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Exploring protein energy landscapes with hierarchical clustering
Author(s) -
Gront Dominik,
Hansmann Ulrich H. E.,
Kolinski Andrzej
Publication year - 2005
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.20741
Subject(s) - maxima and minima , hierarchical clustering , cluster analysis , energy landscape , energy (signal processing) , computer science , sampling (signal processing) , biological system , statistical physics , data mining , chemistry , physics , artificial intelligence , mathematics , biology , statistics , mathematical analysis , biochemistry , filter (signal processing) , computer vision
In this work we present a new method for investigating local energy minima on a protein energy landscape. The Cα(CA), Cβ(B), and the center of mass of the side chain (CABS) method was employed for generating protein models, but any other method could be used instead. Cα traces from an ensemble of models are hierarchical clustered with the hierarchical clustering of protein models (HCPM) method. The efficiency of this method for sampling and analyzing energy landscapes is shown. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005