Impact of SARS-CoV-2 3CL Protease Mutations on Nirmatrelvir Inhibitory Efficiency. Computational Insights into Potential Resistance Mechanisms

Abstract

The use of antiviral drugs can promote the appearance of mutations in the target protein that increase the resistance of the virus to the treatment. This is also the case of nirmatrelvir, a covalent inhibitor of the 3CL protease of SARS-CoV-2. In this work we show how the by-residue decomposition of noncovalent interactions established between the drug and the enzyme, in combination with an analysis of natural occurring mutations, can be used to detect potential mutations in 3CL protease conferring resistance to nirmatrelvir. We also investigate the consequences of these mutations on the reaction mechanism to form the covalent enzyme-inhibitor complex. In particular, we show that the E166V variant of the protease shows a smaller affinity by nirmatrelvir and a larger activation free energy for the formation of the covalent complex, both factors contributing to an increased resistance to the treatment with this drug. The conclusions derived from our work can be used to anticipate the consequences of the introduction of nirmatrelvir in the fitness landscape of the virus and to design new inhibitors to prevent resistance mechanisms.

Dynamical nonequilibrium molecular dynamics simulations identify allosteric sites and positions associated with drug resistance in the SARS-CoV-2 main protease

“…Selection pressure caused by treatments that target M pro , such as nirmatrelvir, drives the emergences of drug resistant mutations in SARS-CoV-2 M pro . 24, 34, 53, 54 Such mutations would be expected to weaken the binding affinity of nirmatrelvir, but still allow M pro to perform its biological function by binding, recognising, and hydrolysing its substrate sequences in the viral polyproteins. The effect of such mutations on dynamics is of particular interest.…”

“…11 Crystal structures 4,[12][13][14] and molecular simulations [15][16][17][18][19][20] have provided insights into M pro structure, dynamics and interactions with its substrates and other ligands. Molecular simulations of M pro have identified cryptic pockets, 21 informed on mechanisms of inhibition, [22][23][24] and helped to suggest potential lead compounds for M pro inhibitor design. 25,26 We have identified conserved interactions between M pro and its 11 native substrates, each modelled as an 11-mer P6-P5ʹ peptide.…”