The Third CACHE Challenge – Finding Ligands Targeting the Macrodomain of SARS-CoV-2 NSP3 Using AI-inspired and Knowledge-Based Approaches.

Abstract

The SARS-CoV-2 virus contains a host of nonstructural proteins (NSPs) that contribute to its structure and viral function. Among them is the nonstructural protein 3 (NSP3), which contains a macrodomain (Mac1) that interferes with antiviral adenosine diphosphate (ADP)-ribosylation signaling. Catalytic mutations in Mac1 render viruses nonpathogenic, making this enzyme a promising target for antiviral development. For this reason, the third CACHE challenge focused on identifying binders of the Mac1 domain of NSP3 for the development of novel antivirals against SARS-CoV-2. To this end, we used available structural data of the NSP3 Mac1 domain in complex with known fragment binders as starting points for ligand discovery; our efforts were primarily focused on sub-sites of the ADP binding site in the NSP3 macrodomain. Then, using Artificial intelligence (AI)-guided and knowledge-based fragment merging and expansion approaches, we generated novel molecules that would serve as templates to identify highly similar compounds in the Enamine REAL database that would be commercially available. Our design yielded a library of 12,800 molecules, which was docked with our program FITTED to a representative crystal structure of NSP3. We ranked the predicted binding poses based on docking score, followed by visual pose analysis of the best 200 compounds. We finally selected and proposed 150 compounds for testing, followed by further shortlisting to yield a final list of 107 molecules. 91 compounds were purchased from Enamine and are being tested at the Structural Genomics Consortium (SGC). Our approach and findings will further contribute to our open science efforts, and we aim to continue to engage the scientific community.