O8-1

Computational and experimental studies of hot spots in thec-Myb–KIX interaction
Shunji Suetaka 1
shunji-suetaka@g.ecc.u-tokyo.ac.jp

Yuuki Hayashi 1,2
hayashi@bio.c.u-tokyo.ac.jp

Munehito Arai 1,3
arai@bio.c.u-tokyo.ac.jp

123

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo,
3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
Environmental Science Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 1130033, Japan
Department of Physics, Graduate School of Science, The University of Tokyo, 3 -8-1 Komaba,Meguro, Tokyo, 153-8902, Japan

Keywords: Protein–protein interaction, Hot spot, Peptide
Protein-protein interactions (PPIs) play key roles in biological functions. Since undesired PPIs
can cause serious diseases, inhibiting the PPIs is a promising strategy for therapeutics. However,developing inhibitors targeting PPIs is challenging due to their large and flat interfaces. A key
challenge in solving this problem is to identify energetically favorable residues in PPIs, called “hotspots” [1].
The KIX domain of the CREB-binding protein interacts with many transcription factors,including c-Myb, and is implicated in serious diseases [2]. Thus, inhibitors of the c-Myb–KIX
interaction is potentially useful as antitumor agents. Here, we computationally and experimentallyinvestigated hot spots in the binding of KIX to c-Myb. To identify the hot spots, we used various
computational tools including AlphaFold 2. Then, we experimentally searched for the host spots on
KIX using an alanine scanning mutagenesis for 21 residues at the c-Myb binding interface of KIX.
The changes in the stability and c-Myb binding affinity of the 21 mutants of KIX were examinedby circular dichroism and isothermal titration calorimetry, respectively. We found that three KIX
mutants reduced the c-Myb binding affinity by more than 2 kcal/mol, indicating that these residues
are the hot spots for c-Myb binding of KIX.
Comparison of the computational alanine scanning mutagenesis with the experimental results
showed that the mCSM program [3] outperformed other computational methods in predicting th e
hot spots of KIX. In addition, we propose a method to find hot spots using the pLDDT scores
predicted by AlphaFold 2, which was the second best method for predicting the host spots of KIX.
These results may be useful in computationally identifying hot spots in PPIs.
[1] Bogan, A. A.; Thorn, K.S. Anatomy of hot spots in protein interfaces, Journal of Molecular
Biology, 1998, 280, 1–9.
[2] Thakur, J. K.; et al., Molecular recognition by the KIX domain and its role in gene regulation ,Nucleic Acids Research, 2014, 42, 2112–2125.
[3] Pires, D. E.; et al., mCSM: predicting the effects of mutations in proteins using graph -based
signatures. Bioinformatics, 2014, 30, 335–342.

O8-2

Upgrade of AI-AAM and its application to lead-to-leadand peptide downsizing scaffold hopping
Kyosuke Tsumura 1

Jun Nakabayashi 1

kyosuke.tsumura@fujifilm.com

jun.nakabayashi@fujifilm.com

Shino Ohira 1
shino.ohira@fujifilm.com

1

Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minamiashigara-shi,Kanagawa 250-0193, Japan

Keywords: AI Drug Discovery, LBDD, de novo design, PPIs, AI-AAM
The failure rate in drug discovery and development after in vivo and vitro testing is higher than
90% [1]. To minimize the rate, we developed a scaffold hopping methodology called Artificial
Intelligence-Amino Acid Mapping (AI-AAM) to obtain hit compounds from a known hit ligand
maintaining the binding affinities to its target protein [2]. Since it was highly essential to use thedescriptors that could represent binding affinities for the purposes, we designed a molecularsimulation-based descriptor, Amino Acid Mapping (AAM), assuming protein–ligand binding could
be described as the set of interactions between amino acids and the ligand. As an extension of theprevious work, we upgraded AI-AAM to automatically find and design a lead compound from aknown lead ligand and a hit compound from a bioactive peptide (Fig. 1). Here, we provide a brief
description of the content of each work (see our posters for the detail).
Search and design for compoundswith equivalent AAM descriptors using AI

Computation of AAM descriptors
using molecular simulations

Lead-to-lead

Scaffold
hopping×

◎×××

○AAM
Descriptors
Downsizing

Scaffold
hopping

Fig. 1 Overview of AI-AAM[1] D. Sun.; et al., Why 90% of clinical drug development fails and how to improve it? Acta Pharm.
Sin. B. 2022, 12, 3049.
[2] K. Tsumura.; et al., A newly developed method based on AI-oriented amino acid interaction
mapping (AI-AAM) for efficient virtual scaffold hopping, CBI Annual Meeting 2018, P2-02.

O8-3

Molecular design method using a reversible tree
representation of chemical compounds and deep
reinforcement learning
Ryuichiro Ishitani 1
ishitani@preferred.jp

Toshiki Kataoka 1
kataoka@preferred.jp

Kentaro Rikimaru 1
krikimaru@preferred.jp

1

Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo,100-0004, Japan

Keywords: deep learning, reinforcement learning, drug design
Automatic design of molecules with specific chemical and biochemical properties is an important
process in material informatics and computational drug discovery. In this study, we designed a novel
coarse-grained tree representation of molecules (“RJT”) for the aforementioned purposes, which isreversely convertible to the original molecule without external information. By leveraging thisrepresentation, we further formulated the molecular design and optimization problem as a treestructure construction using deep reinforcement learning (“RJT-RL”). In this method, all theintermediate states of reinforcement learning and the final structures are convertible to valid
molecules, which could efficiently guide the optimization process in simple benchmark tasks. We
further examined the multi-objective optimization and fine-tuning of the reinforcement learningmodels using RJT-RL, demonstrating the applicability of our method to more realistic tasks in drug
discovery.

O8-4
Improvement of a Prototype VR AFM Manipulation System
Emulated by a Dispensing Machine
Hirotada Kondo1

kondo@vraide.jp

Akinori Kuzuya1,2,3kuzuya@kansai-u.ac.jp

Gutmann Gregory Spence2

gutmann@molecular-robotics.co.jp

Akihiko Konagaya2

konagaya@molecular-robotics.co.jp

Organization for Research and Development of Innovative Science and Technology, Kansai University
Molecular Robotics Research Institute, Co., Ltd.
3
Department of Chemistry and Materials Engineering, Kansai University
1
2

Keywords: Molecular Robotics, VR Simulation, Atomic Force Microscope, DNA origami

Molecular manipulation using a microscope probe tip is one of promising approaches to be applied not
only for observation but also for selective molecular capture and microfabrication. In order to solve
difficulties in operations of molecular manipulation using microscope imaging, we propose a VR system
for interactive and intuitive molecular operations. The system consists of a virtual Atomic ForceMicroscope (VAFM) as a molecular-sized robot arm, virtual molecules, and a remote machine operationinterface. Our virtual nano-mechanical DNA origami device (VDNA pliers) attached on the VAFM canbind a single target molecule selectively, so that it works as a gripper of the robot arm.
We developed a VAFM with remote machine operation interface on the VR system to manipulate adispensing machine that emulates a real AFM, with hand movements and to observe the movements. We
also improved the system for obtaining more detailed hand information and for modifying the virtual
cantilever movements on the VAFM with machine learning techniques to increase the speed and accuracyof the movements.

[1] Gregory Gutmann, Ryuzo Azuma, Akihiko Konagaya: A Virtual Reality Computational Platform
Dedicated for the Emergence of Global Dynamics in a Massive Swarm of Objects, J. of the Imaging
Society of Japan, 2018, 57(6), 647-653.
[2] Akinori Kuzuya, Yusuke Sakai, Takahiro Yamazaki, Yan Xu, Makoto Komiyama: NanomechanicalDNA origami 'single-molecule beacons' directly imaged by atomic force microscopy, Nature Commun.,
2011, 2, 449.

O8-5

Experimental Investigation of Extended Nucleic Acid
Generation Circuits for DNA-based Signal Transduction
Ken Komiya 1

Chizuru Noda 1

komiyak@jamstec.go.jp

1

Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Japan
Agency for Marine-Earth Science & Technology (JAMSTEC), 2-15 Natsushima-cho,Yokosuka, Kanagawa, 237-0061, Japan

Keywords: DNA generation circuit, DNA-based computing, molecular robotics
We had previously proposed various types of extension for a DNA generation circuit that generate
single-stranded DNA molecules as signals at a physiological temperature in response to nucleic acid
inputs [1]. For the purpose of nucleic acid testing and construction of synthetic signal transduction
systems for DNA-based computing and molecular robotics, its modular architecture allowing
modulation of signal generation performance via design and combination of template DNA strands
is promising [2-4]. In the present study, we experimentally investigated the DNA generation circuit
in terms of the input nucleic acid forms and fluorophore-modified DNA probes. In addition,
application of the DNA generation circuit within a tiny solution environment towards digitalbioassay and connection to the other DNA-based computing reaction for achieving more intelligent
functions of DNA-based artificial molecular systems will be discussed.
[1] Komiya, K.; Yamamura, M. Cascading DNA generation reaction for controlling DNA
nanomachines at a physiological temperature, New Generation Computing, 2015, 33, 213-229.
[2] Komiya, K.; Komori, M.; Noda, C.; Kobayashi, S.; Yoshimura T.; Yamamura, M. Leak-freemillion-fold DNA amplification with locked nucleic acid and targeted hybridization in one pot,Organic and Biomolecular Chemistry, 2019, 17, 5708-5713.
[3] Komori, M.; Komiya, K.; Shirakawa, T.; Morikawa, T. J.; Yoshimura, T. Measurement ofmicroRNA with isothermal DNA amplification on fully automated immunoassay analyzers,
Analytical and Bioanalytical Chemistry, 2019, 411, 3789-3800.
[4] Sato, Y.; Komiya, K.; Kawamata, I.; Murata S.; Nomura, S. M. Isothermal amplification ofspecific DNA molecules inside giant unilamellar vesicles, Chemical Communications, 2019, 55,9084-9087.