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Metastable Conformations via successive Perron-Cluster Cluster Analysis of dihedrals

Please always quote using this URN: urn:nbn:de:0297-zib-7074
  • Decomposition of the high dimensional conformational space of bio-molecules into metastable subsets is used for data reduction of long molecular trajectories in order to facilitate chemical analysis and to improve convergence of simulations within these subsets. The metastability is identified by the Perron-cluster cluster analysis of a Markov process that generates the thermodynamic distribution. A necessary prerequisite of this analysis is the discretization of the conformational space. A combinatorial approach via discretization of each degree of freedom will end in the so called ''curse of dimension''. In the following paper we analyze Hybrid Monte Carlo simulations of small, drug-like biomolecules and focus on the dihedral degrees of freedom as indicators of conformational changes. To avoid the ''curse of dimension'', the projection of the underlying Markov operator on each dihedral is analyzed according to its metastability. In each decomposition step of a recursive procedure, those significant dihedrals, which indicate high metastability, are used for further decomposition. The procedure is introduced as part of a hierarchical protocol of simulations at different temperatures. The convergence of simulations within metastable subsets is used as an ''a posteriori'' criterion for a successful identification of metastability. All results are presented with the visualization program AmiraMol.

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Author:Frank Cordes, Marcus Weber, Johannes Schmidt-Ehrenberg
Document Type:ZIB-Report
Tag:Hybrid Monte Carlo; Perron-Cluster Cluster Analysis; curse of dimension; metastability; significant dihedrals
MSC-Classification:62-XX STATISTICS / 62Hxx Multivariate analysis [See also 60Exx] / 62H30 Classification and discrimination; cluster analysis [See also 68T10]
Date of first Publication:2002/11/15
Series (Serial Number):ZIB-Report (02-40)
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