Institute of Mathematics, Freie Universitaet Berlin, D-14195 Berlin, Germany.
J Chem Phys. 2011 May 28;134(20):204105. doi: 10.1063/1.3590108.
Markov state models (MSMs) have become the tool of choice to analyze large amounts of molecular dynamics data by approximating them as a Markov jump process between suitably predefined states. Here we investigate "Core Set MSMs," a new type of MSMs that build on metastable core sets acting as milestones for tracing the rare event kinetics. We present a thorough analysis of Core Set MSMs based on the existing milestoning framework, Bayesian estimation methods and Transition Path Theory (TPT). We show that Core Set MSMs can be used to extract phenomenological rate constants between the metastable sets of the system and to approximate the evolution of certain key observables. The performance of Core Set MSMs in comparison to standard MSMs is analyzed and illustrated on a toy example and in the context of the torsion angle dynamics of alanine dipeptide.
马科夫状态模型（MSMs）已经成为分析大量分子动力学数据的首选工具，通过将其近似为在适当预定义状态之间的马科夫跳跃过程来实现。在这里，我们研究了“核心集 MSMs”，这是一种新的 MSMs，它基于作为追踪罕见事件动力学里程碑的亚稳态核心集。我们基于现有的里程碑框架、贝叶斯估计方法和转移路径理论（TPT），对核心集 MSMs 进行了全面的分析。我们表明，核心集 MSMs 可以用于提取系统亚稳集之间的现象学速率常数，并近似某些关键观测值的演化。我们在一个玩具示例和丙氨酸二肽的扭转角动力学的背景下，对核心集 MSMs 与标准 MSMs 的性能进行了分析和比较。
