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

Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Research Abstract Details 

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Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Abstract Text:

John D Chodera,Nina Singhal,Vijay S Pande,Ken A Dill,William C Swope,

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 F(s) peptide, and the engineered 12-residue beta-hairpin trpzip2-to assess its ability to generate physically meaningful states and faithful kinetic models.

Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Publishing Authors By Initials

JD Chodera,N Singhal,VS Pande,KA Dill,WC Swope,

For similar natural sciences: chemistry: chemistry, physical: phase transition research abstracts see: natural sciences: chemistry: chemistry, physical: phase transition research

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Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: The Journal of chemical physics

VOLUME: 126

Page Numbers: 155101

Journal Abbreviation:

ISSN: 0021-9606

DAY: 21

MONTH: Apr

YEAR: 2007

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

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LANGUAGE: eng

NlmUniqueID: 375360

Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Keywords Mesh Terms:

KEYWORDS: Phase Transition

MESH TERMS: methods

Chemical & Substance for Abstract: Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. Information

Substance Name: Macromolecular Substances

Registry Number: 0

Grant and Affiliation Information for Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics.

AFFILIATION: Graduate Group in Biophysics, University of California-San Francisco, San Francisco, CA 94143, USA.

Country: United States

AGENCY: United States NIGMS

GRANT: GM 34993

ACRONYM: GM

MEDLINETA: J Chem Phys

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