Bicyclic peptide-enhanced covalent inhibitor of SARS-CoV-2 3CL protease

Aim: Develop technology to apply bicyclic peptides for discovering covalent inhibitors of proteases and use this technology to create bicyclic peptide—warhead conjugates for targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like (3CL) protease. Enhance the potency of the discovered bicyclic peptides for potential development into anti-SARS-CoV-2 drugs. Methods: Rational design was employed to discover the initial bicyclic peptide—warhead conjugates. Medicinal chemistry optimization was conducted to improve the potency of these peptides. Enzymatic assays and mass spectrometry characterization were performed to validate the covalent inhibition of the target protease. Results: The need for peptide display selection in discovering hit bicyclic peptides was overcome. Active bicyclic peptide—vinyl sulfone inhibitors with nanomolar potency were discovered. Optimization through medicinal chemistry strategies not only improved the potency of the peptides but also revealed residue preferences at individual positions of the bicyclic peptide inhibitors. The most potent bicyclic peptide can inhibit the target with a half-maximal inhibitory concentration (IC50) of 40.46 ± 6.35 nM. Mass spectrometry tests confirmed the covalent inhibition of the target protease by the developed peptides. Conclusions: Bicyclic peptide and vinyl sulfone conjugates are a form of covalent and potent inhibitors for targeting proteases. The rational design of bicyclic peptide ligands is feasible when structural and amino acid preference information is available. Structural information is also crucial for optimizing the potency of bicyclic peptide ligands.

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Publication Details

Journal
Exploration of Drug Science
Published
2024-10-17
DOI
https://doi.org/10.37349/eds.2024.00071
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.72

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article

Bicyclic peptide-enhanced covalent inhibitor of SARS-CoV-2 3CL protease

Yanhui Wang, Jian Li, Hong Shu Liu, Shiyu Chen et al.
2 citations
Exploration of Drug Science
Computational Drug Discovery Methods
0.72
article

Bicyclic peptide-enhanced covalent inhibitor of SARS-CoV-2 3CL protease

Yanhui Wang, Jian Li, Hong Shu Liu, Shiyu Chen, Qian Wang
article en
2 citations

Abstract

Aim: Develop technology to apply bicyclic peptides for discovering covalent inhibitors of proteases and use this technology to create bicyclic peptide—warhead conjugates for targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like (3CL) protease. Enhance the potency of the discovered bicyclic peptides for potential development into anti-SARS-CoV-2 drugs. Methods: Rational design was employed to discover the initial bicyclic peptide—warhead conjugates. Medicinal chemistry optimization was conducted to improve the potency of these peptides. Enzymatic assays and mass spectrometry characterization were performed to validate the covalent inhibition of the target protease. Results: The need for peptide display selection in discovering hit bicyclic peptides was overcome. Active bicyclic peptide—vinyl sulfone inhibitors with nanomolar potency were discovered. Optimization through medicinal chemistry strategies not only improved the potency of the peptides but also revealed residue preferences at individual positions of the bicyclic peptide inhibitors. The most potent bicyclic peptide can inhibit the target with a half-maximal inhibitory concentration (IC50) of 40.46 ± 6.35 nM. Mass spectrometry tests confirmed the covalent inhibition of the target protease by the developed peptides. Conclusions: Bicyclic peptide and vinyl sulfone conjugates are a form of covalent and potent inhibitors for targeting proteases. The rational design of bicyclic peptide ligands is feasible when structural and amino acid preference information is available. Structural information is also crucial for optimizing the potency of bicyclic peptide ligands.

Exploration of Drug ScienceVol. 2(6)
Chinese Academy of Sciences (CN), Shanghai Institute of Materia Medica (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 25%
Computational Drug Discovery Methods
0.72
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