Computational analysis of single nucleotide polymorphisms (SNPs) in PPAR gamma associated with obesity, diabetes and cancer.

The single nucleotide polymorphisms (SNPs) are the common genetic variations in human genomes and act as markers for molecular susceptibility of complex traits and diseases in humans. Amino acid variations in the non-synonymous SNPs (nsSNPs) in coding and non-coding regions affect the function/structure of the proteins. The Peroxisome proliferator-activated receptor gamma (PPARγ or PPARG) is a nuclear receptor that plays a significant role in lipid metabolism and insulin production and is associated with diabetes, obesity, and cancer. In this study, the PPARG sequence was retrieved from the NCBI database (dbSNP: NP_619726.2), and an analysis was done to predict the damaged/harmful mutated amino acids. We identified five mutated variants (C162S, R166W, Q286P, or Q314P and P467L), which were mostly expressed in cancer tissues and associated with insulin resistance and partial lipodystrophy. The identified mutations were induced, and the analysis of molecular dynamics simulation was established to determine the dynamic stability/flexibility of PPARG. The dynamic trajectories were analyzed by RMSD, RMSF, and Radius of Gyration (Rg) analysis; a vast difference was noticed in each of the protein structure when compared with the PPARG wild-type, and the mutations in PPARG impaired its functions, leading to more significant problems in humans.Communicated by Ramaswamy H. Sarma.