title: Structural quality of unrefined models in protein docking.
abstract: Structural characterization of protein-protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template-based docking techniques that utilize structural similarity between target protein-protein interaction and cocrystallized protein-protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template-free docking. However, the template-based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template-based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template-based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template-based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template-based docking are comparable to those in the free docking, due to the overall higher quality of the template-based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template-based docking predictions as well. Proteins 2016; 85:39-45. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.
authors.forename: Ivan
authors.initials: I
authors.lastname: Anishchenko
authors.affiliation: Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.
authors.forename: Petras J
authors.initials: PJ
authors.lastname: Kundrotas
authors.affiliation: Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047.
authors.forename: Ilya A
authors.initials: IA
authors.lastname: Vakser
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D018360:Crystallography, X-Ray; D062105:Molecular Docking Simulation; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins
keywords: interactome; model refinement; protein modeling; protein recognition; steric clash; structure prediction