Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening

Abstract

The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED’s ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Directory of Useful Decoys-Enhanced dataset and wet-lab tests on Covid-19 3CLpro-inhibitors. ExptGMS improved the active compound enrichment in top-ranked molecules by approximately 20%. Furthermore, ExptGMS identified four active inhibitors of 3CLpro, with the most effective showing an IC₅₀ value of 1.9 µM. We also developed an online database containing experimental ED grids for over 17,000 proteins to facilitate the use of ExptGMS for academic users.

Virtual screening methods for drug discovery typically rely on static structures and lack efficient incorporation of dynamic information exhibited in experimental electron densities. Here, the authors develop an approach utilizing multi-resolution experimental electron density maps to screen docking poses, with the effectiveness demonstrated in both the improvement of active compound enriching exhibited in the test using DUD-E data set and the identification of four inhibitors of Covid-19 3CLpro with IC50 of up to 1.9 μM.