In silico prioritization of Indonesian medicinal plant compounds targeting insulin signalling proteins as potential candidates for type 2 diabetes research
Abstract
Abstract Type 2 diabetes mellitus (T2DM) is characterized by chronic hyperglycemia associated with impaired insulin signaling and insulin resistance. Identifying protein targets involved in insulin-related pathways is important for supporting early-stage discovery of candidate antidiabetic compounds. This study applied a k-dominant skyline query algorithm to prioritize T2DM-associated proteins, highlighting RAC-alpha serine/threonine-protein kinase (AKT1) and insulin (INS) as computationally relevant proteins within the analyzed interaction networks. Using molecular docking and molecular dynamics simulations, we evaluated the predicted binding profiles of bioactive compounds from Indonesian family medicinal plants, particularly Withangulatin-A from Physalis angulata. Withangulatin-A showed favorable predicted binding affinities to both AKT1 (−11.2 kcal/mol) and INS (−7.9 kcal/mol), with stronger docking scores than metformin under the same computational conditions. Molecular dynamics simulations indicated relatively stable protein–ligand complexes, supported by favorable predicted MM/GBSA binding free energy values. These findings suggest that Withangulatin-A may represent a promising computational hit for further investigation of insulin-signalling-related mechanisms in T2DM. Further biochemical, cellular, pharmacokinetic, toxicity, in vitro, and in vivo studies are required to validate these computational predictions.
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