Zhang, Jiong; Barz, Bogdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan
Selective refinement and selection of near-native models in protein structure prediction
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 83:1823-1835, OCT 2015

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. (c) 2015 Wiley Periodicals, Inc.

DOI:10.1002/prot.24866

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