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Markov Dynamic Models for Long-Timescale Protein Motion
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BibTeX
@MISC{Chiang_markovdynamic,
author = {Tsung-han Chiang and David Hsu and Jean-claude Latombe},
title = {Markov Dynamic Models for Long-Timescale Protein Motion},
year = {}
}
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Abstract
Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2-D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains 6 states, a simpler model with only 3 states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements. Contact:
Keyphrases
long-timescale protein motion markov dynamic model alanine dipeptide protein motion protein folding villin headpiece subdomain md simulation data long-timescale motion hp-35 nlenle markovian state generates massive amount 2-d synthetic energy landscape studied peptide protein conformation mean first-passage time well-established method data analysis molecular dynamic simplified model constructed markov model dual challenge available experimental measurement principled criterion probabilistic distribution simpler model atomic scale computation efficiency dynamic quantity hidden state markov model
