title: Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment.
abstract: The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh , Pittsburgh, Pennsylvania 15260, United States.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Baumgartner
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh , Pittsburgh, Pennsylvania 15260, United States.
authors.forename: Carlos J
authors.initials: CJ
authors.lastname: Camacho
mesh_terms: D000465:Algorithms; D015195:Drug Design; D015951:Factor Xa / Q000737:chemistry / Q000378:metabolism; D008024:Ligands; D062105:Molecular Docking Simulation; D004364:Pharmaceutical Preparations / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D013329:Structure-Activity Relationship; D000072377:Syk Kinase / Q000737:chemistry / Q000378:metabolism; D012359:tRNA Methyltransferases / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins; D012359:tRNA Methyltransferases; D000072377:Syk Kinase; D015951:Factor Xa