In silico approach to identify non-synonymous SNPs in human obesity related gene, MC3R (melanocortin-3-receptor). (April 2017)

• Record Type: Journal Article
• Authors: Singh, Rajan Kumar; Mahalingam, Kulandaivelu
• Is Part Of: Computational biology and chemistry. Volume 67(2017)
• Page Start: 122
• Page End: 130
• Publication Date: 2017-04
• Subjects: MC3R -- Obesity -- Energy homeostasis -- SNPs -- Homology modeling; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.12.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Computationally eight nsSNPs were potentially deleterious and significant in MC3R gene. These nsSNPs showed higher scores with all the computational tools used in this study. Homology modeling of rs74315393, rs368205448 and rs143321797 were performed. The mutant models showed higher energy and RMSD values. The nsSNP rs368205448 (Asp121Tyr) was present in the ligand binding site of the MC3R protein. Abstract: The melanocortin-3-receptor (MC3R) is a novel gene candidate for human obesity, which involved in controlling the energy homeostasis and food intake behavior. The main aim behind this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in obesity related gene MC3R by using six computational tools viz., PolyPhen, I-Mutant, PROVEAN, SIFT, PANTHER and PhD-SNP. In our study, we predicted eight nsSNPs i.e., rs74315393 (Ile146Asn), rs368205448 (Asp121Tyr), rs143321797 (Phe45Ser), rs17847261 (Cys274Ser), rs144166442 (Pro257His), rs370533946 (Leu224Pro), rs371354428 (Pro72Leu) and rs373708098 (Gly249Ser) found to be potentially deleterious. The functional impact of three nsSNPs i.e., rs74315393, rs368205448 and rs143321797 have already been validated experimentally in the context of human obesity. Moreover, Homology modeling and structural analysis were carried out for already experimentally validated nsSNPs i.e., rs74315393, rs368205448 and rs143321797 to check the stability of predicted models. The mutant models showed higher energy and RMSD (Root mean square deviation) values. In addition, FTSite server predicted one nsSNP i.e., rs368205448 (Asp121Tyr) out of eight identified nsSNPs found in the MC3R protein binding site. Thus, the present computational study may suggest that predicted nsSNPs possibly be a better drug target and contribute to the treatment and better understanding of human obesity.