Oral Presentation 6

日時： 2020 年 10 月 29 日 17:30-19:00
チャンネル： ３

口頭発表６『分子ロボティクス/計算化学（分子認識）』
Molecular robotics/Computational Chemistry (Molecular Recognition)
座長： 川又 生吹 （東北大学）
Ibuki Kawamata (Tohoku 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)
O3-05"Temperature-based inputs for molecular reservoir computers"
Nicolas Lobato-Dauzier(Institute of Industrial Science, The University of Tokyo)
O3-06"Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using
Surrogate-Assisted MAP-Elites"
Leo Cazenille(Department of Information Sciences, Ochanomizu University, Tokyo, Japan)
O3-07"Prediction of Binding of Existing Drugs to SARS-CoV-2 Main Protease Using
Molecular Dynamics and Fragment Molecular Orbital Calculations"
Yuma Handa(School of Pharmacy and Pharmaceutical Sciences, Hoshi University)

O3-05Temperature-based inputs for molecular reservoir computers
Nicolas Lobato-Dauzier1,2
lobato@iis.u-tokyo.ac.jp

Leo Cazenille3

leo.cazenille@gmail.com

Teruo Fujii1,2

tfujii@iis.u-tokyo.ac.jp

Anthony Genot1,2Nathanael Aubert-Kato3

genot@iis.u-tokyo.ac.jp

naubertkato@is.ocha.ac.jp1

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku,
Tokyo, 153-8505, Japan
2LIMMS, CNRS-IIS, The University of Tokyo, 4-6-1 Komaba, Meguro-ku,
Tokyo, 153-8505 Japan3
Department of Information Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku ,Tokyo, 112-8610, Japan

Keywords: DNA nanotechnology, Reservoir computing, Predator-prey oscillator, in-vitro DNA computing
We design and implement a temperature-based input mechanism for molecular reservoir computing.
Reservoir computing uses a complex but fixed network which is treated as black box combined to a final readout layer which is trained to perform the desired task. This approach of exploiting the complex dynamics ofthe system rather than overcoming them through rational design allows us to design smaller systems, increasing
the practicality of in-vitro experiments. Previously proposed molecular approaches rely on a chemically opensystem, which prevents several potential applications like implementing a controller for molecular robots [1].
Using temperature allows us to interact with the system while keeping it chemically closed.
The input layer of the network corresponds to the impact temperature has on the dynamics of the system. As
reservoir, we use the predator-prey system from [2], a simple, but robust molecular oscillator. We tune
experimentally that system to switch between oscillatory and non-oscillatory behavior based on thetemperature. The state of the system is monitored in real-time by fluorescence. The read-out layer isimplemented by a neural network [3]. The full system is summarized in Fig. 1.

Figure 1:
Reservoir computing scheme: temperature is set
externally, impacting the behavior of the reservoir
made of a predator-prey oscillator. A sliding
window of fluorescence, capturing the state of thesystem, is fed into the output layer of the reservoir.

We first characterize the behavior of the system, provide a range of working conditions suitable for its role
as reservoir and finally show that it can reliably transmit information by training the system to recover its
input signal, a standard benchmark for Reservoir Computing. We reach an average of 87% accuracy for asingle layer and 91% for two layers, showing the potential of such reservoir [4].
[1] Sato, Y., Hiratsuka, Y., Kawamata, I., Murata, S., & Nomura, S. I. M.,Micrometer-sized molecular -robotchanges its shape in response to signal molecules. Sci. Robot, 2(4), 2017[2] Fujii, T. and Rondelez, Y. Predator–prey molecular ecosys- tems. ACS nano, 7(1):27–34, 2013
[3] Chollet, F., Keras. https://keras.io, 2015
[4] Lobato-Dauzier, N., Cazenille, L., Fujii, T., Genot, A., & Aubert-Kato, N., Temperature-based inputs formolecular reservoir computers. In Artificial Life Conference Proceedings, MIT Press, 420-422, 2020

O3-06

Exploring Self-Assembling Behaviors in a Swarm of Biomicro-robots using Surrogate-Assisted MAP-Elites
Leo Cazenille1
leo.cazenille@gmail1
2

Nicolas Bredeche1

Nathanael Aubert-Kato2

nicolas.bredeche@sorbonne-universite.fr aubert.kato.nathanael@ocha.ac.jp

Department of Information Sciences, Ochanomizu University, Tokyo, Japan
ISIR, Sorbonne Université, CNRS, Paris, France

Keywords: Bio-micro-robots, swarm robotics, molecular programming, evolutionary robotics, surrogate
models, quality-diversity algorithms, MAP-Elites
Swarms of molecular robots are a promising approach to create specific shapes at the microscopic scale
through self-assembly [1]. However, controlling their behavior is a challenging problem as it involves
complex non-linear dynamics and high experimental variability. Hand-crafting a molecular controller willoften be time-consuming and give sub-optimal results. Optimization methods, like the Bioneat [1] algorithm,
were previously used to partially overcome these difficulties, but they still had to cope with deceptive search
spaces and computationally expensive simulations.
Here, we describe a novel approach to automatically design the chemical reaction network controllers of alarge swarm (>>1000) of micro-robots, by using MAP-Elites [2], an optimization algorithm that searches forboth high-performing and diverse solutions, and CMA-ES [3], a state-of-the art optimization algorithm. We
apply them to a molecular robotic framework we recently introduced [1,5] that allows sensing, signaling andself-assembly at the micro-scale and show that MAP-Elites outperforms previous approaches. We propose asurrogate model of micro-robots physics and chemical reaction dynamics to reduce the computational costsof simulations. We show that this methodology is capable of optimizing controllers with similar accuracy as
when using a full-fledged realistic model, with half the computational budget.
Robot design

Experimental arena

Results: best CRNs - expert vs optimized
TargetFigure 1: The micro-robots aremicroscopic
agarosebeads
functionalized with CRNs thatserve as robotic controllers [4].
They move through Brownian
motion, can interact with theirenvironment and aggregate into
self-assembled structures. CRNs
are optimized by MAP-Elites sothat the swarm self-aggregates
into a T-shaped pattern.

[1] Aubert-Kato, N, et al. "Evolutionary optimization of self-assembly in a swarm of bio-micro-robots."
GECCO 2017.
[2] Mouret, JB, and Clune, J. "Illuminating search spaces by mapping elites." arXiv:1504.04909 (2015).
[3] Hansen, N. "The CMA evolution strategy: a comparing review." Springer, 2006.
[4] Gines, G, et al. "Microscopic agents programmed by DNA circuits." Nat. nanotech. (2017).
[5] Zadorin, AS, et al. "Synth. and materialization of a R-D French flag pattern." Nat. Chem. (2017).

O3-07Prediction of Binding of Existing Drugs to SARS-CoV-2
Main Protease Using Molecular Dynamics and Fragment
Molecular Orbital Calculations
Yuma Handa 1 Yusuke Kawashima1 Ryo Hatada 2 Koji Okuwaki 2 Kazuki Akisawa 2
Yuji Mochizuki 2 Yuto Komeiji 3 Shigenori Tanaka 4 Takayuki Furuishi 1
Kaori Fukuzawa 1 Etsuo Yonemochi 1
d2002@hoshi.ac.jp y-kawashima@hoshi.ac.jp hatada@rikkyo.ac.jp okuwaki@rikkyo.ac.jp
17cc044m@rikkyo.ac.jp fullmoon@rikkyo.ac.jp y-komeiji@aist.go.jp tanaka2@kobe-u.ac.jp
t-furuishi@hoshi.ac.jp k-fukuzawa@hoshi.ac.jp e-yonemochi@hoshi.ac.jp
1234

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
Health and Medical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566,
Japan
Graduate School of System Informatics, Department of Computational Science, Kobe
University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan

Keywords: SARS-CoV-2 Main protease, Fragment molecular orbital method, Molecular dynamics
COVID-19 has become a global concern and the development of drugs to treat this disease is being
urgently pursued. HIV-1 protease inhibitors such as Nelfinavir and Lopinavir have been proposed as
potential inhibitors of the main protease (Mpro), which is a protein produced by the SARS -CoV-2 virus.
However, the crystal structures of these inhibitors in complex with Mpro have not yet been determined.
In this study, we have combined docking, molecular dynamics (MD) and fragment molecular orbital(FMO) calculations to predict the binding structures and their properties of Mpro to several inhibitors.
First, 30 docking poses were predicted by MOE using AMBER10:EHT force field. Then,FMO-MP2(PR)/6-31G* calculations using ABINIT-MP [1,2] were performed to narrow down thecandidate structures to 4-6. These docking poses were used as the initial structures for 100 ns MD
simulations, and 100 structures were extracted for each of the obtained trajectories. For all theextracted structures, FMO calculations were performed and statistical interaction analysis of the results
was performed.
The results showed that the rankings using FMO interaction energies for the initial docking
structures and those for the post-MD simulated structures were different in order. Therefore,predictions based on MD trajectories rather than docking alone were considered to be important.
Furthermore, in the interaction between Mpro and the inhibitor, not only the electrostatic interaction
with charged residues, but also dispersion interactions such as CH/π interaction between hydrophobic
functional groups were found to be strongly involved in the binding. In addition, Nelfinavir was foundto bind more strongly to Mpro than Lopinavir. This was consistent with the results of the EC 50 [3] .
[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). MD
simulations were performed using TSUBAME 3.0 at the Tokyo Institute of Technology. For FMO
calculations, computational resources of RIKEN R-CCS Fugaku (priority trial use against
coronavirus) and Oakforest-PACS (hp200146 quota) at JCAHPC were used.
[1] Tanaka, S., et al., Electron-Correlated Fragment-Molecular-Orbital Calculations for
Biomolecular and Nano Systems. Phys. Chem. Chem. Phys.,16 (22): 10310–10344, 2014.
[2] 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.
[3] bioRxiv (2020), https://doi.org/10.1101/2020.04.06.026476