A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

Abstract

Halogen bonding, a non-covalent interaction between the halogen σ-hole and Lewis bases, could not be properly characterized by majority of current scoring functions. In this study, a knowledge-based halogen bonding scoring function, termed XBPMF, was developed by an iterative method for predicting protein-ligand interactions. Three sets of pairwise potentials were derived from two training sets of protein-ligand complexes from the Protein Data Bank. It was found that two-dimensional pairwise potentials could characterize appropriately the distance and angle profiles of halogen bonding, which is superior to one-dimensional pairwise potentials. With comparison to six widely used scoring functions, XBPMF was evaluated to have moderate power for predicting protein-ligand interactions in terms of "docking power", "ranking power" and "scoring power". Especially, it has a rather satisfactory performance for the systems with typical halogen bonds. To the best of our knowledge, XBPMF is the first halogen bonding scoring function that is not dependent on any dummy atom, and is practical for high-throughput virtual screening. Therefore, this scoring function should be useful for the study and application of halogen bonding interactions like molecular docking and lead optimization.