Selective refinement and selection of near‐native models in protein structure prediction |
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| Authors: | Jiong Zhang Bogdan Barz Jingfen Zhang Dong Xu Ioan Kosztin |
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| Affiliation: | 1. Department of Physics and Astronomy, University of Missouri, Columbia, Missouri;2. Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri |
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| 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. Proteins 2015; 83:1823–1835. © 2015 Wiley Periodicals, Inc. |
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| Keywords: | protein structure prediction protein structure quality assessment MDR ranking model refinement CASP |
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