Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States.
Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, United States.
J Chem Theory Comput. 2021 May 11;17(5):2948-2963. doi: 10.1021/acs.jctc.0c00933. Epub 2021 Apr 28.
Elucidating physical mechanisms with statistical confidence from molecular dynamics simulations can be challenging owing to the many degrees of freedom that contribute to collective motions. To address this issue, we recently introduced a dynamical Galerkin approximation (DGA) [Thiede, E. H. , 150, 2019, 244111], in which chemical kinetic statistics that satisfy equations of dynamical operators are represented by a basis expansion. Here, we reformulate this approach, clarifying (and reducing) the dependence on the choice of lag time. We present a new projection of the reactive current onto collective variables and provide improved estimators for rates and committors. We also present simple procedures for constructing suitable smoothly varying basis functions from arbitrary molecular features. To evaluate estimators and basis sets numerically, we generate and carefully validate a data set of short trajectories for the unfolding and folding of the trp-cage miniprotein, a well-studied system. Our analysis demonstrates a comprehensive strategy for characterizing reaction pathways quantitatively.
从分子动力学模拟中以统计置信度阐明物理机制可能具有挑战性，因为许多自由度会导致集体运动。为了解决这个问题，我们最近引入了动力学伽辽金近似（DGA）[Thiede, E. H., 150, 2019, 244111]，其中满足动力学算子方程的化学动力学统计量由基展开表示。在这里，我们重新表述了这种方法，澄清了（并减少了）对滞后时间选择的依赖性。我们提出了将反应电流投影到集体变量上的新方法，并提供了改进的速率和承诺估计量。我们还提出了从任意分子特征构建合适的平滑变化基函数的简单过程。为了在数值上评估估计量和基集，我们生成并仔细验证了一个用于 trp-cage 小蛋白的展开和折叠的短轨迹数据集，这是一个研究充分的系统。我们的分析展示了一种全面的策略，用于定量描述反应途径。
