Bioinformatics tool in Identification of the Structural and Functional Impact Of ACE Isoform 1 precursor gene.

Abstract

Background: The genetic variations in human genome causes considerable in the phenotype which is influenced by single nucleotide polymorphism. It is very challenging to determine which SNP in a candidate gene is responsible for a given phenotype, and requires testing hundreds or thousands of SNPs. The SNPs are utilized to map the susceptibility of genes involved in complex diseases and to connect the genetic variants that determine an individual's reaction to different medications. The hardest part of the mapping is deciding which set of SNPs to use. Only those SNPs with functional significance may be included in the set of SNPs selected for a given study after screening. One such prediction tool that helps to distinguish between SNPs with functional significance and neutral SNPs is the tool called "Bioinfor-matics.". One of the microvascular complications of diabetes mellitus is diabetic nephropathy. As the nephropathy advances, the patients depend on the renal replacement therapy. Angiotensin converting enzyme is a part of Renin-angiotensin system that plays an important role in maintaining the blood pressure and renal hemodynamics.

Aim: To analyze and extract the ACE isoform 1 precursor gene's functional SNP by the bioinformatic tool and analysis of ACE rs267604983 gene by SIFT and PROVEAN tool and performing HOPE modelling.

Methods: SIFT and PROVEAN bioinformatics tools were applied to extract the functional SNP's of ACE isoform precursor 1 gene.

Results: The database yielded about 9,680 single nucleotide polymorphisms. Coding variations were 100%, according to SIFT analysis of the ACE precursor gene. 94% of those projected were met. 30% were destructive, and were tolerated. Merely 6% were synonymous, while the remaining 94% were not. According to PROVEAN, 25% of the samples were harmful and 65% were tolerable.

Conclusion: In conclusion, new information about the complexities of diabetic nephropathy may be revealed by combining in silico analysis with wet laboratory research. For those who are at risk of diabetic nephropathy, customized medicine techniques and targeted medicines may become possible if the predictions made by bioinformatics tools match the results of experiments.