Identification, Characterization and Drug Discovery for Novel Target Sites for SARS‐CoV‐2 Proteins

Abstract

The virus SARS‐CoV‐2, the cause of the current COVID‐19 pandemic, is not well understood. It is critical to understand how the viral proteins function and how their function may be modulated. Inhibitors that target these enzymes serve as potential therapeutic interventions against COVID‐19. This work uses artificial intelligence methods developed by us to find sites that other methods may not find and therefore, identify potential exosites, allosteric sites, or other sites of interaction in the structures of viral proteins to serve as new targets for the development of antiviral agents. Large datasets of natural and synthetic compounds are computationally searched for molecules that fit into these alternative sites, and any compounds that fit will be targeted for experimental testing for their ability to inhibit the functions of these viral enzymes. This project uses the unique Partial Order Optimum Likelihood (POOL) machine learning method developed by us to predict multiple types of binding sites in SARS‐CoV‐2 proteins, including catalytic sites, allosteric sites, and other interaction sites. Molecular dynamics simulations are used to generate conformations for ensemble docking. Compounds from large molecular libraries are computationally docked into the predicted sites to identify potentially strong binding ligands. We have identified approximately 10000 potential ligands for more than 50 SARS‐CoV‐2 proteins to date. Candidate ligands to selected SARS‐CoV‐2 proteins are experimentally tested in vitro for binding affinity and the effect of the best‐predicted inhibitors on catalytic activities determined by direct biochemical assays. Compound libraries for the study include selected compounds from the ZINC and Enamine databases; Chemical Service database compounds and COVID‐specific libraries from Enamine and Life Chemicals.