A general and fast scoring function for protein-ligand interactions: A simplified potential approach

A fast, simplified potential-based approach is presented that estimates the protein-ligand binding affinity based on the given 3D structure of a prote in-ligand complex. This general, knowledge-based approach exploits structur al information of known protein-ligand complexes extracted from the Brookha ven Protein Data Bank and converts it into distance-dependent Helmholtz fre e interaction energies of protein-ligand atom pairs (potentials of mean for ce, PMF). The definition of an appropriate reference state and the introduc tion of a correction term accounting for the volume taken by the ligand wer e found to be crucial for deriving the relevant interaction potentials that treat solvation and entropic contributions implicitly. A significant corre lation between experimental binding affinities and computed score was found for sets of diverse protein-ligand complexes and for sets of different lig ands bound to the same target. For 77 protein-ligand complexes taken from t he Brookhaven Protein Data Bank, the calculated score showed a standard dev iation from observed binding affinities of 1.8 log K-i units and an R-2 val ue of 0.61. The best results were obtained for the subset of 16 serine prot ease complexes with a standard deviation of 1.0 log K-i unit and an R-2 val ue of 0.86. A set of 33 inhibitors modeled into a crystal structure of HIV- 1 protease yielded a standard deviation of 0.8 log K-i units from measured inhibition constants and an R-2 value of 0.74. In contrast to empirical sco ring functions that show similar or sometimes better correlation with obser ved binding affinities, our method does not involve deriving specific param eters that fit the observed binding affinities of protein-ligand complexes of a given training set. We compared the performance of the PMF score, Bohm 's score (LUDI), and the SMOG score for eight different test sets of protei n-ligand complexes. It was found that for the majority of test sets the PMF score performs best. The strength of the new approach presented here lies in its generality as no knowledge about measured binding affinities is need ed to derive atomic interaction potentials. The use of the new scoring func tion in docking studies is outlined.