In silico approach to identify non-synonymous SNPs with highest predicted deleterious effect on protein function in human obesity related gene, neuronal growth regulator 1 (NEGR1)

Abstract

Neuronal growth regulator 1 (NEGR1) is a candidate gene for human obesity, which encodes the neural cell adhesion and growth molecule. The aim of the current study was to recognize the non-synonymous SNPs (nsSNPs) with the highest predicted deleterious effect on protein function of the NEGR1 gene. We have used five computational tools, namely, PolyPhen, SIFT, PROVEAN, MutPred and M-CAP, to predict the deleterious and pathogenic nsSNPs of the NEGR1 gene. Homology modeling approach was used to model the native and mutant NEGR1 protein models. Furthermore, structural validation was performed by the PROCHECK server to interpret the stability of the predicted models. We have predicted four potential deleterious nsSNPs, i.e., rs145524630 (Ala70Thr), rs267598710 (Pro168Leu), rs373419972 (Arg239Cys) and rs375352213 (Leu158Phe), which might be involved in causing obesity phenotypes. The predicted mutant models showed higher root mean square deviation and free energy values under the PyMoL and SWISS-PDB viewer, respectively. Additionally, the FTSite server predicted one nsSNP, i.e., rs145524630 (Ala70Thr) out of four identified nsSNPs found in the NEGR1 protein-binding site. There were four potential deleterious and pathogenic nsSNPs, i.e., rs145524630, rs267598710, rs373419972 and rs375352213, identified from the above-mentioned tools. In future, further functional in vitro and in vivo analysis could lead to better knowledge about these nsSNPs on the influence of the NEGR1 gene in causing human obesity. Hence, the present computational examination suggest that predicated nsSNPs may feasibly be a drug target and play an important role in contributing to human obesity.