Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
Data Science Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
J Chem Phys. 2024 Mar 28;160(12). doi: 10.1063/5.0189429.
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.
蛋白质构象变化在其生物学功能中起着至关重要的作用。近年来，从广泛的分子动力学（MD）模拟中构建的马尔可夫状态模型（MSM）已成为模拟复杂蛋白质构象变化的强大工具。在 MSM 中，动力学被建模为在离散时间间隔（称为滞后时间）中在亚稳态构象状态之间的一系列马尔可夫跃迁。MSM 的一个主要挑战是滞后时间必须足够长，以使状态之间的跃迁变得无记忆（或马尔可夫）。然而，这个滞后时间受到可以跟踪这些跃迁的单个 MD 模拟的长度的限制。为了解决这个挑战，我们最近开发了基于广义主方程（GME）的方法，使用时变记忆核来编码非马尔可夫动力学。在本教程中，我们介绍了最近开发的两种基于 GME 的非马尔可夫动力学模型的理论：拟马尔可夫状态模型（qMSM）和积分广义主方程（IGME）。随后，我们概述了构建这些模型的过程，并提供了一个逐步的教程，介绍如何将 qMSM 和 IGME 应用于研究两个肽系统：丙氨酸二肽和绒毛蛋白头部片段。本教程可在 https://github.com/xuhuihuang/GME_tutorials 上获得。本教程中详细介绍的方案旨在为有兴趣使用这些非马尔可夫动力学模型研究生物分子动力学的非专家提供帮助。
