Chemical ScienceBioinformaticsPeer-reviewed journal article
Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding
H. T. Henry ChaniD, Marc A. MoesseriD, Rebecca K. WaltersiD, Tika R. MallaiD, Rebecca M. TwidaleiD, Tobias JohniD, Helen M. DeeksiD, Tristan Johnston-WoodiD, Victor A. MikhailoviD, Richard Barry SessionsiD, William DawsoniD, E. SalahiD, Petra LukacikiD, Claire M. Strain-DamerelliD, Callum D. OweniD, Takahito NakajimaiD, Katarzyna ŚwiderekiD, Alessio LodolaiD, Vicent MolineriD, David R. GlowackiiD, JAMES M. SPENCERiD, Martin Austin WalshiD, Christopher John SchofieldiD, Luigi GenoveseiD, Deborah K. ShoemarkiD, , Garrett M. MorrisiD
Abstract
The main protease (Mpro) 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 Mpro 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 Mpro 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 Mpro, reveal new opportunities for inhibition. Building on our initial Mpro-substrate models, computational mutagenesis scanning was employed to design peptides with improved affinity and which inhibit Mpro competitively. The combined results provide new insight useful for the development of Mpro inhibitors.
- organism: virus
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- approach: simulation
- Citations
- 90
- FWCI
- 8.53
- Field percentile
- top 1%
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