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 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 GlowackiJames Spencer, Martin A Walsh, Christopher J Schofield, Luigi Genovese, Deborah K Shoemark, Adrian J Mulholland, Fernanda Duarte, Garrett M Morris

Research output: Contribution to journal › Article (Academic Journal) › peer-review

27 Citations (Scopus)

51 Downloads (Pure)

Abstract

The main protease (Mpro) 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 Mpro 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 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 plasticity at the S2 site. Building on our initial Mpro-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 Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.

Original language	English
Pages (from-to)	13686-13703
Number of pages	18
Journal	Chemical Science
Volume	12
Issue number	41
Early online date	6 Sept 2021
DOIs	https://doi.org/10.1039/D1SC03628A https://doi.org/10.1039/d1sc03628a
Publication status	Published - 27 Oct 2021

Bibliographical note

Funding Information:
We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRCviaBB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mproplasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force field comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect supportviaEP/S024093/1 and EP/L016044/1. This project made use of time on JADE grantedviathe UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim,), supported by the EPSRCviaEP/P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRCviaHECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research. L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Funding Information:
We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/ L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRC via BB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mpro plasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force eld comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/ 1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect support via EP/ S024093/1 and EP/L016044/1. This project made use of time on JADE granted via the UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim, http:// www.hecbiosim.ac.uk), supported by the EPSRC via EP/ P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www.bris.ac.uk/acrc) under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRC via HECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research (http://cloud.oracle.com/en_US/iaas). L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Publisher Copyright:
© The Royal Society of Chemistry 2021.

Structured keywords

BrisSynBio
Bristol BioDesign Institute

Keywords

SARS-COV-2
Designed peptide inhibitors
Interactive molecular dynamics
Virtual Reality
Modelling and Simulation

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Modelling the reactivity of zinc metalloenzymes and the SARS-CoV-2 main protease
Author: Twidale, R. M., 2 Dec 2021
Supervisor: Butts, C. (Supervisor) & Mulholland, A. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

File

HPC (High Performance Computing) Facility
Sadaf R Alam (Manager), Steven A Chapman (Manager), Polly E Eccleston (Other), Simon H Atack (Other) & D A G Williams (Manager)
Facility/equipment: Facility
Various Computer Clusters, Storage Facility
The University of Bristol
Facility/equipment: Equipment

Cite this

Chan, H. T. H., Moesser, M. A., Walters, R. K., Malla, T. R., Twidale, R. M., John, T., Deeks, H. M., Johnston-Wood, T., Mikhailov, V., Sessions, R. B., Dawson, W., Salah, E., Lukacik, P., Strain-Damerell, C., Owen, C. D., Nakajima, T., Świderek, K., Lodola, A., Moliner, V., ... Morris, G. M. (2021). Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding. Chemical Science, 12(41), 13686-13703. https://doi.org/10.1039/D1SC03628A, https://doi.org/10.1039/d1sc03628a

@article{49ca35cbc19f4d6dbcabd61edab231ca,

title = "Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding",

abstract = "The main protease (Mpro) 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 Mpro 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 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 plasticity at the S2 site. Building on our initial Mpro-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 Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.",

keywords = "SARS-COV-2, Designed peptide inhibitors, Interactive molecular dynamics, Virtual Reality, Modelling and Simulation",

author = "Chan, {H T Henry} and Moesser, {Marc A} and Walters, {Rebecca K} and Malla, {Tika R} and Twidale, {Rebecca M} and Tobias John and Deeks, {Helen M} and Tristan Johnston-Wood and Victor Mikhailov and Sessions, {Richard B} and William Dawson and Eidarus Salah and Petra Lukacik and Claire Strain-Damerell and Owen, {C David} and Takahito Nakajima and Katarzyna {\'S}widerek and Alessio Lodola and Vicent Moliner and Glowacki, {David R} and James Spencer and Walsh, {Martin A} and Schofield, {Christopher J} and Luigi Genovese and Shoemark, {Deborah K} and Mulholland, {Adrian J} and Fernanda Duarte and Morris, {Garrett M}",

note = "Funding Information: We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRCviaBB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mproplasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force field comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect supportviaEP/S024093/1 and EP/L016044/1. This project made use of time on JADE grantedviathe UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim,), supported by the EPSRCviaEP/P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRCviaHECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research. L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Funding Information: We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/ L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRC via BB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mpro plasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force eld comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/ 1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect support via EP/ S024093/1 and EP/L016044/1. This project made use of time on JADE granted via the UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim, http:// www.hecbiosim.ac.uk), supported by the EPSRC via EP/ P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www.bris.ac.uk/acrc) under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRC via HECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research (http://cloud.oracle.com/en_US/iaas). L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} The Royal Society of Chemistry 2021.",

year = "2021",

month = oct,

day = "27",

doi = "10.1039/D1SC03628A",

language = "English",

volume = "12",

pages = "13686--13703",

journal = "Chemical Science",

issn = "2041-6520",

publisher = "Royal Society of Chemistry",

number = "41",

}

Chan, HTH, Moesser, MA, Walters, RK, Malla, TR, Twidale, RM, John, T, Deeks, HM, Johnston-Wood, T, Mikhailov, V, Sessions, RB, Dawson, W, Salah, E, Lukacik, P, Strain-Damerell, C, Owen, CD, Nakajima, T, Świderek, K, Lodola, A, Moliner, V, Glowacki, DR, Spencer, J, Walsh, MA, Schofield, CJ, Genovese, L, Shoemark, DK , Mulholland, AJ, Duarte, F & Morris, GM 2021, 'Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding', Chemical Science, vol. 12, no. 41, pp. 13686-13703. https://doi.org/10.1039/D1SC03628A, https://doi.org/10.1039/d1sc03628a

TY - JOUR

T1 - Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

AU - Chan, H T Henry

AU - Moesser, Marc A

AU - Walters, Rebecca K

AU - Malla, Tika R

AU - Twidale, Rebecca M

AU - John, Tobias

AU - Deeks, Helen M

AU - Johnston-Wood, Tristan

AU - Mikhailov, Victor

AU - Sessions, Richard B

AU - Dawson, William

AU - Salah, Eidarus

AU - Lukacik, Petra

AU - Strain-Damerell, Claire

AU - Owen, C David

AU - Nakajima, Takahito

AU - Świderek, Katarzyna

AU - Lodola, Alessio

AU - Moliner, Vicent

AU - Glowacki, David R

AU - Spencer, James

AU - Walsh, Martin A

AU - Schofield, Christopher J

AU - Genovese, Luigi

AU - Shoemark, Deborah K

AU - Mulholland, Adrian J

AU - Duarte, Fernanda

AU - Morris, Garrett M

N1 - Funding Information: We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRCviaBB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mproplasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force field comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect supportviaEP/S024093/1 and EP/L016044/1. This project made use of time on JADE grantedviathe UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim,), supported by the EPSRCviaEP/P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRCviaHECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research. L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Funding Information: We thank Prof. Michel Sanner for helpful discussions about AutoDock CrankPep. H. T. H. C. thanks the EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine (EP/ L015838/1) and the Clarendon Scholarship. M. A. M. thanks the EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) Grant Number EP/R513295/1 and GlaxoSmithKline. R. K. W. thanks the EPSRC for a PhD studentship. T. R. M. thanks the BBSRC via BB/M011224/1, and Dr Anthony Aimon for dispensing peptibitors using ECHO 550 for the mode of inhibition studies. R. M. T. and T. J. W. acknowledge the EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences (EP/L015722/1). T. J. was supported by the Oxford-GSK-Crick Doctoral Programme in Chemical Biology, EPSRC (EP/R512060/1) and GlaxoSmithKline. E. S. thanks Anastasia Kantsadi and Prof. Ioannis Vakonakis for providing the Mpro plasmid in a pFLOAT vector. D. R. G. acknowledges funding from the Royal Society (URF\R\180033). L. G. thanks Michel Masella for useful discussions and for the force eld comparison. L. G., W. D. and T. N. gratefully acknowledge the CEA-RIKEN collaboration. A. J. M. thanks EPSRC (EP/M022609/ 1). R. B. S. and D. K. S. thank EPSRC Poppi Programme Grant (EP/N013573/1); and BrisSynBio, the BBSRC and EPSRC Synthetic Biology Research Centre (BB/L01386X/1). A. J. M., J. S. and H. M. D. thank the British Society for Antimicrobial Chemotherapy for support (BSAC-COVID-30). V. M. thanks Barcelona Supercomputing Center (QSB-2021-1-0007). G. M. M. thanks the EPSRC and MRC for their indirect support via EP/ S024093/1 and EP/L016044/1. This project made use of time on JADE granted via the UK High-End Computing Consortium for Biomolecular Simulation (HECBioSim, http:// www.hecbiosim.ac.uk), supported by the EPSRC via EP/ P020275/1. MD simulations were also carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www.bris.ac.uk/acrc) under an award for COVID-19 research. D. K. S. and A. J. M. thank EPSRC via HECBioSim for providing ARCHER/ARCHER2 time through a COVID-19 rapid response call; and Oracle Research for Oracle Public Cloud Infrastructure time under an award for COVID-19 research (http://cloud.oracle.com/en_US/iaas). L. G., W. D. and T. N. acknowledge RIKEN through the HPCI System Research Project (Project ID: hp200179 and hp210011) for computer time at the Fugaku supercomputer facility. L. G. is also grateful to the TGCC of CEA for granting of compute hours (gch0429 and gen12047 projects), and to the MaX Center of Excellence. C. J. S. thanks the Wellcome Trust, Cancer Research UK and the Biotechnology and Biological Sciences Research Council for funding. This research was funded in whole, or in part, by the Wellcome Trust (grant no. 106244/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © The Royal Society of Chemistry 2021.

PY - 2021/10/27

Y1 - 2021/10/27

N2 - The main protease (Mpro) 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 Mpro 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 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 plasticity at the S2 site. Building on our initial Mpro-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 Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.

AB - The main protease (Mpro) 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 Mpro 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 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 plasticity at the S2 site. Building on our initial Mpro-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 Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.

KW - SARS-COV-2

KW - Designed peptide inhibitors

KW - Interactive molecular dynamics

KW - Virtual Reality

KW - Modelling and Simulation

U2 - 10.1039/D1SC03628A

DO - 10.1039/D1SC03628A

M3 - Article (Academic Journal)

C2 - 34760153

SN - 2041-6520

VL - 12

SP - 13686

EP - 13703

JO - Chemical Science

JF - Chemical Science

IS - 41

ER -

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

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Student theses

Modelling the reactivity of zinc metalloenzymes and the SARS-CoV-2 main protease

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HPC (High Performance Computing) Facility

Various Computer Clusters, Storage Facility

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