000222154 001__ 222154
000222154 005__ 20180317094436.0
000222154 0247_ $$2doi$$a10.1002/prot.25079
000222154 022__ $$a0887-3585
000222154 02470 $$2ISI$$a000382812900012
000222154 037__ $$aARTICLE
000222154 245__ $$aProtein-protein structure prediction by scoring molecular dynamics trajectories of putative poses
000222154 260__ $$aHoboken$$bWiley-Blackwell$$c2016
000222154 269__ $$a2016
000222154 300__ $$a9
000222154 336__ $$aJournal Articles
000222154 520__ $$aThe prediction of protein-protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state-of-the-art scoring functions (BACH-SixthSense, PIE/PISA and Rosetta) in discriminating finite-temperature ensembles of structures corresponding to the native state and to non-native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH-SixthSense and PIE/PISA perform better in distinguishing near-native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312-1320. (c) 2016 Wiley Periodicals, Inc.
000222154 6531_ $$ascoring function
000222154 6531_ $$aprotein-protein interaction
000222154 6531_ $$arefinement
000222154 6531_ $$acomparative study
000222154 6531_ $$arigid docking
000222154 6531_ $$aMD
000222154 6531_ $$aBACH-SixthSense
000222154 6531_ $$aRosetta
000222154 6531_ $$aPIE
000222154 6531_ $$aPISA
000222154 6531_ $$anative discrimination
000222154 700__ $$aSarti, Edoardo$$uSISSA, Via Bonomea 265, I-34136 Trieste, Italy
000222154 700__ $$aGladich, Ivan$$uSISSA, Via Bonomea 265, I-34136 Trieste, Italy
000222154 700__ $$aZamuner, Stefano$$uSISSA, Via Bonomea 265, I-34136 Trieste, Italy
000222154 700__ $$0249970$$aCorreia, Bruno E.$$g253103
000222154 700__ $$aLaio, Alessandro$$uSISSA, Via Bonomea 265, I-34136 Trieste, Italy
000222154 773__ $$j84$$k9$$q1312-1320$$tProteins-Structure Function And Bioinformatics
000222154 909CO $$ooai:infoscience.tind.io:222154$$particle$$pSTI
000222154 909C0 $$0252545$$pLPDI$$xU13000
000222154 937__ $$aEPFL-ARTICLE-222154
000222154 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000222154 980__ $$aARTICLE