Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

H. T. Henry Chan; Marc A. Moesser; Rebecca K. Walters; Tika R. Malla; Rebecca M. Twidale; Tobias John; Helen M. Deeks; Tristan Johnston-Wood; Victor Mikhailov; Richard B. Sessions; William Dawson; Eidarus Salah; Petra Lukacik; Claire Strain-Damerell; C. David Owen; Takahito Nakajima; Katarzyna Świderek; Alessio Lodola; Vicent Moliner; David R. Glowacki; James Spencer; Martin A. Walsh; Christopher J. Schofield; Luigi Genovese; Deborah K. Shoemark; Adrian J. Mulholland; Fernanda Duarte; Garrett M. Morris

doi:10.1039/D1SC03628A

Discovery of SARS-CoV-2 M^pro peptide inhibitors from modelling substrate and ligand binding†

H. T. Henry Chan,

‡^a Marc A. Moesser,

‡^b Rebecca K. Walters,

‡^cd Tika R. Malla,

‡^a Rebecca M. Twidale,

^c Tobias John,

^a Helen M. Deeks,

^cd Tristan Johnston-Wood,

^a Victor Mikhailov,

^a Richard B. Sessions,

^e William Dawson,

^f Eidarus Salah,

^a Petra Lukacik,

^gh Claire Strain-Damerell,

^gh C. David Owen,

^gh Takahito Nakajima,

^f Katarzyna Świderek,

ⁱ Alessio Lodola,

^j Vicent Moliner,

ⁱ David R. Glowacki,

^d James Spencer,

^d Martin A. Walsh,

^gh Christopher J. Schofield,

*^a Luigi Genovese,

*^k Deborah K. Shoemark,

*^e Adrian J. Mulholland,

*^c Fernanda Duarte

*^a and Garrett M. Morris

*^b

Author affiliations

* Corresponding authors

^a Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research, 12 Mansfield Road, Oxford, UK
E-mail: fernanda.duartegonzalez@chem.ox.ac.uk, christopher.schofield@chem.ox.ac.uk

^b Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, UK
E-mail: garrett.morris@stats.ox.ac.uk

^c Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK
E-mail: adrian.mulholland@bristol.ac.uk

^d Intangible Realities Laboratory, School of Chemistry, University of Bristol, Cantock's Close, Bristol, UK

^e School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, Bristol, UK
E-mail: deb.shoemark@bristol.ac.uk

^f RIKEN Center for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan

^g Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot, UK

^h Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, UK

ⁱ Biocomp Group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castello, Spain

^j Food and Drug Department, University of Parma, Parco Area delle Scienze, 27/A, 43124 Parma, Italy

^k Univ. Grenoble Alpes, CEA, IRIG-MEM-L_Sim, 38000 Grenoble, France
E-mail: luigi.genovese@cea.fr

Abstract

The main protease (M^pro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural M^pro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the 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 plasticity at the S2 site. Building on our initial M^pro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective ‘peptibitors’ inhibit M^pro competitively. Our combined results provide new insights and highlight opportunities for the development of M^pro inhibitors as anti-COVID-19 drugs.

This article is part of the themed collections: Most popular 2021 physical and theoretical chemistry articles, 2021 and 2021 Chemical Science HOT Article Collection

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Discovery of SARS-CoV-2 M^pro peptide inhibitors from modelling substrate and ligand binding†

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Chemical Science

Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding†

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Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

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Discovery of SARS-CoV-2 M^pro peptide inhibitors from modelling substrate and ligand binding†

Discovery of SARS-CoV-2 M^pro peptide inhibitors from modelling substrate and ligand binding