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Title: Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of meta-stable states

Abstract

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 transitions between coarse partitions of conformational space. In contrast to previous work that partitions the space according to geometric proximity, the authors examine here clustering based on kinetics, merging configurational microstates together so as to identify long-lived, i.e., dynamically metastable, states. As test systems microsecond molecular dynamics simulations of the polyalanines Ala{sub 8} and Ala{sub 12} are analyzed. Both systems clearly exhibit metastability, with some kinetically distinct metastable states being geometrically very similar. Using the backbone torsion rotamer pattern to define the microstates, a definition is obtained of metastable states whose lifetimes considerably exceed the memory associated with interstate dynamics, thus allowing the kinetics to be described by a Markov model. This model is shown to be valid by comparison of its predictions with the kinetics obtained directly from the molecular dynamics simulations. In contrast, clustering based on the hydrogen-bonding pattern fails to identify long-lived metastable states or a reliable Markov model. Finally, an approach is proposed to generate a hierarchical model of networks, each having a different number of metastable states.more » The model hierarchy yields a qualitative understanding of the multiple time and length scales in the dynamics of biomolecules.« less

Authors:
 [1];  [2];  [1]; ORCiD logo [3]
  1. Heidelberg University, Germany
  2. Free University of Berlin, Germany
  3. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
932198
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: The Journal of Chemical Physics; Journal Volume: 126; Journal Issue: 15
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; MOLECULAR DYNAMICS METHOD; ALANINES; ORGANIC POLYMERS; KINETICS; METASTABLE STATES; MATHEMATICAL MODELS; BIOLOGICAL MATERIALS

Citation Formats

Noe, F, Horenko, Illia, Schuette, C., and Smith, Jeremy C. Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of meta-stable states. United States: N. p., 2007. Web. doi:10.1063/1.2714539.
Noe, F, Horenko, Illia, Schuette, C., & Smith, Jeremy C. Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of meta-stable states. United States. doi:10.1063/1.2714539.
Noe, F, Horenko, Illia, Schuette, C., and Smith, Jeremy C. Sun . "Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of meta-stable states". United States. doi:10.1063/1.2714539.
@article{osti_932198,
title = {Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of meta-stable states},
author = {Noe, F and Horenko, Illia and Schuette, C. and Smith, Jeremy C.},
abstractNote = {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 transitions between coarse partitions of conformational space. In contrast to previous work that partitions the space according to geometric proximity, the authors examine here clustering based on kinetics, merging configurational microstates together so as to identify long-lived, i.e., dynamically metastable, states. As test systems microsecond molecular dynamics simulations of the polyalanines Ala{sub 8} and Ala{sub 12} are analyzed. Both systems clearly exhibit metastability, with some kinetically distinct metastable states being geometrically very similar. Using the backbone torsion rotamer pattern to define the microstates, a definition is obtained of metastable states whose lifetimes considerably exceed the memory associated with interstate dynamics, thus allowing the kinetics to be described by a Markov model. This model is shown to be valid by comparison of its predictions with the kinetics obtained directly from the molecular dynamics simulations. In contrast, clustering based on the hydrogen-bonding pattern fails to identify long-lived metastable states or a reliable Markov model. Finally, an approach is proposed to generate a hierarchical model of networks, each having a different number of metastable states. The model hierarchy yields a qualitative understanding of the multiple time and length scales in the dynamics of biomolecules.},
doi = {10.1063/1.2714539},
journal = {The Journal of Chemical Physics},
number = 15,
volume = 126,
place = {United States},
year = {Sun Apr 01 00:00:00 EDT 2007},
month = {Sun Apr 01 00:00:00 EDT 2007}
}