Selective refinement and selection of near-native models in protein structure prediction.

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Selective refinement and selection of near-native models in protein structure prediction.

Proteins. 2015 Jul 27;

Authors: Zhang J, Barz B, Zhang J, Xu D, Kosztin I

Abstract
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. This article is protected by copyright. All rights reserved.

PMID: 26214389 [PubMed - as supplied by publisher]