The use of protein-ligand interaction information has been reported to improve and optimize the docking results in virtual screening experiments. Here we propose an improved weighted-residue profile based method to profile the protein-ligand interactions based on the available dataset of known actives and utilize this weighted residue profile information, together with the scoring function, as selection criteria to increase hit rates in virtual screening experiments. The generated fingerprint data is not directly based on the protein-ligand complexes but taken from the available interaction data derived from the docking results. The ability of the method to recover the active compounds was tested on two data sets of a compound library designed for antagonists of the A2A receptor. The results show better hits enrichments by using the weighted-residue based profiles of protein-ligand interactions as compared to the normal binding energy based scoring schemes of the two docking programs.
Keywords: Adenosine receptor A2A antagonists; Docking; GPCR; Protein-ligand interaction fingerprints; Virtual screening.
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