多维广义朗文方程的粗粒度构象动力学：如何、何时以及为何

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-09-11 DOI:10.1021/acs.jctc.4c00729

Pinchen Xie, Weinan E

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引用次数: 0

摘要

我们开发了一种数据驱动的广义朗文方程（AIGLE）方法，用于学习和模拟高维、异质、粗粒度（CG）构象动力学。在波动消散定理的约束下，该方法可以建立与全原子分子动力学动态一致（DC）的构象模型。我们还提出了 AIGLE 执行长期 DC 的实用标准。对具有 20 个 CG 位点的玩具聚合物和具有两个二面角的丙氨酸二肽的案例研究，阐明了在实践中采用 AIGLE 或其马尔可夫极限来建立 CG 构象动力学模型的原因。

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Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation–dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term DC. Case studies of a toy polymer, with 20 CG sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling CG conformational dynamics in practice.

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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Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere.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.