Optimized Structure-based Methodology for Studying PPARγ Partial Agonists

Abstract

The peroxisome proliferator-activated receptor (PPAR) γ is a master regulator of the lipid and glucose metabolism, and thus is a valuable drug target. Since its full activation is accompanied by a number of adverse effects, researchers focus on discovery of novel compounds with ligand-receptor interaction patterns of PPARγ partial agonists. Molecular modelling is an appropriate way to achieve this goal. In this study we aimed at optimization of the docking algorithm for structure-based investigation of PPARγ partial agonists. A dataset with structures and activities of PPARγ partial agonists was constructed. A comparative study of different scoring functions’ performance was conducted by redocking the partial agonists’ structures selected from experimentally resolved 3D structures of PPARγ protein-ligand complexes. The docking protocols’ performance regarding pose scoring, reproducibility and interpretability in the context of the collected activity data was estimated. An optimized docking protocol was developed to successfully correlate the docking scores of the studied compounds with their experimentally derived activity values and to provide the best matching degree with their experimental binding modes. Overall, these results could be useful for further molecular modelling studies of novel PPARγ partial agonists by selection of reliable docking poses to predict their binding mode and for ranking them in respect to their agonistic activity using the calculated docking scores.

Keywords

• PPARγ
• Partial agonists
• Docking
• Optimization