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.

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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

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article

Identification of apigenin as a multi‐target inhibitor against SARS ‐ CoV ‐2 by computational exploration

Juanjuan Huang, Hanxi Xie, Jiwei Jia, Runze Wu et al.
3 citations
The FASEB Journal
Computational Drug Discovery Methods
1.08
article

Identification of apigenin as a multi‐target inhibitor against SARS ‐ CoV ‐2 by computational exploration

Juanjuan Huang, Hanxi Xie, Jiwei Jia, Runze Wu, Ming Yang, Guoqing Wang, Yixuan Jin
article en
3 citations

Abstract

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.

The FASEB JournalVol. 38(24)
Jilin University (CN), Jilin Medical University (CN), Union Hospital (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Good health and well-being
Openalex Percentile: Top 19%
Computational Drug Discovery Methods
1.08
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