Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes

计算机科学 马尔可夫链 马尔可夫过程 主方程 统计物理学 分子动力学 马尔可夫模型 核(代数) 理论计算机科学 物理 数学 化学 计算化学 机器学习 组合数学 统计 量子 量子力学
作者
Y. Wu,Siqin Cao,Yunrui Qiu,Xuhui Huang
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:160 (12) 被引量:4
标识
DOI:10.1063/5.0189429
摘要

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.
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