Developing novel approaches to improve binding energy estimation and virtual screening: a PARP case study.

Poly-(ADP-ribose)-polymerase (PARP) is a promising anti-cancer target as it plays a crucial role in the cellular reparation and survival mechanisms. However, the development of a robust and cost effective experimental technique to screen PARP inhibitors is still a scientific challenge owing to the difficulties in quantitative detection of the enzyme activity. In this work we demonstrate that the computational chemistry tools including molecular docking and scoring can perform on par with the experimental studies in assessing binding constants and in the recovery of active compounds in virtual screening. Using the recently introduced Lead Finder software we were able to dock a set of 142 well characterized PARP inhibitors and obtain a good correlation between the calculated and experimentally measured binding energies with the rmsd of 1.67 kcal mol(-1). Additionally, fine-tuning of the energy scaling coefficients within the Lead Finder scoring function has further decreased rmsd to the value of 0.88 kcal mol(-1). Moreover, we were able to reproduce the selectivity of ligand binding between the two isoforms of the enzyme-PARP1 and PARP2-suggesting that the Lead Finder software can be used to design isoform-selective inhibitors of PARP. An impressive enrichment was obtained in the virtual screening experiment, in which the mentioned set of PARP inhibitors was mixed with a commercial library of 300,000 compounds. We also demonstrate that the virtual screening performance can be significantly improved by an additional structural filtration of the docked ligand poses through detection of the crucial hydrogen bonding interactions with the enzyme.