HotLig: a molecular surface-directed approach to scoring protein-ligand interactions

Accurate prediction of ligand-binding poses is crucial for understanding molecular interactions and is very important for drug discovery, structural design, and optimization. In this study, we developed a novel scoring program, HotLig, which applies the Connolly surface of a protein to calculate hydrophobic interaction and paired pharmacophore interactions with ligands. In addition to molecular surface distance, ligand-contacting areas and hydrogen-bond angles were also introduced to the scoring functions in HotLig. Four individual energy scoring functions for H-bonds, ionic pairs, metal coordination, and hydrophobic effects were derived from 600 protein-ligand complexes, and then, their weighting factors were optimized through an interaction-characterized training set. Success rates of ligand-binding-pose predictions (with a root mean squared deviation of ≤2 Å) for the Wang, GOLD, and Cheng data sets were respectively validated to be 91.0%, 87.0%, and 85.6%. HotLig was found to possess equally good predictive powers for the hydrophilic (88.6%) and hydrophobic subsets (87.5%), and the success rate for the mixed subset was as high as 96.9%. The Spearman correlation coefficients were as good as 0.609 to 0.668, which indicates HotLig also has satisfactory predictive power for binding affinities. These results suggested that the HotLig can analyze diverse ligands, including peptides, and is expected to be a powerful tool for drug design and discovery.