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Discovery of SARS-CoV-2 M-pro peptide inhibitors from modelling substrate and ligand binding
dc.contributor.author | Chan, Henry | |
dc.contributor.author | Moesser, Marc Alexander | |
dc.contributor.author | Walters, Rebecca | |
dc.contributor.author | Malla, Tika | |
dc.contributor.author | Twidale, Rebecca | |
dc.contributor.author | John, Tobias | |
dc.contributor.author | Deeks, Helen M. | |
dc.contributor.author | Johnston-Wood, Tristan | |
dc.contributor.author | Mikhailov, Victor | |
dc.contributor.author | Sessions, Richard | |
dc.contributor.author | Dawson, William | |
dc.contributor.author | Salah, Eidarus | |
dc.contributor.author | Lukacik, Petra | |
dc.contributor.author | Strain-Damerell, Claire | |
dc.contributor.author | Owen, David | |
dc.contributor.author | Nakajima, Takahito | |
dc.contributor.author | Świderek, Katarzyna | |
dc.contributor.author | Lodola, Alessio | |
dc.contributor.author | Moliner, Vicent | |
dc.contributor.author | Glowacki, David | |
dc.contributor.author | Walsh, Martin Austin | |
dc.contributor.author | Schofield, Christopher | |
dc.contributor.author | Genovese, Luigi | |
dc.contributor.author | Shoemark, Deborah K. | |
dc.contributor.author | Mulholland, Adrian | |
dc.contributor.author | Duarte, Fernanda | |
dc.contributor.author | Morris, Garrett | |
dc.date.accessioned | 2021-11-04T10:44:00Z | |
dc.date.available | 2021-11-04T10:44:00Z | |
dc.date.issued | 2021-09-06 | |
dc.identifier.citation | CHAN, HT Henry, et al. Discovery of SARS-CoV-2 Mpro Peptide Inhibitors from Modelling Substrate and Ligand Binding. bioRxiv, 2021. doi: https://doi.org/10.1101/2021.06.18.446355 | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/195377 | |
dc.description.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 linearscaling 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. | ca_CA |
dc.format.extent | 19 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | The Royal Society of Chemistry | ca_CA |
dc.relation.isPartOf | Chem. Sci., 2021, 12, 13686 | ca_CA |
dc.rights | © 2021 The Author(s). Published by the Royal Society of Chemistry | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | ca_CA |
dc.subject | SARS-CoV-2 | ca_CA |
dc.subject | coronavirus | ca_CA |
dc.subject | Mpro inhibitors | ca_CA |
dc.subject | main protease (Mpro) | ca_CA |
dc.subject | anti-COVID- 19 drugs | ca_CA |
dc.title | Discovery of SARS-CoV-2 M-pro peptide inhibitors from modelling substrate and ligand binding | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1101/2021.06.18.446355 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine | ca_CA |
project.funder.name | EPSRC University of Oxford Mathematics, Physical, and Life Sciences Division (MPLS) Doctoral Training Partnership (DTP) | ca_CA |
project.funder.name | GlaxoSmithKline | ca_CA |
project.funder.name | Biotechnology and Biological Sciences Research Council (BBSRC) | ca_CA |
project.funder.name | Royal Society of Chemistry | ca_CA |
project.funder.name | BrisSynBio | ca_CA |
project.funder.name | EPSRC Synthetic Biology Research Centre | ca_CA |
project.funder.name | British Society for Antimicrobial Chemotherapy | ca_CA |
project.funder.name | Barcelona Supercomputing Center | ca_CA |
project.funder.name | UK High-End Computing Consortium for Biomolecular Simulation | ca_CA |
project.funder.name | Advanced Computing Research Centre, University of Bristol | ca_CA |
project.funder.name | Oracle Public Cloud Infrastructure | ca_CA |
project.funder.name | HECBioSim, ARCHER/ARCHER2 | ca_CA |
project.funder.name | RIKEN, HPCI System Research Project | ca_CA |
project.funder.name | Very Large Computing center of CEA (TGCC) | ca_CA |
project.funder.name | MaX Center of Excellence | ca_CA |
project.funder.name | Wellcome Trust, Cancer Research, UK | ca_CA |
oaire.awardNumber | EP/ L015838/1 | ca_CA |
oaire.awardNumber | EP/R513295/1 | ca_CA |
oaire.awardNumber | BB/M011224/1 | ca_CA |
oaire.awardNumber | EP/L015722/1 | ca_CA |
oaire.awardNumber | EP/R512060/1 | ca_CA |
oaire.awardNumber | URF\R\180033 | ca_CA |
oaire.awardNumber | EP/M022609/1 | ca_CA |
oaire.awardNumber | EP/N013573/1 | ca_CA |
oaire.awardNumber | BB/L01386X/1 | ca_CA |
oaire.awardNumber | BSAC-COVID-30 | ca_CA |
oaire.awardNumber | QSB-2021-1-0007 | ca_CA |
oaire.awardNumber | EP/S024093/1 | ca_CA |
oaire.awardNumber | EP/L016044/1 | ca_CA |
oaire.awardNumber | EP/ P020275/1 | ca_CA |
oaire.awardNumber | hp200179 | ca_CA |
oaire.awardNumber | hp210011 | ca_CA |
oaire.awardNumber | gch0429 | ca_CA |
oaire.awardNumber | gen12047 | ca_CA |
oaire.awardNumber | 106244/Z/14/Z | ca_CA |
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