Identification of apigenin as a multi‐target inhibitor against <scp>SARS</scp> ‐ <scp>CoV</scp> ‐2 by computational exploration
Multi-target strategy can serve as a valid treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but existing drugs most focus on a single target. Thus, multi-target drugs that bind multiple sites simultaneously need to be urgently studied. Apigenin has antiviral and anti-inflammatory properties. Here, we comprehensively explored the potential effect and mechanism of apigenin in SARS-CoV-2 treatment by a network algorithm, deep learning, molecular docking, molecular dynamics (MD) simulation, and normal mode analysis (NMA). KATZ-based VDA prediction method (VDA-KATZ) indicated that apigenin may provide a latent drug therapy for SARS-CoV-2. Prediction of DTA using convolution model with self-attention (CSatDTA) showed potential binding affinity of apigenin with multiple targets of virus entry, assembly, and cytokine storms including cathepsin L (CTSL), membrane (M), envelope (E), Toll-like receptor 4 (TLR4), nuclear factor-kappa B (NF-κB), NOD-like receptor pyrin domain-containing protein 3 (NLRP3), apoptosis-associated speck-like protein (ASC), and cysteinyl aspartate-specific proteinase-1 (Caspase-1). Molecular docking indicated that apigenin could effectively bind these targets, and its stability was confirmed using MD simulation and NMA. Overall, apigenin is a multi-target inhibitor for the entry, assembly, and cytokine storms of SARS-CoV-2.
Authors
- Juanjuan Huang (ORCID: https://orcid.org/0000-0002-6608-947X)
- Hanxi Xie
- Jiwei Jia (ORCID: https://orcid.org/0000-0001-9055-7627)
- Runze Wu
- Ming Yang (ORCID: https://orcid.org/0000-0002-1617-9960)
- Guoqing Wang (ORCID: https://orcid.org/0000-0001-9326-3755)
- Yixuan Jin
Institutions
- Jilin University (CN)
- Jilin Medical University (CN)
- Union Hospital (CN)
Publication Details
- Journal
- The FASEB Journal
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1096/fj.202401972rrr
- Citations
- 3
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.08
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities