Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes.

Abstract

Computational methods to calculate binding affinity in protein-ligand interaction are of immense interest because of obvious practical applications in structure-based drug design. Scoring functions attempt to calculate the variation in binding affinity of ligands-inhibitors bound to protein targets at various levels of theory. In this study we use semiempirical quantum mechanics to design a scoring function that can calculate the electrostatic interactions and solvation free energy expected during complexation. This physically based approach has the ability to capture binding affinity trends in a diverse range of protein-ligand complexes. We also show the predictive power of this scoring function within protein targets and its ability to score ligand poses docked to a protein target. We also demonstrate the ability of this scoring function to discriminate between native and decoy poses and highlight the crucial role played by electrostatic interactions in molecular recognition. Finally we compare the performance of our scoring function with other available scoring functions in the literature.