A higher flexibility at the SARS-CoV-2 main protease active site compared to SARS-CoV and its potentialities for new inhibitor virtual screening targeting multi-conformers

Abstract

The main-protease (M^pro) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (M^CoV2_pro) with 12 mutations compared to SARS-CoV (M^CoV1_pro). Recent studies point out that these subtle differences lead to mobility variances at the active site loops with functional implications. We use metadynamics simulations and a sort of computational analysis to probe the dynamic, pharmacophoric and catalytic environment differences between the monomers of both enzymes. So, we verify how much intrinsic distinctions are preserved in the functional dimer of M^CoV2_pro, as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the M^CoV2_pro monomer compared to M^CoV1_pro. A higher hydration propensity for the M^CoV2_pro S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for M^CoV2_pro are less catalytically active in the monomer. However, the statistics for contacts involving the N-finger suggest higher maintenance of this activity at the dimer. Docking analyses suggest that the ability to vary the active site width can be important to improve the access of the ligand to the active site in different ways. So, we carry out a multiconformational virtual screening with different ligand bases. The results point to the importance of taking into account the protein conformational multiplicity for new promissors anti M^CoV2_pro ligands. We hope these results will be useful in prospecting, repurposing and/or designing new anti SARS-CoV-2 drugs.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV
• COVID 19
• metadynamics simulation
• multiconformational drug targeting
• quantum calculations

Acknowledgements

The authors thank Dr. Peter Göttig for rich suggestions. The authors also acknowledge the physical structure and computational support provided by Universidade Federal da Paraíba (UFPB); Universidade Federal de Itajubá (Unifei); Laboratório Nacional de Computação Científica – LNCC, Petrópolis/RJ, Brazil (Project “Prospecção e testes in vitro de inibidores de proteínas associadas ao vírus SARS-Cov 2 por meio do uso conjunto de ferramentas de bioinformática, simulação molecular, química quântica e aprendizado de máquina - qcbiocovid19” Supercomputer SDumont); Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP); Núcleo de Processamento de Alto Desempenho of Universidade Federal do Rio Grande do Norte (NPAD/UFRN).

Disclosure statement

No potential conflict of interest was reported by the authors.

Author’s contributions

L.H.F.L. and G.B.R. designed research; R.E.O.R., E.J.F.C., P.H.C., L.S.C.C., I.B.G., L.E.G.C., F.C.G., C. H.S., M.T.S., A.D.C., K.S.M., A.V.W., R.S.F., G.B.R. and L.H.F.L. performed research; R.E.O.R., E.J.F.C., I.B.G., L.E.G.C., R.S.F., G.B.R., C.H.S. and L.H.F.L. analyzed data; and R.E.O.R., E.J.F.C., G.B.R., C.H.S. and L.H.F.L. wrote the paper.