Abstract
BACKGROUND: PPAR-γ has an integrative role in the management of insulin resistance; ligands of this receptor have emerged as potent insulin sensitizers and may modulate proteins involved in the pathogenesis of diabetes mellitus. Hence the present study is aimed to identify PPAR-γ modulators from the plant Cassia glauca and predict the ontology enrichment analysis utilizing various in-silico tools. METHODS: ChEBI database was used to mine the phytoconstituents present in the plant C. glauca, SwissTargetPrediction database was used to identify the targets, and scrutinizing of phytoconstituents modulating PPAR-γ was performed. Autodock4.0 was used to dock phytoconstituent ligands with the target PPAR-γ. Multiple open-source databases and in-silico tools were utilized to predict the drug-likeness characters and predict side effects of the phytoconstituents modulating PPAR-γ and STRING database was used to construct a network between the modulated genes. RESULTS: Twenty-four phytoconstituents were identified from the plant Cassia glauca from which four were found to modulate PPAR-γ, sennoside was predicted to have the greatest drug-likeness score and a significantly less side effect whereas diphenyl sulfone was predicted to show hepatotoxicity with the greatest pharmacological activity of 0.815. [epicatechin-(4beta- > 8)]5-epicatechin showed the lowest binding affinity with target PPAR-γ i.e. -8.6 kcal/mol and possessing a positive drug-likeness score with no side effect data. CONCLUSION: Bioctives were found free from probable side effects leaving out diphenyl sulfone having a prediction of hepatotoxicity, the anti-diabetic property of the plant may be due to the presence of [epicatechin-(4beta- > 8)]5-epicatechin which needs further validation by in-vitro and in-vivo protocols.