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Selective refinement and selection of near‐native models in protein structure prediction
Author(s) -
Zhang Jiong,
Barz Bogdan,
Zhang Jingfen,
Xu Dong,
Kosztin Ioan
Publication year - 2015
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.24866
Subject(s) - computer science , in silico , ranking (information retrieval) , protein structure prediction , protocol (science) , identification (biology) , selection (genetic algorithm) , computational biology , protein structure , data mining , biological system , artificial intelligence , chemistry , biology , medicine , biochemistry , botany , alternative medicine , pathology , gene
In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near‐native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target‐specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all‐atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best‐refined models are selected with SMDR by testing their relative stability against gradual heating through all‐atom MD simulations. Through extensive testing we have found that Mufold‐MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions. Proteins 2015; 83:1823–1835. © 2015 Wiley Periodicals, Inc.