期刊
CHEMICAL SCIENCE
卷 12, 期 41, 页码 13686-13703出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1sc03628a
关键词
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资金
- EPSRC [EP/L015838/1, EP/R513295/1, EP/L015722/1, EP/R512060/1, EP/M022609/1, EP/N013573/1, BB/L01386X/1, EP/S024093/1, EP/L016044/1, EP/P020275/1]
- Clarendon Scholarship
- GlaxoSmithKline
- BBSRC [BB/L01386X/1, BB/M011224/1]
- Royal Society [URF\R\180033]
- BrisSynBio
- British Society for Antimicrobial Chemotherapy [BSAC-COVID-30]
- Barcelona Supercomputing Center [QSB-2021-1-0007]
- MRC [EP/S024093/1, EP/L016044/1]
- University of Bristol
- RIKEN through the HPCI System Research Project [hp200179, hp210011]
- Wellcome Trust [106244/Z/14/Z]
- Cancer Research UK
- [gch0429]
- [gen12047]
- BBSRC [BB/L01386X/1] Funding Source: UKRI
- EPSRC [EP/P020275/1, EP/S024093/1, EP/M022609/1, EP/N013573/1] Funding Source: UKRI
Through various biophysical and crystallographic data analysis, researchers investigated the molecular features underlying the binding of SARS-CoV-2's main protease to its natural cleavage sites, highlighting the importance of interactions between substrates and enzymes. They designed peptides with improved affinity to competitively inhibit M-pro, providing new insights and opportunities for the development of M-pro inhibitors as anti-COVID-19 drugs.
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.
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