Please use this identifier to cite or link to this item:
Title: Markov dynamic models for long-timescale protein motion
Authors: Chiang, T.-H.
Hsu, D. 
Latombe, J.-C.
Issue Date: 2010
Citation: Chiang, T.-H., Hsu, D., Latombe, J.-C. (2010). Markov dynamic models for long-timescale protein motion. Bioinformatics 26 (12) : i269-i277. ScholarBank@NUS Repository.
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 longtimescale protein motions. Our method was tested on 2D 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 six states, a simpler model with only three 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 firstpassage time. The results are consistent with available experimental measurements. Contact: © The Author(s) 2010. Published by Oxford University Press.
Source Title: Bioinformatics
ISSN: 13674803
DOI: 10.1093/bioinformatics/btq177
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Aug 15, 2019


checked on Aug 15, 2019

Page view(s)

checked on Aug 10, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.