Nature's cryptographic codebreaker: in silico decoding of apigenin's triple defense against SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic underscored the urgent need for broad-spectrum antiviral agents capable of targeting both viral proteins and host factors to mitigate disease severity. Apigenin has antiviral and anti-inflammatory properties. However, the potential of apigenin against SARS-CoV-2 remains insufficiently explored.

In this study, the potential role of apigenin in the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the underlying mechanism were explored using matrix factorization, deep learning, multiscale molecular modeling and network pharmacology.

The graph-based integrated Gaussian kernel similarity (GiGs) model predicted that apigenin might be a drug against SARS-CoV-2. The prediction of drug-target affinity using a convolution model with self-attention (CSatDTA) model revealed the potential binding affinity of apigenin with glucose-regulated protein 78 (GRP78) and heparan sulfate proteoglycan (HSPG). Molecular docking further validated strong binding to GRP78 (-8.198 kcal/mol) and moderate binding to HSPG (-5.6 kcal/mol), mediated by van der Waals forces and hydrogen bonds. Multiscale molecular modeling revealed that apigenin could bind to Non-structural protein 15 (Nsp15). Further, the network pharmacology analysis implied that apigenin might modulate the host inflammatory responses by potentially regulating the PI3K-Akt and HIF-1 signaling pathways and binding directly to protein kinase B (AKT1) and prostaglandin endoperoxide synthase 2 (PTGS2).

Computational profiling suggests apigenin exerts a multi-target mechanism against SARS-CoV-2, potentially disrupting viral entry, replication, and host inflammatory responses. The findings of this research outline a promising strategy and provide a rationale for developing novel natural product-based treatment methods for SARS-CoV-2.