Multi-conformation dynamic pharmacophore modeling of the peroxisome proliferator-activated receptor γ for the discovery of novel agonists

• Dynamic pharmacophore models were generated using structures of clustering result.
• The novel approach utilizing representative structures with two ligands was tried.
• Final candidates were selected by virtual screening and molecular docking simulation.
• Additional molecular dynamics simulation was performed to validate final leads.

Activation of the peroxisome proliferator-activated receptor γ (PPARγ) is important for the treatment of type 2 diabetes and obesity through the regulation of glucose metabolism and fatty acid accumulation. Hence, the discovery of novel PPARγ agonists is necessary to overcome these diseases. In this study, a newly developed approach, multi-conformation dynamic pharmacophore modeling (MCDPM), was used for screening candidate compounds that can properly bind PPARγ. Highly populated structures obtained from molecular dynamics (MD) simulations were selected by clustering analysis. Based on these structures, pharmacophore models were generated from the ligand-binding pocket and then validated to check the rationality. Consequently, two hits were retrieved as final candidates by utilizing virtual screening and molecular docking simulations. These compounds can be used in the design of novel PPARγ agonists.

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