8Y7T

Crystal structure of SARS-CoV-2 main protease in complex with C2

This is a non-PDB format compatible entry.

Summary for 8Y7T

• Entry DOI: 10.2210/pdb8y7t/pdb
• Descriptor: 3C-like proteinase nsp5, 6-(iminomethyl)-4-(2-pyridin-2-ylethyl)-2-[4-(trifluoromethyl)phenyl]-1,2,4-triazine-3,5-dione (3 entities in total)
• Functional Keywords: severe acute respiratory syndrome coronavirus 2, main protease, viral protein
• Biological source: Severe acute respiratory syndrome coronavirus 2
• Total number of polymer chains: 1
• Total formula weight: 34255.91

Authors

Zeng, R.,Deng, X.Y.,Yang, Z.Y.,Wang, K.,Jiang, Y.Y.,Lei, J. (deposition date: 2024-02-05, release date: 2025-02-05, Last modification date: 2025-04-23)

Primary citation

Yang, Z.,Wang, K.,Zhang, G.,Jiang, Y.,Zeng, R.,Qiao, J.,Li, Y.,Deng, X.,Xia, Z.,Yao, R.,Zeng, X.,Zhang, L.,Zhao, Y.,Lei, J.,Chen, R.
A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.
Eur.J.Med.Chem., 291:117602-117602, 2025

PubMed Abstract: Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge, we present Pocket-StrMod, a deep-learning model tailored for structure-based bioactivity optimization. Pocket-StrMod employs an autoregressive flow-based architecture, optimizing molecules within a specific protein binding pocket while explicitly incorporating chemical expertise. It synchronously optimizes all substituents by generating atoms and covalent bonds at designated sites within a molecular scaffold nestled inside a protein pocket. We applied this model to optimize the bioactivity of Hit1, an inhibitor of the SARS-CoV-2 main protease (M) with initially poor bioactivity (IC : 34.56 μM). Following two rounds of optimization, six compounds were selected for synthesis and bioactivity testing. This led to the discovery of C5, a potent compound with an IC value of 33.6 nM, marking a remarkable 1028-fold improvement over Hit1. Furthermore, C5 demonstrated promising in vitro antiviral activity against SARS-CoV-2. Collectively, these findings underscore the great potential of deep learning in facilitating rapid and cost-effective bioactivity optimization in the early phases of drug development.

PubMed: 40239482
DOI: 10.1016/j.ejmech.2025.117602

Experimental method

X-RAY DIFFRACTION (2.5 Å)