Application of Hidden Markov Models in Biomolecula

Authors: Saurabh Shukla Zahra Shamsi Alexander S Moffett Balaji Selvam Diwakar Shukla

Publish Date: 2017

Volume: , Issue: , Pages: 29-41

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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 Metenkephalin peptide simulation dataset and compare the timescales of the process

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