Handle URI:
http://hdl.handle.net/10754/596801
Title:
Markov dynamic models for long-timescale protein motion.
Authors:
Chiang, Tsung-Han; Hsu, David; Latombe, Jean-Claude
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 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 first-passage time. The results are consistent with available experimental measurements.
Citation:
Chiang T-H, Hsu D, Latombe J-C (2010) Markov dynamic models for long-timescale protein motion. Bioinformatics 26: i269–i277. Available: http://dx.doi.org/10.1093/bioinformatics/btq177.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
1-Jun-2010
DOI:
10.1093/bioinformatics/btq177
PubMed ID:
20529916
PubMed Central ID:
PMC2881362
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
AcRF grant R-252-000-350-112 from the Ministry of Education, Singapore (to D.H., in parts). National Science Foundation grant DMS-0443939 and two grants from the Academic Excellence Alliance program between King Abdullah University of Science&Technology (KAUST) and Stanford University (to J-C.L., in parts)
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorChiang, Tsung-Hanen
dc.contributor.authorHsu, Daviden
dc.contributor.authorLatombe, Jean-Claudeen
dc.date.accessioned2016-02-21T08:50:57Zen
dc.date.available2016-02-21T08:50:57Zen
dc.date.issued2010-06-01en
dc.identifier.citationChiang T-H, Hsu D, Latombe J-C (2010) Markov dynamic models for long-timescale protein motion. Bioinformatics 26: i269–i277. Available: http://dx.doi.org/10.1093/bioinformatics/btq177.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid20529916en
dc.identifier.doi10.1093/bioinformatics/btq177en
dc.identifier.urihttp://hdl.handle.net/10754/596801en
dc.description.abstractMolecular 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 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 first-passage time. The results are consistent with available experimental measurements.en
dc.description.sponsorshipAcRF grant R-252-000-350-112 from the Ministry of Education, Singapore (to D.H., in parts). National Science Foundation grant DMS-0443939 and two grants from the Academic Excellence Alliance program between King Abdullah University of Science&Technology (KAUST) and Stanford University (to J-C.L., in parts)en
dc.publisherOxford University Press (OUP)en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.0/uk/en
dc.titleMarkov dynamic models for long-timescale protein motion.en
dc.typeArticleen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC2881362en
dc.contributor.institutionNational University of Singapore, Singapore City, Singaporeen
dc.contributor.institutionStanford University, Palo Alto, United Statesen
kaust.grant.programAcademic Excellence Alliance (AEA)en
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