title: A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes.
abstract: We developed a knowledge-based statistical energy function for protein-ligand, protein-protein, and protein-DNA complexes by using 19 atom types and a distance-scale finite ideal-gas reference (DFIRE) state. The correlation coefficients between experimentally measured protein-ligand binding affinities and those predicted by the DFIRE energy function are around 0.63 for one training set and two testing sets. The energy function also makes highly accurate predictions of binding affinities of protein-protein and protein-DNA complexes. Correlation coefficients between theoretical and experimental results are 0.73 for 82 protein-protein (peptide) complexes and 0.83 for 45 protein-DNA complexes, despite the fact that the structures of protein-protein (peptide) and protein-DNA complexes were not used in training the energy function. The results of the DFIRE energy function on protein-ligand complexes are compared to the published results of 12 other scoring functions generated from either physical-based, knowledge-based, or empirical methods. They include AutoDock, X-Score, DrugScore, four scoring functions in Cerius 2 (LigScore, PLP, PMF, and LUDI), four scoring functions in SYBYL (F-Score, G-Score, D-Score, and ChemScore), and BLEEP. While the DFIRE energy function is only moderately successful in ranking native or near native conformations, it yields the strongest correlation between theoretical and experimental binding affinities of the testing sets and between rmsd values and energy scores of docking decoys in a benchmark of 100 protein-ligand complexes. The parameters and the program of the all-atom DFIRE energy function are freely available for academic users at http://theory.med.buffalo.edu.
authors.affiliation: Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology & Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, New York 14214, USA.
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
authors.forename: Song
authors.initials: S
authors.lastname: Liu
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Zhu
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004247:DNA / Q000737:chemistry*; D008024:Ligands; D015233:Models, Statistical; D008968:Molecular Conformation; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D008024:Ligands; D011506:Proteins; D004247:DNA