In-Silico Studies On Astilbin As A Potential Antioxidant Agent: A Multi-Faceted Computational Approach
Keywords:
Astilbin, Molecular Docking, Swiss Similarity, ADME, Toxicity Predictions
Abstract
Astilbin, a prominent flavonoid derived from the rhizomes of Smilax glabra, exhibits significant antioxidant properties. This study employs a comprehensive in-silico approach, encompassing Swiss Similarity studies, ADME and toxicity predictions, Cytoscape network analysis, and molecular docking studies, to elucidate the potential of astilbin as an effective antioxidant agent. Swiss Similarity analysis identified structurally similar compounds, facilitating the selection of promising candidates. ADME and toxicity predictions confirmed favourable pharmacokinetic properties and low toxicity. Cytoscape network analysis highlighted key protein interactions, and molecular docking studies demonstrated strong binding affinities of astilbin to antioxidant-related enzymes, with comparative analysis against the standard antioxidant ascorbic acid. These findings underscore the potential of astilbin in antioxidant therapy, warranting further experimental validation.
References
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2. Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: A web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014 Jul;42(Web Server issue)
3. Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules. Sci Rep. 2017 Mar 3;7:42717.
4. Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, Félix E, et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res. 2019 Jan 8;47(D1)
5. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A free tool to discover chemistry for biology. J Chem Inf Model. 2012 Jul 23;52(7):1757-68.
6. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018 Jan 4;46(D1)
7. BindingDB. BindingDB: The Binding Database. Available from: https://www.bindingdb.org/. Accessed 17/05/2024.
8. ADMET LAB 3.0. ADMET LAB 3.0: Predictive modeling for drug absorption, distribution, metabolism, excretion, and toxicity. Available from: https://admetlab3.scbdd.com/. Accessed 21/05/2024.
9. STRING. STRING: Functional protein association networks [Internet]. Available from: https://string-db.org/. Accessed 25/05/2024.
10. Cytoscape. Cytoscape: An Open-Source Platform for Complex Network Analysis and Visualization [Internet]. Available from: https://cytoscape.org/. Accessed 26/05/2024.
11. KEGG. Kyoto Encyclopedia of Genes and Genomes (KEGG) [Internet]. Available from: https://www.genome.jp/kegg/. Accessed 27/05/2024.
12. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1) Available from: https://academic.oup.com/nar/article/47/D1/D607/5198476. Accessed 28/05/2024.
13. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. GenomeRes. 2003;13(11):2498-504. Available from: https://genome.cshlp.org/content/13/11/2498.short. Accessed 29/05/2024.
14. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(D1) Available from: https://academic.oup.com/nar/article/40/D1/D109/2904084. Accessed 30/05/2024.
15. Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list. Nucleic Acids Res. 2006;34(suppl_1)Availablefrom: https://academic.oup.com/nar/article/34/suppl_1/D504/1130207.Accessed 1/05/2024.
16. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2(10):2366-82. Available from: https://www.nature.com/articles/nprot.2007.324. Accessed 28/05/2024.
17. MolSoft L.L.C. ICM-Pro User Manual. Available from: https://www.molsoft.com/manual/index.html
18. Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365-82.
19. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220(4598):671-80.
20. Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins. 2002;47(4):409-43.
21. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935-49.
Published
2023-12-20
How to Cite
Pratikkumar Gavval, Shrinivas Mohite, Sandeep Kane, Sonali Tambave, Pallavi Sutar, Vidya Dange, Avani Shewale, & Shailaja Kamble. (2023). In-Silico Studies On Astilbin As A Potential Antioxidant Agent: A Multi-Faceted Computational Approach. Revista Electronica De Veterinaria, 24(4), 357 - 365. Retrieved from https://www.veterinaria.org/index.php/REDVET/article/view/703
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