Discovery of SARS-CoV-2 M pro Peptide Inhibitors from Modelling Substrate and Ligand Binding

  1. H. T. Henry Chan
  2. Marc A. Moesser
  3. Rebecca K. Walters
  4. Tika R. Malla
  5. Rebecca M. Twidale
  6. Tobias John
  7. Helen M. Deeks
  8. Tristan Johnston-Wood
  9. Victor Mikhailov
  10. Richard B. Sessions
  11. William Dawson
  12. Eidarus Salah
  13. Petra Lukacik
  14. Claire Strain-Damerell
  15. C. David Owen
  16. Takahito Nakajima
  17. Katarzyna Świderek
  18. Alessio Lodola
  19. Vicent Moliner
  20. David R. Glowacki
  21. Martin A. Walsh
  22. Christopher J. Schofield
  23. Luigi Genovese
  24. Deborah K. Shoemark
  25. Adrian J. Mulholland
  26. Fernanda Duarte
  27. Garrett M. Morris

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Abstract

The main protease (M pro ) of SARS-CoV-2 is central to its viral lifecycle and is a promising drug target, but little is known concerning structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of classical molecular mechanics and quantum mechanical techniques, including automated docking, molecular dynamics (MD) simulations, linear-scaling DFT, QM/MM, and interactive MD in virtual reality, to investigate the molecular features underlying recognition of the natural M pro substrates. Analyses of the subsite interactions of modelled 11-residue cleavage site peptides, ligands from high-throughput crystallography, and designed covalently binding inhibitors were performed. Modelling studies reveal remarkable conservation of hydrogen bonding patterns of the natural M pro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular at the P2/S2 sites. The binding modes of the natural substrates, together with extensive interaction analyses of inhibitor and fragment binding to M pro , reveal new opportunities for inhibition. Building on our initial M pro -substrate models, computational mutagenesis scanning was employed to design peptides with improved affinity and which inhibit M pro competitively. The combined results provide new insight useful for the development of M pro inhibitors.

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    SciScore for 10.1101/2021.06.18.446355: (What is this?)

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    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

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    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.06.18.446355v1 on bioRxiv
  3. Version published to 10.1039/d1sc03628a