Prediction of Molecular Targets for Anthraquinone and Its Analogs for Treatment of Good Pasteur Syndrome - V Shivaranjani (International Journal of Bioinformatics and Computational Biology, 2023)
Objective: In order to find prospective molecular targets for the treatment of Good Pasteur Syndrome (GPS), a rare autoimmune disease that affects the kidneys and other organs, computational methods and network pharmacology were applied in this work. The goal of the study is to identify particular human proteins that might interact with anthraquinone and its analogues as well as to uncover potential mechanisms of action by which these drugs might treat GPS. Method: The current study's purpose was to employ computational methodologies to evaluate the efficiency of anthraquinone and its analogs against good pasteur syndrome. The IMPPAT database is used to retrieve potential ligands. While known target proteins associated with GPS were retrieved via the GeneCards database and predicted target proteins related to AQ were screened through the STITCH and TargetNet databases. STRING database was used to construct a protein-protein interaction network. Gene ontology pathway analysis done in ShinyGo 0.76.3 database. The BIOVIA Discovery Studio Visualizer and the virtual screening tool PyRx were used to systematically perform molecular docking.By using ADMETlab 2.0 pharmacological studies were performed. Results: The results from this study showed that these anthraquinone and its analogs have best binding affinity towards targeted proteins and these targets are involved in key pathways related to inflammation, oxidative stress, and immune regulation, which are known to be dysregulated in GPS. Conclusion: These findings may contribute to the development of innovative therapeutic drugs that can specifically target these particular biochemical pathways, thereby resulting in more potent and well-tolerated treatments for this difficult disease.
Keywords: anthraquinone; autoimmune; binding affinity; good Pasteur syndrome; molecular docking; network pharmacology