Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Adv Exp Med Biol. 2014;805:29-66. doi: 10.1007/978-3-319-02970-2_2.
Conformational changes of proteins are an*Author contributed equally with all other contributors. essential part of many biological processes such as: protein folding, ligand binding, signal transduction, allostery, and enzymatic catalysis. Molecular dynamics (MD) simulations can describe the dynamics of molecules at atomic detail, therefore providing a much higher temporal and spatial resolution than most experimental techniques. Although MD simulations have been widely applied to study protein dynamics, the timescales accessible by conventional MD methods are usually limited to timescales that are orders of magnitude shorter than the conformational changes relevant for most biological functions. During the past decades great effort has been devoted to the development of theoretical methods that may enhance the conformational sampling. In recent years, it has been shown that the statistical mechanics framework provided by discrete-state and -time Markov State Models (MSMs) can predict long timescale dynamics from a pool of short MD simulations. In this chapter we provide the readers an account of the basic theory and selected applications of MSMs. We will first introduce the general concepts behind MSMs, and then describe the existing procedures for the construction of MSMs. This will be followed by the discussions of the challenges of constructing and validating MSMs, Finally, we will employ two biologically-relevant systems, the RNA polymerase and the LAO-protein, to illustrate the application of Markov State Models to elucidate the molecular mechanisms of complex conformational changes at biologically relevant timescales.
蛋白质构象变化是许多生物过程的重要组成部分，如：蛋白质折叠、配体结合、信号转导、变构和酶催化。分子动力学 (MD) 模拟可以在原子细节上描述分子的动力学，因此提供了比大多数实验技术更高的时间和空间分辨率。尽管 MD 模拟已被广泛应用于研究蛋白质动力学，但常规 MD 方法可达到的时间尺度通常仅限于与大多数生物功能相关的构象变化短得多的时间尺度。在过去的几十年中，人们付出了巨大的努力来开发可能增强构象采样的理论方法。近年来，已经表明离散状态和时间马尔可夫状态模型 (MSM) 提供的统计力学框架可以从短 MD 模拟池中预测长时间尺度动力学。在本章中，我们为读者提供了 MSM 基本理论和选定应用的说明。我们将首先介绍 MSM 背后的一般概念，然后描述构建 MSM 的现有程序。接下来将讨论构建和验证 MSM 所面临的挑战，最后，我们将使用两个与生物学相关的系统，RNA 聚合酶和 LAO 蛋白，来说明马尔可夫状态模型在阐明复杂构象变化的分子机制方面在生物学相关时间尺度上的应用。
