@misc{GalliatDeuflhardRoitzschetal., author = {Galliat, Tobias and Deuflhard, Peter and Roitzsch, Rainer and Cordes, Frank}, title = {Automatic Identification of Metastable Conformations via Self-Organized Neural Networks}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6197}, number = {00-51}, abstract = {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.}, language = {en} } @misc{HuisingaBestCordesetal., author = {Huisinga, Wilhelm and Best, Christoph and Cordes, Frank and Roitzsch, Rainer and Sch{\"u}tte, Christof}, title = {From Simulation Data to Conformational Ensembles: Structure and Dynamics based Methods}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-3797}, number = {SC-98-36}, 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 {\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.}, language = {en} } @inproceedings{GalliatDeuflhardRoitzschetal.2002, author = {Galliat, Tobias and Deuflhard, Peter and Roitzsch, Rainer and Cordes, Frank}, title = {Automatic identification of metastable conformations via self-organized neural networks}, series = {Proceedings of the 3rd International Workshop on Algorithms for Macromolecular Modelling}, booktitle = {Proceedings of the 3rd International Workshop on Algorithms for Macromolecular Modelling}, year = {2002}, language = {en} } @inproceedings{GalliatDeuflhardRoitzschetal.2002, author = {Galliat, Tobias and Deuflhard, Peter and Roitzsch, Rainer and Cordes, Frank}, title = {Automatic Identification of Metastable Conformations via Self-Organized Neural Networks}, series = {Computational Methods for Macromolecules}, booktitle = {Computational Methods for Macromolecules}, number = {24}, editor = {Schlick, T. and Gan, H.}, publisher = {Springer}, year = {2002}, language = {en} } @article{HuisingaBestRoitzschetal.1999, author = {Huisinga, Wilhelm and Best, Christoph and Roitzsch, Rainer and Sch{\"u}tte, Christof and Cordes, Frank}, title = {From Simulation Data to Conformational Ensembles}, series = {J. Comp. Chem.}, volume = {20}, journal = {J. Comp. Chem.}, number = {16}, pages = {1760 -- 1774}, year = {1999}, language = {en} } @article{HuisingaBestCordesetal.1999, author = {Huisinga, Wilhelm and Best, Christoph and Cordes, Frank and Roitzsch, Rainer and Sch{\"u}tte, Christof}, title = {Identification of Molecular Conformations via Statistical Analysis of Simulation Data}, series = {Comp. Chem.}, volume = {20}, journal = {Comp. Chem.}, pages = {1760 -- 1774}, year = {1999}, language = {en} } @article{HuisingaBestRoitzschetal.1999, author = {Huisinga, Wilhelm and Best, Christoph and Roitzsch, Rainer and Sch{\"u}tte, Christof and Cordes, Frank}, title = {From Simulation Data to Conformational Ensembles}, series = {J. Comp. Chem.}, volume = {20}, journal = {J. Comp. Chem.}, number = {16}, doi = {10.1002/(SICI)1096-987X(199912)20:16<1760::AID-JCC8>3.0.CO;2-2}, pages = {1760 -- 1774}, year = {1999}, language = {en} }