title: Assessing scoring functions for protein-ligand interactions.
abstract: An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
authors.affiliation: Department of Molecular Biology (TPC6), The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
authors.forename: Philippe
authors.initials: P
authors.lastname: Ferrara
authors.forename: Holger
authors.initials: H
authors.lastname: Gohlke
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Price
authors.forename: Gerhard
authors.initials: G
authors.lastname: Klebe
authors.forename: Charles L
authors.initials: CL
authors.lastname: Brooks
mesh_terms: D001665:Binding Sites; D016333:HIV Protease / Q000737:chemistry; D057927:Hydrophobic and Hydrophilic Interactions; D008024:Ligands; D008958:Models, Molecular; D008968:Molecular Conformation; D011506:Proteins / Q000737:chemistry*; D011522:Protons; D021281:Quantitative Structure-Activity Relationship; D012997:Solvents / Q000737:chemistry; D013816:Thermodynamics; D014357:Trypsin / Q000737:chemistry; D015032:Zinc / Q000737:chemistry
chemical_list: D008024:Ligands; D011506:Proteins; D011522:Protons; D012997:Solvents; D014357:Trypsin; D016333:HIV Protease; D015032:Zinc