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Open AccessArticle

Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

by 1,*, 1,2,3, 4,5,6, 1,3,7 and 3,5,7,*
1
RIKEN Compass to Healthy Life Research Complex Program, Kobe 650-0047, Japan
2
RIKEN Center for Computational Science, Kobe 650-0047, Japan
3
RIKEN Medical Sciences Innovation Hub Program, Yokohama 230-0045, Japan
4
Graduate School of Frontier Sciences, he UTniversity of Tokyo, Kashiwa 277-8561, Japan
5
RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
6
Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba 305-0047, Japan
7
Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
*
Authors to whom correspondence should be addressed.
Biomolecules 2020, 10(3), 482; https://doi.org/10.3390/biom10030482
Received: 15 February 2020 / Revised: 11 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies. View Full-Text
Keywords: coarse-grained molecular dynamics simulation; biological rotary motor; machine learning; active learning; bayesian optimization coarse-grained molecular dynamics simulation; biological rotary motor; machine learning; active learning; bayesian optimization
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MDPI and ACS Style

Kanada, R.; Tokuhisa, A.; Tsuda, K.; Okuno, Y.; Terayama, K. Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning. Biomolecules 2020, 10, 482. https://doi.org/10.3390/biom10030482

AMA Style

Kanada R, Tokuhisa A, Tsuda K, Okuno Y, Terayama K. Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning. Biomolecules. 2020; 10(3):482. https://doi.org/10.3390/biom10030482

Chicago/Turabian Style

Kanada, Ryo; Tokuhisa, Atsushi; Tsuda, Koji; Okuno, Yasushi; Terayama, Kei. 2020. "Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning" Biomolecules 10, no. 3: 482. https://doi.org/10.3390/biom10030482

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