Targeting SARS-CoV2 spike glycoprotein: molecular insights into phytocompounds binding interactions – <i>in-silico</i> molecular docking
This study utilized small molecular characterization and docking study to evaluate the binding affinity of seven antiviral phytocompounds with the SARS CoV-2 variants (SARS-CoV-2 Spike Glycoprotein, SARS-CoV-2 Spike Protein Variant in 1-RBD, Alpha Variant SARS-CoV2- Spike Protein). The results revealed that five of seven compounds, possesses excellent drug lead property reveled through in-silico ADMET analysis. In addition, six of seven except D-Glucosamine, exhibited excellent binding affinity. Six ligands possess significant binding affinity towards SARS-CoV-2 variants 6VXX, 7LWV and 7R13, which is certainly greater than Remdesivir. Fagaronine found to be the best drug candidate against SARS-CoV-2 variants, It was found that -7.4, -5.6 and -6.3 is the docking score respectively. Aranotin, Beta aescin, Gliotoxin, and Fagaronine formed hydrogen bonds with specific amino acids and exhibited significant binding interactions. These findings suggest that these phytocompounds could be promising candidates for developing antiviral therapies against SARS-CoV-2. Moreover, the study underscores the importance of molecular docking in understanding protein-ligand interactions and its role in drug discovery. The documented pharmacological properties of these compounds in the literature further support their potential therapeutic relevance in various diseases.
Authors
- S. Elavarasi
- P. Karuppannan
- Kamatchi Sundara Saravanan
- G. Revathi (ORCID: https://orcid.org/0000-0001-9419-2637)
- M. Ashokkumar (ORCID: https://orcid.org/0000-0003-3040-4295)
- C. Muthusamy
- A. Ram Kumar
Institutions
- University College for Women (IN)
- Memorial Foundation (US)
- Holy Cross College (GB)
- Hindustan Institute of Technology and Science (IN)
- Saveetha University (IN)
Publication Details
- Journal
- Journal of Biomaterials Science Polymer Edition
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1080/09205063.2024.2399395
- Citations
- 5
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.81