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Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states
Molecular dynamics simulation generates large quantities of data that must be interpreted using physically meaningful analysis. A common approach is to describe the system dynamics in terms of transit...

Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics

J. Chem. Phys. 126, 155101 (2007); doi:10.1063/1.2714538

Published 19 April 2007

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John D. Chodera
Graduate Group in Biophysics, University of California, San Francisco, California 94143

Nina Singhal
Department of Computer Science, Stanford University, Stanford, California 94305

Vijay S. Pande
Department of Chemistry, Stanford University, Stanford, California 94305

Ken A. Dill
Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143

William C. Swope
IBM Almaden Research Center, 650 Harry Road, San Jose, California 95120
To meet the challenge of modeling the conformational dynamics of biological macromolecules over long time scales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the time scales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. The authors apply this method to three peptides in explicit solvent—terminally blocked alanine, the 21-residue helical Fs peptide, and the engineered 12-residue beta-hairpin trpzip2—to assess its ability to generate physically meaningful states and faithful kinetic models. ©2007 American Institute of Physics
History: Received 21 November 2006; accepted 13 February 2007; published 19 April 2007
Permalink: http://link.aip.org/link/?JCPSA6/126/155101/1
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EDITORIALLY RELATED

  1. Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states
    Frank Noé et al.
    J. Chem. Phys. 126, 155102 (2007)

KEYWORDS and PACS

Keywords
PACS
  • 36.20.Ey
    Macromolecular conformation (statistics and dynamics)
  • 36.20.Hb
    Macromolecular configuration (bonds, dimensions)
  • 87.15.He
    Biomolecular dynamics and conformational changes
  • 87.15.Aa
    Theory and modeling in molecular biophysics; computer simulation
  • 87.15.Rn
    Biochemical reactions and kinetics; polymerization
  • YEAR: 2007

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PUBLICATION DATA

ISSN:
0021-9606 (print)   1089-7690 (online)
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