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Efficient identification of near‐native conformations in ab initio protein structure prediction using structural profiles
Author(s) -
Wolff Katrin,
Vendruscolo Michele,
Porto Markus
Publication year - 2009
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.22533
Subject(s) - pairwise comparison , ab initio , cluster analysis , protein structure prediction , selection (genetic algorithm) , computer science , root mean square , identification (biology) , protein structure , range (aeronautics) , force field (fiction) , resolution (logic) , biological system , data mining , algorithm , artificial intelligence , chemistry , physics , materials science , biology , biochemistry , organic chemistry , quantum mechanics , composite material , botany
One of the major bottlenecks in many ab initio protein structure prediction methods is currently the selection of a small number of candidate structures for high‐resolution refinement from large sets of low‐resolution decoys. This step often includes a scoring by low‐resolution energy functions and a clustering of conformations by their pairwise root mean square deviations (RMSDs). As an efficient selection is crucial to reduce the overall computational cost of the predictions, any improvement in this direction can increase the overall performance of the predictions and the range of protein structures that can be predicted. We show here that the use of structural profiles, which can be predicted with good accuracy from the amino acid sequences of proteins, provides an efficient means to identify good candidate structures. Proteins 2010. © 2009 Wiley‐Liss, Inc.