Targeted covalent inhibition represents one possible strategy to block the function of SARS-CoV-2 Main Protease (M), an enzyme that plays a critical role in the replication of the novel SARS-CoV-2. Toward the design of covalent inhibitors, we built a covalent inhibitor dataset using deep learning models followed by high throughput virtual screening of these candidates against M. Two top-ranking inhibitors were selected for mechanistic investigations-one with an activated ester warhead that has a piperazine core and the other with an acrylamide warhead. Specifically, we performed a detailed analysis of the free energetics of covalent inhibition by hybrid quantum mechanics/molecular mechanics simulations. Cleavage of a fragment of the non-structured protein (NSP) from the SARS-CoV-2 genome was also simulated for reference. Simulations show that both candidates form more stable enzyme-inhibitor (E-I) complexes than the chosen NSP. It was found that both the NSP fragment and the activated ester inhibitor react with CYS145 of M in a concerted manner, whereas the acrylamide inhibitor follows a stepwise mechanism. Most importantly, the reversible reaction and the subsequent hydrolysis reaction from E-I complexes are less probable when compared to the reactions with an NSP fragment, showing promise for these candidates to be the base for efficient M inhibitors.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9722715 | PMC |
http://dx.doi.org/10.1038/s41598-022-23570-6 | DOI Listing |
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