Computational Discovery of Natural Dual Inhibitors Targeting SARS‐CoV‐2 Main and Papain‐Like Proteases
Abstract The COVID‐19 pandemic, caused by SARS‐CoV‐2, underscores the need for effective therapeutic agents targeting viral proteases. In this study, we explored natural compounds from flavonoids, terpenoids, phenylpropanoids, and steroids derived from plants such as Ginkgo biloba , Lippia alba , Glechoma hederacea , and Quercus mongolica bark. These compounds were screened for their anticoagulant activity against two critical SARS‐CoV‐2 proteases: the main protease (Mpro, PDB ID: 7JN2) and the papain‐like protease (PLpro, PDB ID: 7K40). A library of 110 anticoagulant compounds was subjected to molecular docking to assess their binding modes and affinities. Comparative docking analyses with Nirmatrelvir, a reference drug, identified Genkwanin (M021), Luteolin 7‐O‐(600‐malonylglucoside) (M072), and Astragalin (M080) as potential candidates with notable binding affinities. Among these, M080 stood out for its favorable pharmacokinetic properties, meeting Lipinski's rule of five criteria. Furthermore, a 100 ns molecular dynamics simulation demonstrated the binding stability of these compounds within the protease active sites, providing insights into their potential as inhibitors. Our findings suggest that M080 holds significant promise as a lead compound for developing SARS‐CoV‐2 inhibitors, warranting further investigation in preclinical studies.
Authors
- Imane Yamari (ORCID: https://orcid.org/0000-0002-8406-8533)
- Mohammed Talbi (ORCID: https://orcid.org/0000-0002-9262-2223)
- Mhammed El Kouali (ORCID: https://orcid.org/0000-0002-7036-450X)
- Samir Chtita (ORCID: https://orcid.org/0000-0003-2344-5101)
- Abdelkbir Errougui (ORCID: https://orcid.org/0000-0001-9972-7522)
- Mohammed Bouachrine
- Lamiae Elbouamri
Institutions
- Université Moulay Ismail de Meknes (MA)
- University of Hassan II Casablanca (MA)
Publication Details
- Journal
- ChemistrySelect
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/slct.202501293
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50