The life cycle of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves a number of viral proteins and enzymes required for infectivity and replication. Inhibitors that target these enzymes serve as potential therapeutic interventions against coronavirus disease 2019 (COVID-19). With this award, the Chemistry of Life Processes program in the Chemistry Division is supporting the research of Drs. Mary Jo Ondrechen and Penny J. Beuning from Northeastern University to apply computational methods to identify sites in SARS-CoV-2 proteins that would be good targets for binding inhibitors. The project uses artificial intelligence methods developed at Northeastern University to identify pockets and crevices in the structures of viral proteins that may 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 experimentally tested for their ability to inhibit the functions of these viral enzymes. The project provides training in computational chemistry and biochemical analysis to graduate students and postdoctoral associates.
This project uses the unique Partial Order Optimum Likelihood (POOL) machine learning (ML) method developed by Dr. Ondrechen’s group to predict multiple types of binding sites in SARS-CoV-2 proteins, including catalytic sites, allosteric sites, and other interaction sites. The goals of this project are to apply the POOL-ML method to identify the binding sites on viral pathogen SARS-CoV-2 proteins using the three-dimensional protein structures as input. Molecular dynamics simulations are used to generate conformations for ensemble docking. Compounds from the large molecular databases are computationally docked into the predicted sites to identify potentially strong binding ligands. Candidate ligands to selected SARS-CoV-2 proteins, including the main protease and 2ʹ-O-ribose RNA methyltransferase, are experimentally tested in vitro for binding affinity and the effect of the best predicted inhibitors on catalytic activities determined by direct biochemical assays. All the SARS-CoV-2 protein structures in the Protein Data Bank (PDB) are studied. Compound libraries for the study include: a) selected 2600+ compounds from the ZINC and Enamine databases that are already being manufactured; b) a library of 20,000+ compounds found in foods that the team recently gained access to; these potentially hold some special advantages, including ready availability in the public domain and low cost; and c) the March 2020 open access CAS (American Chemical Society) database of 50,000 compounds with known or potential anti-viral activity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
