Published 16 March 2021 in ACS Pharmacology & Translational Sciences (doi 10.1021/acsptsci.0c00215):
A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics
Jonas Gossen, Simone Albani, Anton Hanke, Benjamin P Joseph, Cathrine Bergh, Maria Kuzikov, Elisa Costanzi, Candida Manelfi, Paola Storici, Philip Gribbon, Andrea R Beccari, Carmine Talarico, Francesca Spyrakis, Erik Lindahl, Andrea Zaliani, Paolo Carloni, Rebecca C Wade, Francesco Musiani, Daria B Kokh, and Giulia Rossetti
The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.
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