TY - GEN A1 - Galliat, Tobias A1 - Deuflhard, Peter A1 - Roitzsch, Rainer A1 - Cordes, Frank T1 - Automatic Identification of Metastable Conformations via Self-Organized Neural Networks N2 - As has been shown recently, the identification of metastable chemical conformations leads to a Perron cluster eigenvalue problem for a reversible Markov operator. Naive discretization of this operator would suffer from combinatorial explosion. As a first remedy, a pre-identification of essential degrees of freedom out of the set of torsion angles had been applied up to now. The present paper suggests a different approach based on neural networks: its idea is to discretize the Markov operator via self-organizing (box) maps. The thus obtained box discretization then serves as a prerequisite for the subsequent Perron cluster analysis. Moreover, this approach also permits exploitation of additional structure within embedded simulations. As it turns out, the new method is fully automatic and efficient also in the treatment of biomolecules. This is exemplified by numerical results. T3 - ZIB-Report - 00-51 KW - biochemical conformations KW - cluster analysis KW - Molecular Dynamics KW - Monte-Carlo methods KW - operator discretization KW - Perron cluster Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-6197 ER - TY - GEN A1 - Huisinga, Wilhelm A1 - Best, Christoph A1 - Cordes, Frank A1 - Roitzsch, Rainer A1 - Schütte, Christof T1 - From Simulation Data to Conformational Ensembles: Structure and Dynamics based Methods N2 - 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 {\sc 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 {\em conformational ensembles} or {\em 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. T3 - ZIB-Report - SC-98-36 KW - conformational ensemble KW - cluster method KW - structural and dynamical similarity KW - representative KW - conformation KW - essential degrees of freedom KW - transi Y1 - 1998 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-3797 ER - TY - CHAP A1 - Galliat, Tobias A1 - Deuflhard, Peter A1 - Roitzsch, Rainer A1 - Cordes, Frank T1 - Automatic identification of metastable conformations via self-organized neural networks T2 - Proceedings of the 3rd International Workshop on Algorithms for Macromolecular Modelling Y1 - 2002 ER - TY - CHAP A1 - Galliat, Tobias A1 - Deuflhard, Peter A1 - Roitzsch, Rainer A1 - Cordes, Frank ED - Schlick, T. ED - Gan, H. T1 - Automatic Identification of Metastable Conformations via Self-Organized Neural Networks T2 - Computational Methods for Macromolecules Y1 - 2002 UR - http://www.zib.de/PaperWeb/abstracts/ZR-00-51 IS - 24 PB - Springer ER - TY - JOUR A1 - Huisinga, Wilhelm A1 - Best, Christoph A1 - Roitzsch, Rainer A1 - Schütte, Christof A1 - Cordes, Frank T1 - From Simulation Data to Conformational Ensembles JF - J. Comp. Chem. Y1 - 1999 VL - 20 IS - 16 SP - 1760 EP - 1774 ER - TY - JOUR A1 - Huisinga, Wilhelm A1 - Best, Christoph A1 - Cordes, Frank A1 - Roitzsch, Rainer A1 - Schütte, Christof T1 - Identification of Molecular Conformations via Statistical Analysis of Simulation Data JF - Comp. Chem. Y1 - 1999 VL - 20 SP - 1760 EP - 1774 ER - TY - JOUR A1 - Huisinga, Wilhelm A1 - Best, Christoph A1 - Roitzsch, Rainer A1 - Schütte, Christof A1 - Cordes, Frank T1 - From Simulation Data to Conformational Ensembles JF - J. Comp. Chem. Y1 - 1999 UR - http://publications.imp.fu-berlin.de/92/ U6 - https://doi.org/10.1002/(SICI)1096-987X(199912)20:16<1760::AID-JCC8>3.0.CO;2-2 VL - 20 IS - 16 SP - 1760 EP - 1774 ER -