Global Optimization of Large Molecular Systems Using Rigid-Body Chain Stochastic Surface Walking

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2025-05-27 DOI:10.1021/acs.jctc.5c0035010.1021/acs.jctc.5c00350

Tong Guan, Xin-Tian Xie, Xiao-Jie Zhang, Cheng Shang* and Zhi-Pan Liu*,

{"title":"Global Optimization of Large Molecular Systems Using Rigid-Body Chain Stochastic Surface Walking","authors":"Tong Guan, Xin-Tian Xie, Xiao-Jie Zhang, Cheng Shang* and Zhi-Pan Liu*, ","doi":"10.1021/acs.jctc.5c0035010.1021/acs.jctc.5c00350","DOIUrl":null,"url":null,"abstract":"<p >The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to “the curse of dimensionality”. To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle–axis representation for a single rigid body, our algorithm realizes the cooperative motion of connected rigid bodies and achieves the coupling between rigid-body chain movement and lattice variation in the generalized coordinate. By exploiting the numerical energy second derivative information on rigid bodies, RC-SSW can optimize the global PES of large molecular systems with an unprecedentedly high efficiency. We show that RC-SSW is more than 10 times faster in locating the model protein global minimum while revealing many more low energy conformations than molecular dynamics and can identify low energy phases of molecular crystals up to 172 atoms missed in the sixth CCDC blind test.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 11","pages":"5757–5770 5757–5770"},"PeriodicalIF":5.7000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Theory and Computation","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jctc.5c00350","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}

引用次数: 0

Abstract

The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to “the curse of dimensionality”. To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle–axis representation for a single rigid body, our algorithm realizes the cooperative motion of connected rigid bodies and achieves the coupling between rigid-body chain movement and lattice variation in the generalized coordinate. By exploiting the numerical energy second derivative information on rigid bodies, RC-SSW can optimize the global PES of large molecular systems with an unprecedentedly high efficiency. We show that RC-SSW is more than 10 times faster in locating the model protein global minimum while revealing many more low energy conformations than molecular dynamics and can identify low energy phases of molecular crystals up to 172 atoms missed in the sixth CCDC blind test.

查看原文本刊更多论文

基于刚体链随机曲面行走的大分子系统全局优化

由于“维度的诅咒”，大分子体系的全局势能面（PES）搜索仍然是化学领域的一个重大挑战。为了解决这个问题，我们在随机表面行走（SSW）全局优化方法的框架下开发了刚体链方法，称为刚体链SSW （RC-SSW）。该算法基于单个刚体的角轴表示，实现了连接刚体的协同运动，在广义坐标下实现了刚体链运动与点阵变化的耦合。RC-SSW利用刚体的数值能量二阶导数信息，能够以前所未有的高效率优化大分子体系的全局PES。我们发现RC-SSW在定位模型蛋白全局最小值方面比分子动力学快10倍以上，同时揭示了更多的低能构象，并且可以识别出在第六次CCDC盲测中缺失的多达172个原子的分子晶体的低能相。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.