9E9P

Crystal structure of SARS-CoV-2 main protease (Mpro) in complex with covalent inhibitor A02

This is a non-PDB format compatible entry.

Summary for 9E9P

• Entry DOI: 10.2210/pdb9e9p/pdb
• Descriptor: 3C-like proteinase nsp5, 1-[(1M)-1-(3-methoxyphenyl)-2,5-dimethyl-1H-pyrrol-3-yl]ethan-1-one (3 entities in total)
• Functional Keywords: sars-cov-2, protease, covalent inhibitor, viral protein, hydrolase
• Biological source: Severe acute respiratory syndrome coronavirus 2
• Total number of polymer chains: 1
• Total formula weight: 34212.98

Authors

D'Oliviera, A.,Mugridge, J.S. (deposition date: 2024-11-08, release date: 2026-05-13, Last modification date: 2026-05-27)

Primary citation

Zhou, W.,D Oliviera, A.,Dai, X.,Mugridge, J.S.,Zhang, Y.
A Novel Covalent Inhibitor Fragment for the SARS-CoV-2 Main Protease Identified by Target-Specific Deep Learning.
Acs Chem.Biol., 21:1112-1124, 2026

PubMed Abstract: The SARS-CoV-2 main protease (M, also known as 3CL) is an attractive antiviral drug target due to its essential role in viral replication and absence of human homologues. Development of new coronavirus-specific M inhibitors will be important as SARS-CoV-2 continues to evolve. Leveraging the rapidly expanding pool of diverse, experimental M-inhibitor data, we developed a target-specific deep learning workflow to accelerate the discovery of new M inhibitor compounds and fragment-like starting points. This workflow combined a fine-tuned inhibitor prediction model with solubility (logS) and lipophilicity (logP) models, molecular similarity analysis, and literature mining to prioritize novel, drug-like candidates. Applied to a purchasable library of over 500,000 compounds, the approach rapidly identified 24 candidates for experimental testing. Biochemical assays revealed a novel, small covalent inhibitor fragment (A02) with an apparent IC of 1.5 μM, prior to any synthetic optimization or derivatization. A 1.76 Å crystal structure of M bound to A02 confirmed covalent modification of the catalytic M cysteine (C145), unique engagement of the underutilized M S3' pocket, and the potential for derivatives of this scaffold to interact with additional M pockets in future optimization efforts. Together, these results demonstrate the potential for target-specific deep learning approaches to guide the rapid screening and discovery of new inhibitor leads or drug scaffolds.

PubMed: 42066065
DOI: 10.1021/acschembio.6c00120

Experimental method

X-RAY DIFFRACTION (1.76 Å)