Fragment molecular orbital (FMO) method, MP4(SDQ)
It is well known that the spike protein of SARS-CoV-2 commits the infection to human cells. Thus,
considerable efforts in worldwide have been conducted to understand the natures of this protein build with 3chains (a total of 3.3 thousand amino acid residues). Here, we have performed highly correlated FMO(fragment molecular orbital) calculations with ABINIT-MP [1]. The calculated systems were the spike proteinof both closed (PDB ID: 6VXX) and open (PDB ID: 6VYB) structures and also a couple of RBD (receptorbinding domain) complexes with human ACE2 (angiotensin-converting enzyme 2) (PDB ID: 6M0J) and B38
antibody (PDB ID: 7BZ5).
The structures of 6VXX and 6VYB were prepared with a homology modeling and relaxed by an MD(molecular dynamics) annealing using AMBER [2]. The structure preparations for 6M0J and 7BZ5 were rather
simply done with MOE [5]. The level of FMO calculations were MP2, MP3 [2], and MP4(SDQ) [3], and ascaling for correlation energy such as MP2.5 was adopted to improve accuracy. The basis sets were 6-31G*
and cc-pVDZ. A series of IFIE (inter-fragment interaction energy) analyses were then made for these systems.
It was found that the inter-chain stabilization energies (A-B, B-C and C-A) of the open structure (6VYB)
were sizably different from those of the closed structure (6VXX). In particular, the stabilization energy of RBD
in chain B of 6VYB is remarkably decreased relative to that of 6VXX. The importance of charged residues inchain interactions was illuminated as well.
The calculated IFIEs of 6M0J and 7BZ5 suggested that the stabilization energy loss of RBD in chain B ispartly compensated by the binding with ACE2 and B38 antibody. These results could be consistent with thefact that the open structure is responsible for the infection to human cell and the target of antibody. The detailsof these results will be reported on the day of the presentation.
[Acknowledgment] The present work was supported by Rikkyo SFR and AMED-BINDS (JP20am0101113).
The world's largest scale higher-order correlated FMO calculations were carried out using the RIKEN R-CCS"Fugaku" (priority trial use against coronavirus) and the ITO System A (hp200147 quota) at the Kyushu
University.
[1] Tanaka, S., et al., Electron-Correlated Fragment-Molecular-Orbital Calculations for Biomolecular andNano Systems. Phys. Chem. Chem. Phys.,16 (22): 10310–10344, 2014.
[2] <https://ambermd.org>.
[3] Mochizuki, Y., et al., Large-Scale FMO-MP3 Calculations on the Surface Proteins of Influenza Virus,
Hemagglutinin (HA) and Neuraminidase (NA). Chem. Phys. Lett. 2010, 493 (4), 346–352.
[4] Mochizuki, Y., et al., Higher-Order Correlated Calculations Based on Fragment Molecular Orbital Scheme.
Theor. Chem. Acc. 2011, 130 (2), 515–530.
[5] < https://www.chemcomp.com/>.

O1-10Predicting inhibitors for SARS-CoV-2 RNA-dependent
RNA polymerase using machine learning
Nazim Medzhidov 1

Romeo Cozac 1

nazim.medzhidov@elix-inc.com

romeo.cozac@elix-inc.com

Shinya Yuki 1

shinya.yuki@elix-inc.com

1

Elix, Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku,
Tokyo 102-0081, Japan

Keywords: SARS-CoV-2, RdRp, Machine Learning

Global coronavirus disease pandemic (COVID-19) caused by newly identified SARSCoV-2 coronavirus continues to claim the lives of thousands of people worldwide. The
unavailability of specific medications to treat COVID-19 has led to drug repositioning
efforts using various approaches, including computational analyses. Such analyses mostly
rely on molecular docking and require the 3D structure of the target protein to be available.
In this study, we utilized a set of machine learning algorithms and trained them on adataset of RNA-dependent RNA polymerase (RdRp) inhibitors to run inference analyses onantiviral and anti-inflammatory drugs solely based on the ligand information. We also
performed virtual screening analysis of the drug candidates predicted by machine learningmodels and docked them against the active site of SARS- CoV-2 RdRp, a key componentof the virus replication machinery. Based on the ligand information of RdRp inhibitors, themachine learning models were able to identify candidates such as remdesivir and baloxavir
marboxil, molecules with documented activity against RdRp of the novel coronavirus.
Among the other identified drug candidates were beclabuvir, a non-nucleoside inhibitor of
the hepatitis C virus (HCV) RdRp enzyme, and HCV protease inhibitors paritaprevir andfaldaprevir. Further analysis of these candidates using molecular docking against theSARS-CoV-2 RdRp revealed low binding energies against the enzyme active site. Ourapproach also identified anti-inflammatory drugs lupeol, lifitegrast, antrafenine, betulinicacid, and ursolic acid to have potential activity against SARS-CoV-2 RdRp. We proposethat the results of this study are considered for further validation as potential therapeutic
options against COVID-19.

O1-11

Theoretical insights into the molecular mechanism ofNA-I117V-Mediated Oseltamivir Resistance in H5N1
Avian Influenza Virus
Mohini Yadav 1
s1879602EC@s.chibakoudai.jp

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

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

12

Department of Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino,Chiba 275-0016, Japan
Research Center for Zoonosis Control, Hokkaido University, 5 Chome Kita 8 Jonishi, Kita
Ward, Sapporo, Hokkaido 060-0808, Japan

Keywords: Avian Influenza, Neuraminidase, H5N1, NA-I117V, Drug Resistance, Oseltamivir
Resistance
Influenza is a contagious respiratory illness that infects the nose, throat, and lungs, causing mildto severe illness. Glycoproteins, hemagglutinin (HA) and neuraminidase (NA), of the influenza
virus play essential roles in its replication process. HA binds to sialic acid (SA) at the end of asugar chain present on the surface of a host cell, and then the virus is taken into the cell. NA on thevirus surface plays a role of scissors to cleave the bond between SA and the sugar chain, whichallows the daughter virus to liberate to begin infecting the surrounding cells.
Anti-influenza drugs that inhibit the interaction between NA and SA have been developed.
Currently, four types of NA inhibitors (oseltamivir, zanamivir, peramivir, and laninamivir) havebeen used for the treatment of influenza in Japan. But resistance against available anti-influenza
drugs is emerging rapidly. Hence, it’s crucial to study the detailed mechanism of drug resistance todevelop anti-influenza drugs which are less prone to resistance.
Most of the receptor mutations that lead to drug resistance occurs at its active site residues, since
they are the key residues interacting with the drug. But residue 117 is not a part of active site ofneuraminidase and still an Ile-to-Val substitution at position 117 (NA-I117V) causes a slight
reduction in susceptibility to oseltamivir (OT) in vitro and dramatically in vivo. 1 However, themolecular mechanism of OT resistance caused by NA-I117V mutation is still not clear.
In this study, to clarify the binding affinities of SA and OT to the NA-I117V, we computed thecorresponding binding free-energies using the molecular mechanics Poisson-Boltzmann surface
area (MM-PBSA) method. We also computed the pairwise per-residue free energy decomposition
calculation to clarify the molecular mechanism of OT resistance in NA-I117V mutant.
[1] Takano, R., Kiso, M., Igarashi, M., Le Q.M., Sekijima M., Ito, K., Takada, A., Kawaoka, Y.,
Molecular Mechanisms Underlying Oseltamivir Resistance Mediated by an I117V Substitution
in the Neuraminidase of Subtype H5N1 Avian Influenza A viruses, The Journal of Infectious
Diseases, 207:89-97, 2013.