Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning
Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning
复制标题
DOI：
10.3390/biom10030482
复制
发表时间：
2020-03-01
期刊：
影响因子：
5.5
通讯作者：
Terayama, Kei
中科院分区：
文献类型：
--
作者：
Kanada, Ryo;Tokuhisa, Atsushi;Terayama, Kei
随着结构生物学的进步，揭示生物分子结构的方法越来越多，分子动力学(MD)方法，特别是适用于大分子的粗粒(CG)分子动力学方法，对于阐明它们的动力学和行为变得越来越重要。事实上，CG-MD模拟已经成功地以合理的计算代价定性地再现了各种生物分子的各种生物过程，如构象变化和蛋白质折叠。然而，CG-MD模拟强烈依赖于各种参数，而选择合适的参数集对于再现特定的生物过程是必要的。由于对所有候选参数的穷举检查是低效的，因此识别成功的参数非常重要。此外，所需过程可重现的成功区域对于描述功能过程的详细机制以及环境敏感性和稳健性是必不可少的。通过使用贝叶斯优化和主动学习两种机器学习技术，提出了一种有效的搜索方法来识别成功区域。我们使用F1-ATPase生物旋转马达，通过CG-MD模拟来评估其性能。与穷举搜索相比，我们在不牺牲精确度的情况下，以较低的计算代价(最好的情况是12.3%)成功地确定了成功区域。这种方法不仅可以加速参数搜索，而且可以加速基于MD模拟研究的功能过程和环境敏感性的详细机制的生物学讨论。
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.