Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 62, Issue 1, Pages 116-128Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c00851
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Funding
- DOE Office of Science through the National Virtual Biotechnology Laboratory
- CANDLE project by the DOE-Exascale Computing Project [17-SC-20-SC]
- DOE-Computational Sciences Graduate Fellowship (DOE-CSGF) [DE-SC0019323]
- The Office of Biological and Environmental Research
- DOE Office of Science User Facility [DE-AC02-06CH11357, DE-AC05-00OR22725]
- Coronavirus CARES Act
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This paper describes the discovery of a novel noncovalent small-molecule inhibitor targeting the main protease of SARS-CoV-2, utilizing high-throughput virtual screening and a compound library of over 6.5 million molecules. Downstream biochemical assays validate the effectiveness and binding mechanism of the inhibitor, providing a reference for future therapeutic design.
Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (M-pro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this M-pro inhibitor with an inhibition constant (K-i) of 2.9 mu M (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of M-pro forming stable hydrogen bond and hydrophobic interactions. We then used multiple its-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by M-pro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits M-pro and offers a springboard for further therapeutic design.
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