SC 98-36 Wilhelm Huisinga, Christoph Best, Frank Cordes, Rainer Roitzsch, Christof Schütte: From Simualtion Data to Conformational Ensembles: Structure
and Dynamics based Methods Appeared in: J. Comp. Chemistry 20 (1999) pp. 1760-1774
Abstract: 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 induces 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, 17 (1993)). It allows to identify essential degrees of
freedom of a molecular system and is extended in order 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,
e.g., 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.
Keywords: conformational ensemble,
cluster method,
structural and dynamical similarity,
representative,
conformation,
essential degrees of freedom,
transition states,
cluster analysis,
feature extraction
MSC: 65U05, 62H30