From sequence analysis of DPP-4 to molecular docking based searching of its inhibitors

Screening of potential antidiabetic phytochemicals from Gongronema latifolium leaf against therapeutic targets of type 2 diabetes mellitus: multi-targets drug design

Diabetes mellitus (DM) is the most predominant group of metabolic disorders wreaking havoc on the wellbeing of man, with type 2 diabetes mellitus (type 2 DM) accounting for most DM related cases. This study, hence, investigated the antidiabetic potential of Gongronema latifolium leaf fractionated compounds against proteins implicated in different molecular pathways related to the onset and progression of type 2 DM. A total of fifteen proteins that can act as type 2 DM therapeutic targets were identified from the literature and downloaded/modelled using respective repositories. After docking the compounds with the fifteen proteins, glycogen synthase kinase 3 beta (GSK 3β), glucagon-like peptide-1 receptor (GLP-1R) and human aldose reductase were chosen as the ideal targets due to their high binding affinities with the compounds. Subsequent in silico analysis like binding free energy, ADMET predictions using different servers, and machine-learning predictive models (QSAR) using kernel partial least square regression were employed to identify promising compounds against the three targets. The eleven identified compounds (Luteonin, Kampferol, Robinetin, Gallocatechin, Baicalin, Apigenin, Genistein, Rosmaric acid, Chicoric acid and Naringenin) formed stable complexes with the proteins, showed moderation for toxicity, drugability, GI absorptions and drug-drug interactions, though structure modifications may be needed for lead optimization. The predictive QSAR models with reliable correlation coefficient (R2) showed the potency of the compounds to act as inhibitors (pIC50) of aldose reductase and GSK 3β, and act as agonists (pEC50) of GLP-1R. Thus, this study experimental framework can be used to design compounds that can modulate proteins related to type 2 DM without inducing off-target effects.