Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why,Journal of Chemical Theory and Computation

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Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-11 , DOI: 10.1021/acs.jctc.4c00729
Pinchen Xie _{1,

2} , Weinan E _{3,

4}

Affiliation

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.

中文翻译：

具有多维广义朗之万方程的粗粒构象动力学：如何、何时和为什么

开发了一种数据驱动的从头算广义朗之万方程 (AIGLE) 方法来学习和模拟高维、异质、粗粒度 (CG) 构象动力学。受涨落耗散定理的约束，该方法可以建立具有全原子分子动力学的动态一致性（DC）CG模型。我们还为 AIGLE 实施长期 DC 提出了实用标准。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为什么在实践中应采用 AIGLE 或其马尔可夫极限来建模 CG 构象动力学。

更新日期：2024-09-11

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