The tendency of docking scoring functions to generate crystal close conformations of ligands bound to protein structures face limitations in not reproducing the exact crystal intermolecular contacts in dock poses. Intermolecular H bond contacts enumerated at the protein-docked ligand interface can be used to train scoring models and improve virtual screening performance. There is a need to incorporate additional knowledge of protein-ligand H bond contacts in extension to crystal contacts from docking solutions within the reproducibility efficiency of the docking program. A computational approach PLHINT (Protein-ligand H bond interaction pattern) is presented here which extracts intermolecular H bond interactions from native-like docked ligand poses, transform into the scoring scheme and apply over the virtual screening results of database molecules. The basic premise of the PLHINT approach is to score the most observed H bond patterns with the high score to achieve high recovery rates. Tested on ten diverse DUD-E benchmark datasets, the approach has demonstrated better overall performance and ligand enrichment competency over virtual screening results generated by three genetic algorithm-based docking programs viz. AutoDock Vina, FlexAID and PLANTS. Furthermore, the approach has successfully recovered the poor and random virtual screening results with better enrichments.
Keywords: AutoDock vina; Crystal contacts; H bond; Ligand enrichment; Protein-ligand interaction; Virtual screening.
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