title: DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites.
abstract: Over the past decade, protein-protein interactions have emerged as attractive but challenging targets for therapeutic intervention using small molecules. Due to the relatively flat surfaces that typify protein interaction sites, modern virtual screening tools developed for optimal performance against "traditional" protein targets perform less well when applied instead at protein interaction sites. Previously, we described a docking method specifically catered to the shallow binding modes characteristic of small-molecule inhibitors of protein interaction sites. This method, called DARC (Docking Approach using Ray Casting), operates by comparing the topography of the protein surface when "viewed" from a vantage point inside the protein against the topography of a bound ligand when "viewed" from the same vantage point. Here, we present five key enhancements to DARC. First, we use multiple vantage points to more accurately determine protein-ligand surface complementarity. Second, we describe a new scheme for rapidly determining optimal weights in the DARC scoring function. Third, we incorporate sampling of ligand conformers "on-the-fly" during docking. Fourth, we move beyond simple shape complementarity and introduce a term in the scoring function to capture electrostatic complementarity. Finally, we adjust the control flow in our GPU implementation of DARC to achieve greater speedup of these calculations. At each step of this study, we evaluate the performance of DARC in a "pose recapitulation" experiment: predicting the binding mode of 25 inhibitors each solved in complex with its distinct target protein (a protein interaction site). Whereas the previous version of DARC docked only one of these inhibitors to within 2 Å RMSD of its position in the crystal structure, the newer version achieves this level of accuracy for 12 of the 25 complexes, corresponding to a statistically significant performance improvement (p < 0.001). Collectively then, we find that the five enhancements described here--which together make up DARC 2.0--lead to dramatically improved speed and performance relative to the original DARC method.
authors.affiliation: Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66045, United States of America.
authors.forename: Ragul
authors.initials: R
authors.lastname: Gowthaman
authors.affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles St., Baltimore, MD, 21218, United States of America.
authors.forename: Sergey
authors.initials: S
authors.lastname: Lyskov
authors.affiliation: Center for Computational Biology, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66045, United States of America; Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66045, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Karanicolas
mesh_terms: D062105:Molecular Docking Simulation; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D058996:Quality Improvement; D012984:Software
chemical_list: D011506:Proteins