An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state.
Structure prediction on a genomic scale requires a simplified energy function that can efficiently sample the conformational space of polypeptide chains. A good energy function at minimum should discriminate native structures against decoys. Here, we show that a recently developed, residue-specific, all-atom knowledge-based potential (167 atomic types) based on distance-scaled, finite ideal-gas reference state (DFIRE-all-atom) can be substantially simplified to 20 residue types located at side-chain center of mass (DFIRE-SCM) without a significant change in its capability of structure discrimination. Using 96 standard multiple decoy sets, we show that there is only a small reduction (from 80% to 78%) in success rate of ranking native structures as the top 1. The success rate is higher than two previously developed, all-atom distance-dependent statistical pair potentials. Applied to structure selections of 21 docking decoys without modification, the DFIRE-SCM potential is 29% more successful in recognizing native complex structures than an all-atom statistical potential trained by a database of dimeric interfaces. The potential also achieves 92% accuracy in distinguishing true dimeric interfaces from artificial crystal interfaces. In addition, the DFIRE potential with the C(alpha) positions as the interaction centers recognizes 123 native structures out of a comprehensive 125-protein TOUCHSTONE decoy set in which each protein has 24,000 decoys with only C(alpha) positions. Furthermore, the performance by DFIRE-SCM on newly established 25 monomeric and 31 docking Rosetta-decoy sets is comparable to (or better than in the case of monomeric decoy sets) that of a recently developed, all-atom Rosetta energy function enhanced with an orientation-dependent hydrogen bonding potential.
Zhang
Chi
C
Howard Hughes Medical Institute Center for Single Molecule Biophysics, SUNY Buffalo, 124 Sherman Hall, Buffalo, NY 14214, USA.
Liu
Song
S
Zhou
Hongyi
H
Zhou
Yaoqi
Y
D000596:Amino Acids / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D023281:Genomics; D008956:Models, Chemical; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics; D012680:Sensitivity and Specificity; D013816:Thermodynamics
D000596:Amino Acids; D011506:Proteins