Computational approach for the identification of skeletal muscle-specific AMPK complex (α2β2γ1) activators for type-2 diabetes therapy

The present study aims to identify skeletal muscle-specific (α2β2γ1) AMP-activated protein kinase (AMPK) activators by utilizing bioinformatic tools. This study introduces a sensible computational strategy to systematically identify skeletal muscle-specific AMPK activators, thus providing a platform for developing novel therapeutics for Type-2 diabetes mellitus. Owing to the concerning systemic complications and off-target toxicity of the existing anti-diabetic drugs, our preliminary attempt involves structure-based virtual screening of a library of small molecules against the target using a combination of computational tools to reliably pinpoint molecules that could selectively interact with skeletal muscle-specific AMPK complex. The computationally screened library of selective small molecules was retrieved from the PubChem database against the crystal structure of full-length human α2β2γ1 AMPK complex (PDB ID: 6B2E with a resolution of 3.80 Å) from protein databank. The screening comprised a series of approaches, including molecular docking, molecular dynamics simulations, molecular mechanics/generalised born surface area, density functional theory calculations, pharmacokinetics, and toxicity prediction performed using a combination of relevant computational tools. In conclusion, this study has unveiled some interesting in silico hits that seem to have the potential to facilitate the development of a variety of plausible candidate skeletal muscle-specific (α2β2γ1) AMPK activators.