A Multi-Target Approach for Identifying Natural Inhibitors of Metabolic Syndrome Proteins (AMPK, PPAR-Γ, IRS-1) Using Molecular Docking and in-Silico Screening

Abstract

Metabolic syndrome is a complex cardiometabolic disorder driven by coordinated dysregulation of energy balance, insulin signaling, and lipid metabolism. Central regulatory proteins—including AMP-activated protein kinase (AMPK), peroxisome proliferator-activated receptor-γ (PPAR-γ), and insulin receptor substrate-1 (IRS-1) - represent interconnected molecular nodes within this network, yet current therapeutic strategies largely rely on single-target modulation. Natural products offer structurally diverse scaffolds capable of engaging multiple targets, providing a rational basis for multi-target drug discovery. In this study, a systematic in silico multi-target screening strategy was employed to evaluate phytochemicals derived from Hyptis verticillata against AMPK, PPAR-γ, and IRS-1. Molecular docking was performed using AutoDock Vina against crystallographic structures of AMPK and PPAR-γ, while a homology-modeled structure of IRS-1 was utilized. Binding affinities and protein–ligand interaction profiles were analyzed, followed by in silico assessment of drug-likeness and pharmacokinetic properties using SwissADME. Docking analyses revealed binding energies ranging from −3.8 to −8.6 kcal/mol across the targets. Dehydropodophyllotoxin, oleanolic acid, cadina-4,10(15)-dien-3-one, aromadendr-1(10)-en-9-one, and squalene consistently exhibited favorable binding across multiple proteins. Interaction mapping indicated that ligand stabilization was dominated by hydrophobic and π-alkyl interactions within functionally relevant binding regions. Pharmacokinetic profiling suggested acceptable oral drug-likeness for several top-ranking compounds, particularly oleanolic acid. Collectively, these findings identify H. verticillata phytochemicals as promising multi-target molecular scaffolds relevant to metabolic regulation. While the results reflect predicted molecular recognition rather than functional modulation, this work establishes a robust computational framework for prioritizing natural compounds for experimental validation and supports the utility of multi-target in silico approaches in metabolic syndrome drug discovery.