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Protein structure prediction using residue‐ and fragment‐environment potentials in CASP 11
Author(s) -
Kim Hyungrae,
Kihara Daisuke
Publication year - 2016
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.24920
Subject(s) - casp , decoy , protein structure prediction , computer science , protein structure , statistical potential , score , pipeline (software) , biological system , computational biology , artificial intelligence , machine learning , chemistry , biology , biochemistry , receptor , programming language
An accurate scoring function that can select near‐native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse‐grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluates the native‐likeness of local structural environment of residues in a structure decoy considering positions and the depth of side‐chains of spatially neighboring residues. We also introduced a helix interaction potential as an additional scoring function for selecting decoys. The best models selected by PRESCO and the helix interaction potential underwent structure refinement, which includes side‐chain modeling and relaxation with a short molecular dynamics simulation. Our protocol was successful, achieving the top rank in the free modeling category with a significant margin of the accumulated Z‐score to the subsequent groups when the top 1 models were considered. Proteins 2016; 84(Suppl 1):105–117. © 2015 Wiley Periodicals, Inc.