Refinement of the Docking Component of Virtual Screening for PPARgamma Therapeutics Using Pharmacophore Analysis and Molecular Dynamics

Exploration of peroxisome proliferator-activated receptor-gamma (PPARgamma) as a drug target holds applications for treating a wide variety of chronic inflammation-related diseases. Type 2 diabetes (T2D), which is a metabolic disease influenced by chronic inflammation, is quickly reaching epidemic proportions. Although some treatments are available to control T2D, more efficacious compounds with fewer side effects are in great demand. Drugs targeting PPARgamma typically are compounds that function as agonists toward this receptor, which means they bind to and activate the protein. Identifying compounds that bind to PPARgamma (i.e. binders) using computational docking methods has proven difficult given the large binding cavity of the protein, which yields a large target area and variations in ligand positions within the binding site. We applied a combined computational and experimental concept for characterizing PPARgamma and identifying binders. The goal was to establish a time- and cost-effective way to screen a large, diverse compound database potentially containing natural and synthetic compounds for PPARgamma agonists that are more efficacious and safer than currently available T2D treatments. The computational molecular modeling methods used include molecular docking, molecular dynamics, steered molecular dynamics, and structure- and ligand-based pharmacophore modeling. Potential binders identified in the computational component funnel into wet-lab experiments to confirm binding, assess activation, and test preclinical efficacy in a mouse model for T2D and other chronic inflammation diseases. The initial process used provided alpha-eleostearic acid as a compound that ameliorates inflammatory bowel disease in a pre-clinical trial. Incorporating pharmacophore analyses and binding interaction information improved the method for use with a diverse ligand database of thousands of compounds. The adjusted methods showed enrichment for full agonist binder identification. Identifying lead compounds using our method would be an efficient means of addressing the need for alternative T2D treatments.