Targeting SARS-CoV-2 main protease (3CL^pro) with Paeonia-derived phytochemicals.

Area of Science:

• Phytochemistry
• Computational Drug Discovery
• Virology

Background:

• Coronavirus Disease 19 (COVID-19), caused by SARS-CoV-2, necessitates novel therapeutic strategies.
• Ethnomedicinal plants, like those from the Paeonia genus, offer a potential source of antiviral compounds.
• The main protease of SARS-CoV-2 (3CLpro) is a key target for antiviral drug development.

Purpose of the Study:

• To evaluate Paeonia-derived compounds for drug-like properties and 3CLpro inhibitory potential using computational methods.
• To validate in silico findings through experimental determination of inhibitory concentrations.
• To assess the cytotoxicity of Paeonia root extracts on human cell lines.

Main Methods:

• Machine learning and deep learning tools were employed to screen Paeonia compounds.
• Molecular dynamics simulations were used to assess binding affinity to 3CLpro.
• Experimental determination of IC50 values for selected compounds against purified 3CLpro.
• Cytotoxicity assays were performed on human cell lines.

Main Results:

• Paeonia-derived compounds with favorable drug-like properties and predicted high binding affinity for 3CLpro were identified.
• Molecular dynamics simulations supported the binding affinity of these compounds.
• Experimental validation showed IC50 values of 9.33 µM for paeoniflorigenone and 43.94 µM for 3-O-methylquercetin.
• Paeonia root extracts exhibited minimal cytotoxicity.

Conclusions:

• In silico drug discovery methods, including machine learning, can effectively identify potential antiviral phytochemicals.
• Paeonia-derived compounds are promising candidates for further development as SARS-CoV-2 inhibitors.
• This study provides a proof-of-concept for using computational approaches to discover ethnomedicinal antivirals.