PNNL

Abstract

Targeted covalent inhibition represents one possible strategy to block the function of SARS-CoV-2 Main Protease (MPRO), an enzyme that plays a critical role in the replication of the novel SARS-CoV-2. Toward the design of covalent inhibitors, we built the covalent inhibitor datasets using our deep learning models followed by high throughput virtual screening of these candidates against MPRO using covalent workflow. For mechanistic investigations, we selected two promising warheads – 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 molecular dynamics simulations. Cleavage of a fragment of the non-structured protein (NSP) from the SARS-CoV2 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 MPRO 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 MPRO inhibitors.