Evaluation of selected 3D virtual screening tools for the prospective identification of peroxisome proliferator-activated receptor (PPAR) γ partial agonists

Abstract

The peroxisome proliferator-activated receptor (PPAR) γ regulates the expression of genes involved in adipogenesis, lipid homeostasis, and glucose metabolism, making it a valuable drug target. However, full activation of the nuclear receptor is associated with unwanted side effects. Research therefore focuses on the discovery of novel partial agonists, which show a distinct protein-ligand interaction pattern compared to full agonists. Within this study, we employed pharmacophore- and shape-based virtual screening and docking independently and in parallel for the identification of novel PPARγ ligands. The ten top-ranked hits retrieved with every method were further investigated with external in silico bioactivity profiling tools. Subsequent biological testing not only confirmed the binding of nine out of the 29 selected test compounds, but enabled the direct comparison of the method performances in a prospective manner. Although all three methods successfully identified novel ligands, they varied in the numbers of active compounds ranked among the top-ten in the virtual hit list. In addition, these compounds were in most cases exclusively predicted as active by the method which initially identified them. This suggests, that the applied programs and methods are highly complementary and cover a distinct chemical space of PPARγ ligands. Further analyses revealed that eight out of the nine active molecules represent novel chemical scaffolds for PPARγ, which can serve as promising starting points for further chemical optimization. In addition, two novel compounds, identified with docking, proved to be partial agonists in the experimental testing.