Computational screening and automatic filtering for the discovery of novel inhibitors of TMPRSS2, a type II transmembrane serine protease

Abstract

Transmembrane Serine Protease 2 (TMPRSS2) is a membrane protein of the type II serine protease family of enzymes implied in epithelial homeostasis. It is involved in several diseases, notably prostate cancer and SARS-CoV-2 infections. Over the years, only a few tested TMPRSS2 inhibitors showed consistent results. This prompted us to select it as target of structure-based virtual screening, to search for novel inhibitors among a library of 475,770 small molecules. Two sets of TMPRSS2 structures were selected, one taken from molecular dynamics simulations, the other from recently solved X-ray crystallographic structures. We designed a workflow to filter docking results in a reproducible way, allowing for a faster and more reliable selection. The program uses four metrics: the pose consistency of the ligand, docking score, number of interactions with key protein residues, and cluster analysis. This led to the selection and visual inspection of two sets of 500 compounds, which yielded 10 reasonable hit candidates.