Shukla Saurabh, Shamsi Zahra, Moffett Alexander S, Selvam Balaji, Shukla Diwakar
Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Methods Mol Biol. 2017;1552:29-41. doi: 10.1007/978-1-4939-6753-7_3.
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.
隐马尔可夫模型（HMMs）提供了一个框架来分析生物分子模拟数据集的大型轨迹。HMMs将生物分子的构象空间分解为有限数量的状态，这些状态以一定的速率相互转换。HMMs简化了长时间尺度的轨迹以便于人类理解，并允许将模拟与实验数据进行比较。在本章中，我们概述了构建用于分析双分子模拟数据集的HMMs的方法。我们展示了为甲硫氨酸脑啡肽肽模拟数据集构建隐马尔可夫模型的过程，并比较了该过程的时间尺度。
