O6-1
Understanding RNA sequence specificity of inhibitorsof translation initiation factor by dynamical FMO analysis
Yuma Handa 1,2 Koji Okuwaki 1 Takayuki Furuishi 1 Etsuo Yonemochi 1 Takuhiro Ito 3
Hironori Saito 4,5 Shintaro Iwasaki 4,5 Koichiro Kato 6 Chiduru Watanabe 3 Teruki Honma 3
Kaori Fukuzawa 1, 2d2002@hoshi.ac.jp k-okuwaki@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
kato.koichiro.957@m.kyushu-u.ac.jp chiduru.watanabe@riken.jp honma.teruki@riken.jp
fukuzawa-k@phs.osaka-u.ac.jp123456Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, JapanRIKEN Center for Biosystems Dynamics Research (BDR), 1-7-22 Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, JapanRIKEN Cluster for Pioneering Research (CPR), 2-1, Hirosawa, Wako, Saitama 351-0198,
Japan
The University of Tokyo, 5-1-5 Kashiwa, Chiba 277-8561, Japan
Kyushu University, 744 Motoka, Nishi-ku, Fukuoka, 819-0395, Japan

Keywords: eIF4A, RNA, Fragment molecular orbital method, Molecular Dynamics, Rocaglamide
A, Pateamine A, RHT
Rocaglamide A (RocA) is a natural compound found in Aglaia odorata, which has potent anticancer
activity and acts as a translation inhibitor targeting to translation initiation factor eIF4A. This
compound is known to function in a unique mechanism; clamping eIF4A onto purine sequencespecificity [1] for steric hindrance for scanning ribosomes. Our group recently identified that
another eIF4A-targeting small molecule Pateamine A (PatA), which was isolated from a sponge,
works as a translation inhibitor in a similar manner, but with a different RNA specificit y;
GNG motifs. However, the detailed molecular mechanism for such base selectivity provided by thecompounds remains unclear. To tackle this issue, here we employed dynamical fragment molecularorbital (FMO) calculations combining classical molecular dynamics (MD) and FMO methods.
For RocA, four structures were created, each with one of the G6, A7, G8, and A9 bases aroundligand mutated to U in addition to the wild-type 6 GAGA 9 RNA. In addition, we analyzed structurewith the RocA analog RHT. For PatA, we created 6 AGAG 9 , polyA, and polyG sequences by mutating
the 6 GAGA 9 sequence around ligand in addition to the wild type. For all model structures, MD
simulations were performed for 50 ns each. From the MD results, we extracted 10 structures every
3 ns, and performed FMO calculations at the MP2/6-31G* level. The MD and FMO calculations
were performed on the supercomputer Fugaku (hp220143) and TSUBAME 3.0 (AMED -BINDS,
JP21am0101113).
In the RocA complex, π/π interaction with U7 was lost in the A7U mutant and the hydrogen bondand π/π interaction with U8 were lost in the G8U mutant. These results suggest that the interaction
with these two bases is important for purine sequence-specific recognition of RocA. RHT showedno significant change in major interactions compared to RocA . PatA showed loss of hydrogenbonding at G8 and weakened π/π interactions at A7 and G8 compared to RocA. The mutant form ofPatA will be investigated.

[1] Iwasaki S. et al., The Translation Inhibitor Rocaglamide Targets a Bimolecular Cavity between
eIF4A and Polypurine RNA, Mol Cell. 2019;73(4):738-748.e930

O6-2
Development of protein-ligand binding affinity evaluation
method by combining classical MD and FMO calculations
Hiromu Matsumoto 1
matsumoto.hiromu.238@s.kyushu-u.ac.jp

Tsuyohiko Fujigaya1

fujigaya.tsuyohiko.948@m.kyushu-u.ac.jp

Koichiro Kato 1
kato.koichiro.957@m.kyushu-u.ac.jp
1

Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan

Keywords: Protein-ligand interaction, Fragment molecular orbital method (FMO), Molecular
dynamic simulation
Molecular simulations to evaluate protein-ligand interactions are useful for identifying drugcandidates from a huge number of compounds. In SBDD-based computational drug discovery,
protein-ligand interactions are usually evaluated using classical force field-based methods. On theother hand, quantum mechanical calculations are desirable in terms of accuracy but are difficult due
to the enormous computational cost. The fragment molecular orbital (FMO) method has been
proposed as a unique method that can calculate the entire protein system quantum-chemically.
Interaction evaluation by the FMO method is usually performed by single-point calculations
in vacuum using X-ray crystal structures. However, recent studies have suggested that
including thermal fluctuations gives a more reliable interaction, which is not evaluated bysingle-point calculations in vacuum [1]. Therefore, we focused on the possibility of further
improving the accuracy by taking into account not only thermal fluctuations but also desolvation
effects and ligand deformation during binding.
In this research, evaluations were performed on three proteins with the different total numbersof residues, ER, CDK2, and SARS-CoV-2 Mpro ligand complexes.
For each complex, two 10 ns molecular dynamics (MD) simulations were performed usingGROMACS. From the results of each MD simulation, 20 structures were extracted every 1 ns. Theinteraction energies (Δ𝐸) were obtained by FMO calculations at the FMO2-MP2/6-31G* level
using ABINIT-MP. Then MM-PB/SA calculations were performed to evaluate each desolvationenergy (Δ𝐺) classically. Ligand deformation energies (𝛥𝐸 ) were calculated as the energydifference between conformations of the ligand in the complex structure and in the water. Finally,
we added up their effects and averaged them over 20 samples to obtain a binding affinity value( Δ𝐺= Δ𝐸+ Δ𝐺+ Δ𝐸 ). The results of FMO calculations were obtained using thesupercomputer “Fugaku” (hp220143).
For ER complexes, 5 compounds were used. The enhancement of correlation between
calculation values and experimental values (pIC 50 ) was confirmed by considering thermal
fluctuation, ligand deformationenergy, and desolvation energy.
The 𝑅 between simple singlepoint FMO calculation energies
and pIC 50 was about 0.11 ( Fig. 1(a)), but it was improved to about0.44 ( Fig. 1 (b)) by taking allthese effects into account. The
results for other complexes will
be presented in detail.
[1] Hatada, R.; et al., Appl. Phys.
Express, 2021, 14, 027003

Fig. 1 pIC50 vs (a) single-point 𝜟𝑬𝐅𝐌𝐎 and (b) 𝜟𝑮𝐛𝐢𝐧𝐝
based on sampling of ER-complex

31

O6-3

Structure-Activity Relationship Study of PROTACs against
Hematopoietic Prostaglandin D2 Synthase
Hinata Osawa 1,2p9u36syp@s.okayama-u.ac.jp

Norihito Shibata 2
n-shibata@nihs.go.jp

1234

Takashi Kurohara 2
tks-kurohara@nihs.go.jp

Mikihiko Naito 4

Yuki Murakami 3
w225431@yokohama-u.ac.jp

Yosuke Demizu 1,2,3

miki-naito@g.ecc.u-tokyo.ac.jp

demizu@nihs.go.jp

Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Division of
Pharmaceutical Science of Okayama University, 1-1-1 Tsushimanaka, Kita 700-8530, Japan.
National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki, Kanagawa 210-9501,Japan.
Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Yokohama,Kanagawa, 230-0045, Japan.
Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033,
Japan.

Keywords: PROTAC, H-PGDS, PROTAC Modeling, MD simulation
Degradation of hematopoietic prostaglandin D 2 synthase (H-PGDS) by proteolysis-targeting
chimeras (PROTACs) is expected to be important in the treatment of allergic diseases andDuchenne’s muscular dystrophy. We recently reported that PROTAC 1, which is composed of HPGDS inhibitor (TFC-007) and cereblon (CRBN) E3 ligase ligand (pomalidomide), showed potentH-PGDS degradation activity [1] .
TFC-007
C4 = PROTAC 1
In this study, we investigated theC5 = PROTAC 2
structure–activity relationships ofCRBN
PROTAC 1, focusing on theTAS-205
C4 = PROTAC 3
C5 = PROTAC 4
conjugation site between the HPGDS ligand and the E3 ligand,and the effect of changing the HDegradation
PGDS ligand from TFC-007 toProteasome
TAS-205. Three new PROTACs
H-PGDS
2-4 were evaluated for H-PGDS
affinity,
H-PGDS
reducing
Ubiquitination
PROTAC Modelingactivity,andinhibitionofMD simulations
prostaglandin D 2 production. Allcompounds showed high H-PGDS
reducing activities, but PROTAC
3 was slightly less active than theother compounds.
To investigate these differences,
the PROTAC Modeling Tools and molecular dynamics (MD) simulations were used to evaluate thestability of each PROTAC ternary complex. The results showed that the ternary complex including
PROTAC 3 had the highest RMSD value, suggesting that the reduced activity of PROTAC 3 is dueto the lower stability of the H-PGDS–PROTAC–CRBN ternary complex.
[1] H. Yokoo. et al., J. Med. Chem. 2021, 64, 15868–15882.

32

O6-4

Investigation of hydrogen bond network in the active
center of catechol O-methyltransferaseby X-ray crystallography and FMO method.
Katsuki Takebe 1 Tomoko Shirai 2 Yuma Handa 2,3 Kaori Fukuzawa 3 Mamoru Suzuki 4
Takao Kuwada-Kusunose 5 Tokomo Takamiya 6 Narikazu Uzawa 1 Hiroshi Iijima 6

takebek@dent.osaka-u.ac.jp fukuzawa-k@phs.osaka-u.ac.jp mamoru.suzuki@protein.osaka-u.ac.jp
kuwada.takao@nihon-u.ac.jp
s181114@hoshi.ac.jpd2002@hoshi.ac.jp
takamiya.tomoko@nihonu.ac.jp uzawa.narikazu.dent@osaka-u.ac.jp iijima.hiroshi@nihon-u.ac.jp123456Osaka University Graduate School of Dentistry., 1-8 Yamadaoka, Suita,Osaka 565-0871,
Japan
Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan
Graduate School of Pharmaceutical Sciences., 1-6 Yamadaoka, Suita,Osaka 565-0871, Japan
Institute for Protein Research, Osaka University Osaka Univ., 3-2 Yamadaoka, Suita,Osaka565-0871, Japan
Nihon University School of Dentistry at Matsudo., 2-870-1 Matsudo. Chiba 271-8587, Japan
School of Pharmacy, Nihon Univ., 7-7-1 Narashinodai, Funabashi, Chiba 274-8555, Japan

Keywords: X-ray crystallography, FMO method, hydrogen network
Catechol O-methyltransferase (COMT) is a target enzyme for the drug development for
Parkinson’s disease. COMT uses S‐adenosylmethionine (SAM) as a substrate for L-Dopa
methylation to produce S-Adenosyl-L-homocysteine (SAH).
We determined the structures of COMT/SAM/Mg/opicpaone and COMT/SAH/Mg/opicapone
complexes by X-ray crystallography. In both structures, a complex hydrogen bond network between
COMT and opicapone was observed. Determineation of precise position of hydrogen atoms involvedin the network is important to design new inhibitors. However, it is difficult to discuss the hydrogenposition based only on X-ray crystallography. We evaluated the energy of the hydrogen network forpossible models by computing with the FMO method. Finally multiple network models weresucessfully obtained.

(Figure) Structure of active center determined by X-ray diffraction COMT/SAM/Mg/opicapone

33

O6-5

PoSSuMds Update : Addition Of Clinical Candidates
And COVID-19 Clinical Trials Information
Tomoki Yonezawa 1
yonezawa-tm@pha.keio.ac.jp

123

Yuko Tsuchiya 2

Masanori Osawa 1

yuko.tsuchiya@aist.go.jp

osawa-ms@pha.keio.ac.jp

Kentaro Tomii 2

Kazuyoshi Ikeda 1,3

k-tomii@aist.go.jp

kazuyoshi.ikeda@riken.jp

Keio Univ., 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, Japan
AIRC, AIST, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0063, JapanRIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: PDB, Pocket similarity, Clinical candidates, COVID-19
PoSSuM is a database of comprehensive pocket similarity pairs in proteins registered in the PDB[1]. PoSSuMds (PoSSuM Drug Search) is a subset for investigating the diversity of ligands andreceptors among a set of pockets that can bind to a drug compound [2]. We provide here the newPoSSuMds, updated with the latest approved drugs and candidates , resulting in an increase in thenumber of drug compounds that can be queried. A total of 4,142 compounds, selected from theChEMBL [3] (version 29), were matched with PDB ligand structures, provided at Ligand Expo, anddetermined presence in PoSSuM with HET code. When matching ligand structures, the structure
standardization protocol was refined from the previous version for precise structure matching. As aresult, 599 unique compounds had at least one protein -ligand complex structure in the PDB. Of
these, 580 compounds had pocket similarity data in the PoSSuM, indicating an increase of 386
compounds compared to the previous version (194 compounds). From this increase, the number ofbinding pockets (query pockets) has increased 8-fold, previous 2,595 pockets to current 20,866, andcomparable pockets (target pockets) has increased 5 -fold, previous 26,509 pockets to current
151,482, and the number of pockets pair similarities has increased 7-fold, previous 530,898 pairs
to current 3,858,525. In addition, a survey using clinical candidate data from the ClinicalTrials.gov
reveals that 68 out of 580 compounds in PoSSuMds were tested in clinical trials as potential
treatments for COVID-19. With this update, a similar pocket search on a larger scale has become
possible, increasing the effectiveness of investigations such as drug reuse, including for infectiousdiseases.
[1] https://possum.cbrc.pj.aist.go.jp/PoSSuM/[2] Ito, J. I.; Ikeda, K.; Yamada, K.; Mizuguchi, K.; Tomii, K. PoSSuM v.2.0: Data Update and a
New Function for Investigating Ligand Analogs and Target Proteins of Small -Molecule Drugs.
Nucleic Acids Research 2015, 43 (D1), D392–D398.
[3] Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A. P.; Chambers, J.; Mendez, D.; Mutowo, P.;
Atkinson, F.; Bellis, L. J.; Cibrián-Uhalte, E.; Davies, M.; Dedman, N.; Karlsson, A.; Magariños,M. P.; Overington, J. P.; Papadatos, G.; Smit, I.; Leach, A. R. The ChEMBL Database in 2017.
Nucleic Acids Res. 2017, 45 (D1), D945–D954.

34

O6-6

Databases of similar epitopes, PoSSuMAg
andputative ligand-binding pockets on AlphaFold models,PoSSuMAF
Yuko Tsuchiya 1

yuko.tsuchiya@aist.go.jp

Kazuyoshi Ikeda 3

kazuyoshi.ikeda@riken,jp

1
2
3

Tomoki Yonezawa 2

yonezawa-tm@pha.keio.ac.jp

Yu Yamamori 1

yu.yamamori@aist.go.jp

Masanori Osawa 2

osawa-ms@pha.keio.ac.jp

Kentaro Tomii 1k-tomii@aist.go.jp

AIRC, AIST, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0063, Japan
Keio Univ., 1-5-30 Shibakoen, Minato-ku, Tokyo 105-8512, JapanRIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

Keywords: similarity search of binding sites, epitopes, ligand-binding pockets, AlphaFoldPoSSuM is a database of similar ligand-binding and putative pockets on proteins [1]. Based on thePoSSuM scheme for detecting putative pockets and searching similar binding sites [2-5], we havedeveloped two new databases. One is a database of similar epitopes, antibody-binding sites on(putative) antigen proteins [6]. The current version contains the data of about 75,000 similar pairs
between known and known, and over two million similar pairs between known and putative epitopes,
which includes the information of known and putative epitopes on SARS-CoV-2 proteins. We foundseveral putative epitopes that are similar to known epitopes on the antigen proteins in complex with
antibody drugs. The other is a database of putative ligand-binding pockets on AlphaFold models of20,891 human proteins in AlphaFold database [7], which are similar to the known ligand-bindingsites in the PoSSuM database [1]. Our method detected about 290,000 putative pockets on theAlphaFold models, and about 56 million similar pairs between known and putative pockets [8]. Itis expected that these databases will be helpful for drug discovery and development.

[1] https://possum.cbrc.pj.aist.go.jp/PoSSuM/[2] Ito, J.; Ikeda, K.; Yamada, K.; Mizuguchi, K.; Tomii, K.; PoSSuM v.2.0: data update and a newfunction for investigating ligand analogs and target proteins of small-molecule drugs, Nucleic
Acids Res, 2015, 43, D392-398.
[3] Ito, J.; Tabei, Y.; Shimizu, K.; Tsuda, K.; Tomii, K.; PoSSuM: a database of similar proteinligand binding and putative pockets, Nucleic Acids Res, 2012, 40, D541-548.
[4] Ito, J.; Tabei, Y.; Shimizu, K.; Tomii, K.; Tsuda, K.; PDB-Scale analysis of known and putativeligand binding sites with structural sketches, Proteins, 2011, 80, 747-763.
[5] Tabei, Y.; Uno, T.; Sugiyama, M.; Tsuda, K.; Single Versus Multiple Sorting for All Pairs
Similarity Search, The 2nd Asian Conference on Machine Learning (ACML2010), 2010.
[6] https://possum.cbrc.pj.aist.go.jp/PoSSuMAg/[7] https://alphafold.ebi.ac.uk/[8] https://possum.cbrc.pj.aist.go.jp/PoSSuMAF/

35