title: Native fold and docking pose discrimination by the same residue-based scoring function.
abstract: Structure prediction and quality assessment are crucial steps in modeling native protein conformations. Statistical potentials are widely used in related algorithms, with different parametrizations typically developed for different contexts such as folding protein monomers or docking protein complexes. Here, we describe BACH-SixthSense, a single residue-based statistical potential that can be successfully employed in both contexts. BACH-SixthSense shares the same approach as BACH, a knowledge-based potential originally developed to score monomeric protein structures. A term that penalizes steric clashes as well as the distinction between polar and apolar sidechain-sidechain contacts are crucial novel features of BACH-SixthSense. The performance of BACH-SixthSense in discriminating correctly the native structure among a competing set of decoys is significantly higher than other state-of-the-art scoring functions, that were specifically trained for a single context, for both monomeric proteins (QMEAN, Rosetta, RF_CB_SRS_OD, benchmarked on CASP targets) and protein dimers (IRAD, Rosetta, PIE*PISA, HADDOCK, FireDock, benchmarked on 14 CAPRI targets). The performance of BACH-SixthSense in recognizing near-native docking poses within CAPRI decoy sets is good as well.
authors.affiliation: SISSA, Physics Faculty, Trieste, I-34136, Italy.
authors.forename: Edoardo
authors.initials: E
authors.lastname: Sarti
authors.forename: Daniele
authors.initials: D
authors.lastname: Granata
authors.forename: Flavio
authors.initials: F
authors.lastname: Seno
authors.forename: Antonio
authors.initials: A
authors.lastname: Trovato
authors.forename: Alessandro
authors.initials: A
authors.lastname: Laio
mesh_terms: D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D011485:Protein Binding; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D011506:Proteins
keywords: knowledge based potentials; molecular recognition; protein-protein interaction; quality assessment; structure prediction