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From simulation data to conformational ensembles: Structure and dynamics‐based methods
Author(s) -
Huisinga Wilhelm,
Best Christoph,
Roitzsch Rainer,
Schütte Christof,
Cordes Frank
Publication year - 1999
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/(sici)1096-987x(199912)20:16<1760::aid-jcc8>3.0.co;2-2
Subject(s) - degrees of freedom (physics and chemistry) , molecular dynamics , flexibility (engineering) , statistical physics , computer science , conformational ensembles , property (philosophy) , monte carlo method , algorithm , biological system , computational chemistry , chemistry , physics , mathematics , philosophy , statistics , epistemology , quantum mechanics , biology
Statistical methods for analyzing large data sets of molecular configurations within the chemical concept of molecular conformations are described. The strategies are based on dependencies between configurations of a molecular ensemble; the article concentrates on dependencies induced by (a) correlations between the molecular degrees of freedom, (b) geometrical similarities of configurations, and (c) dynamical relations between subsets of configurations. The statistical technique realizing aspect (a) is based on an approach suggested by Amadei et al. (Proteins 1993, 17). It allows identification of essential degrees of freedom of a molecular system, and is extended to determine single configurations as representatives for the crucial features related to these essential degrees of freedom. Aspects (b) and (c) are based on statistical cluster methods. They lead to a decomposition of the available simulation data into conformational ensembles or subsets, with the property that all configurations in one of these subsets share a common chemical property. In contrast to the restriction to single representative conformations, conformational ensembles include information about, for examples, structural flexibility or dynamical connectivity. The conceptual similarities and differences of the three approaches are discussed in detail, and are illustrated by application to simulation data originating from a hybrid Monte Carlo sampling of a triribonucleotide.© 1999 John Wiley & Sons, Inc. J Comput Chem 20: 1760–1774, 1999