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Identifying Natural Products as Feline Coronavirus Mpro Inhibitors by Structural-Based Virtual Screening and
Zunyun Jiang1, Lianhua Piao2,3, Changyi Ren3
1Jiangsu Key Laboratory of Pesticide Science, College of Sciences, Nanjing Agricultural University, Nanjing 210095, P.R. China.
ACS Omega
|January 27, 2025
Summary
Natural compounds were screened to inhibit feline coronavirus Mpro, a key target in feline infectious peritonitis. Several compounds, including theaflavin 3,3-digallate, showed significant inhibitory effects, offering potential for new FIP treatments.
Area of Science:
- Veterinary Virology
- Drug Discovery
- Computational Chemistry
Background:
- Feline coronavirus (FCoV) causes feline infectious peritonitis (FIP), a fatal disease in cats.
- The viral main protease (Mpro) is essential for FCoV replication and a critical drug target.
Purpose of the Study:
- To identify potent and low-toxicity natural compounds that inhibit FCoV Mpro.
- To explore the binding interactions of these compounds with the Mpro active site.
Main Methods:
- Virtual screening of a natural product library against FCoV Mpro.
- Enzyme inhibition assays to determine the efficacy of selected compounds.
- Molecular dynamics simulations and binding free energy calculations to analyze interactions.
Main Results:
- Eighty-six compounds were initially selected based on docking scores.
- Twelve compounds demonstrated over 40% inhibition of Mpro at 200 μmol/L.
- Theaflavin 3,3'-digallate, sennoside C, pinocembrin-galloyl-HHDP-G, and thonningianin A showed IC50 values ranging from 25.0 to 50.6 μmol/L.
- Curcuminoids and flavonoids also exhibited notable inhibitory activity.
- Simulations revealed key interactions between compounds and Mpro active site residues (His-41, Cys-144, Glu-165).
Conclusions:
- Natural products, particularly theaflavin 3,3'-digallate and sennoside C, are effective inhibitors of FCoV Mpro.
- These identified compounds represent promising leads for developing novel FIP therapeutics.
- Detailed binding analyses provide insights for future drug design and optimization.

