Comprehensive Computational Analysis Revealed Seven Novel Mutations in Human Insulin gene

Abstract

Background: Insulin gene mapped on chromosome 11p15.5 consists of 3 exons, which translated to 51 residues of small globular protein secreted from the secretory granules in β-cells of the pancreatic gland. In this study, we used various computational approaches to identify nsSNPs, which probably be deleterious to the structure and/or function of insulin protein that might be associated with different disease pathophysiology. Methods: The data on human insulin gene retrieved from dbSNP/NCBI. Three functional analysis tools SIFT, Polyphen-2, Provean used to predict whether the effect of retrieved SNPs on the biological function of Insulin based on sequence homology, position-specific independent count scores and the impact of amino acid substitution on protein 3D structure. Then the shortlisted SNPs tested by three disease-associated SNPs predicting tools; Predictor of human Deleterious Single Nucleotide Polymorphisms, Pathogenic mutation prediction and SNPS&GO. Then the disease-causing SNPs analyzed to check protein stability analysis using I-mutant 2.0, and we used RaptorX server for homology modeling and its visualization analysis applied by Chimera through entering the native wild type protein structure and the mutant protein residues as input inquiry. Results: After retrieval of SNPs from the NCBI database, 130 SNPs classified as missense SNPs. From functional analysis software, 60 SNPs were predicted to be deleterious then they analyzed by disease – related software resulted on 28 SNPs, which checked for protein stability, and the final analysis revealed seven novel SNPs that decreased protein stability. Conclusion: These seven novel mutations within the proinsulin gene will give a clue for their effect on the biological function of insulin and might contribute on developing new biomarkers that will used on therapeutic and diagnostic area of different diseases.