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Extracting representative structures from protein conformational ensembles
Author(s) -
Perez Alberto,
Roy Arijit,
Kasavajhala Koushik,
Wagaman Amy,
Dill Ken A.,
MacCallum Justin L.
Publication year - 2014
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24633
Subject(s) - cluster (spacecraft) , cluster analysis , conformational ensembles , computer science , representation (politics) , limit (mathematics) , protein structure , data mining , artificial intelligence , physics , mathematics , mathematical analysis , nuclear magnetic resonance , politics , political science , law , programming language
A large number of methods generate conformational ensembles of biomolecules. Often one structure is selected to be representative of the whole ensemble, usually by clustering and selecting the structure closest to the center of the most populated cluster. We find that this structure is not necessarily the best representation of the cluster and present here two computationally inexpensive averaging protocols that can systematically provide better representations of the system, which can be more directly compared with structures from X‐ray crystallography. In practice, systematic errors in the generated conformational ensembles appear to limit the maximum improvement of averaging methods. Proteins 2014; 82:2671–2680. © 2014 Wiley Periodicals, Inc.