IN SILICO ANALYSIS OF FAT MASS OBESITY ASSOCIATED (FTO) GENE USING COMPUTATIONAL ALGORITHMS

Non-synonymous single nucleotide polymorphisms (nsSNPs) are used as biomarkers to disease susceptibility. In this study, nsSNPs in Fat Mass Obesity Associated (FTO) gene were screened for its functional impact on the protein. Firstly, SNPs was retrieved from dbSNP database. The computational algorithms namely, PolyPhen and PANTHER were used to identify the potentially deleterious nsSNPs. The following four SNPs, rs139000284, rs139577103, rs368490949 and rs373076420 are found to be most significant SNPs affecting the protein function. The gene network analysis using STRING algorithm showed that the following genes NPY, IGF2BP2, CDKAL1, TCF7L2, SLC30A8, TMEM18, HHEX, TCF7 and GNPDA2 are functionally associated with FTO gene. The homology models of the FTO proteins having the nsSNPs were predicted using SWISSMODELLER software. The stereochemical properties of the models were checked by Ramachandran plot using PROCHECK algorithm. Root Mean Squared Deviation (RMSD) was calculated by superimposing with native model. The free energy values for these mutant models were higher as compared to their native structure. The present study suggests that these four nsSNPs can be used as biomarkers to screen obesity susceptibility.