Identification of molecular targets of potential antidiabetic drugs using prediction of activity spectra for substances and molecular docking

Ms. Neha Sharma


Context: Diabetes mellitus is not a solitary sickness yet is a gathering of metabolic issue influencing countless on the planet. It is essentially described by incessant hyperglycemia because of deformities in insulin discharge or insulin activity. It is predicated that the quantity of diabetes individual on the planet could reach up to 366 million by the year 2030. Even though the instances of diabetes are expanding step by step, aside from insulin and oral hypoglycemic medications, no other method for treatment has been effectively grown up until now. Objective: In the present study, an initiative is tried to delineate the usefulness of prediction of activity spectra for substances (PASS) online software and molecular docking technique for providing new molecular ways of predicting new antidiabetic drug targets of potential phytoconstituents. Materials and Methods: In the study, important phytoconstituents having reported in vitro and in vivo antidiabetic activities have been reviewed. Among them, few phytoconstituents were selected for presenting to PASS online software. Pa and Pi value was predicted for these phytoconstituents on different antidiabetic target sites. Based on PASS prediction, five phytoconstituents were selected for molecular docking study using AutoDock Vina 4.0. Three target sites which were dipeptidyl peptidase-4 (DPP-4), glucagon-like peptide-1 (GLP-1), and α glucosidase were selected for prediction of probable affinities of these 5 selected phytoconstituents. Result and Discussion: Among these five constituents, diosmin showed best binding affinity with DPP-4, GLP-1, and α glucosidase that was −10.2 kcal/mol, −8.3 kcal/mol, and −9.7kcal/mol, followed by kaempferol. Results of the present study can be utilized for designing of further in vitro and in vivo antidiabetic studies for these phytoconstituents. Conclusion: This study suggested the usefulness of these software in predicting the probable antidiabetic targets sites of potential antidiabetic phytoconstituents.

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