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遗传算法引导的多维虚拟系统耦合分子动力学方法(GA-guided mD-VcMD)

GA-guided mD-VcMD: A genetic-algorithm-guided method for multi-dimensional virtual-system coupled molecular dynamics.

作者信息

Higo Junichi, Kusaka Ayumi, Kasahara Kota, Kamiya Narutoshi, Hayato Itaya, Qilin Xie, Takahashi Takuya, Fukuda Ikuo, Mori Kentaro, Hata Yutaka, Fukunishi Yoshifumi

机构信息

Graduate School of Simulation Studies, University of Hyogo, Kobe, Hyogo 650-0047, Japan.

Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan.

出版信息

Biophys Physicobiol. 2020 Dec 26;17:161-176. doi: 10.2142/biophysico.BSJ-2020008. eCollection 2020.

Abstract

We introduced a conformational sampling method in an earlier report: The multi-dimensional virtual-system coupled molecular dynamics (mD-VcMD) enhances conformational sampling of a biomolecular system by computer simulations. Herein, new sampling method, a subzone-based mD-VcMD, is presented as an extension of mD-VcMD. Then, the subzone-based method is extended further using a genetic algorithm (GA) named the GA-guided mD-VcMD. In these methods, iterative simulation runs are performed to increase the sampled region gradually. The new methods have the following benefits: (1) They are free from a production run: i.e., all snapshots from all iterations are useful for analyses. (2) They are free from fine tuning of a weight function (probability distribution function or potential of mean force). (3) A canonical ensemble (i.e., a thermally equilibrated ensemble) is generated from a simple procedure. A thermodynamic weight is assigned to each snapshot. (4) Selective sampling can be performed for particularly addressing a poorly sampled region without breaking the proportion of the canonical ensemble if the poorly sampled conformational region emerges in sampling. By applying the methods to a simple system that involves an energy barrier between potential-energy minima, we demonstrated that the new methods have considerably higher sampling efficiency than the original mD-VcMD does.

摘要

我们在之前的一份报告中介绍了一种构象采样方法

多维虚拟系统耦合分子动力学(mD-VcMD)通过计算机模拟增强了生物分子系统的构象采样。在此,提出了一种新的采样方法——基于子区域的mD-VcMD,作为mD-VcMD的扩展。然后,使用一种名为遗传算法引导的mD-VcMD的遗传算法(GA)进一步扩展基于子区域的方法。在这些方法中,进行迭代模拟运行以逐渐增加采样区域。新方法具有以下优点:(1)它们无需生产运行:即所有迭代的所有快照都可用于分析。(2)它们无需对权重函数(概率分布函数或平均力势)进行微调。(3)通过一个简单的过程生成正则系综(即热平衡系综)。为每个快照分配一个热力学权重。(4)如果在采样中出现采样不足的构象区域,可以进行选择性采样,以特别针对采样不足的区域,而不会破坏正则系综的比例。通过将这些方法应用于一个在势能极小值之间存在能垒的简单系统,我们证明了新方法的采样效率比原始的mD-VcMD高得多。

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