Multi-conformational frame from molecular dynamics as a structure-based pharmacophore model for mapping, screening and identifying ligands against PPAR-γ: a new protocol to develop promising candidates

Abstract

Despite intensive research on clinical and molecular factors, the development of antidiabetic drugs in the last few decades is decelerating and as a result, the number of drugs approved by the US FDA is reduced. Hence, there is a persistent need for the innovative development of novel anti-diabetic drugs. Recent studies have provided ample proof that the peroxisome proliferator-activated receptor gamma (PPARγ), a ligand-activated transcription factor and its co-activator PGC-1 alpha may serve as good candidates for the treatment of several metabolic disorders. Therefore, in this study, 50 ns molecular dynamics (MD) simulations of the ligand-receptor complex were carried out and the most populated cluster of rosiglitazone bound to crucial amino acids during dynamics studies were selected to generate multi-conformation frame and further dynamic pharmacophore models. Finally, three pharmacophore models were generated, and 10 hits were retrieved as final lead candidates by virtual screening of ZINC database and molecular docking. The study reveals that the amino acids Met364, Lys367, His449, Leu453, Leu469, and Tyr473 play a crucial role in the binding of the compounds at the active site of PPARγ and the selected compounds from the ZINC database showed promising binding as compared to rosiglitazone. Further, ADMET studies were carried out to define the pharmacokinetic properties of promising PPARγ ligand candidates. Communicated by Ramaswamy H. Sarma