@inbook{869d87eac1294f14881fec6b5145f55c,
title = "Application of Hidden Markov models in biomolecular simulations",
abstract = "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.",
keywords = "Hidden Markov models, Markov state models, Molecular dynamics, Protein conformational change, Protein dynamics",
author = "Saurabh Shukla and Zahra Shamsi and Moffett, {Alexander S.} and Balaji Selvam and Diwakar Shukla",
note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media LLC 2017.",
year = "2017",
doi = "10.1007/978-1-4939-6753-7_3",
language = "English (US)",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "29--41",
booktitle = "Methods in Molecular Biology",
}