In Silico analysis of missense single nucleotide polymorphisms in human GATA4 Gene

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 436

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شناسه ملی سند علمی:

CIGS15_525

تاریخ نمایه سازی: 13 بهمن 1398

چکیده مقاله:

Introduction: GATA4 gene encodes a member of the GATA family of zinc-finger transcription factors. This protein is thought to regulate genes involved in embryogenesis, myocardial differentiation and function, and normal testicular development. Mutations in this gene have been associated with cardiac septal defects. Protein structure and dynamics are an important part of understanding molecular bases of complex biological processes and play an important role in computational biology. There are many mutations in coding regions. All of these mutations were associated with Atrial Septal Defect (ASD). The functional significance of observed GATA4 mutations was analyzed using PROVEAN, PMut, and PYMOL software. Method: We identified three missense single nucleotide polymorphisms in the GATA4 gene (rs387906771, rs387906772, and rs56298569) using Ensembl database and then analyzed their effect on the protein structure using PyMOL software and PROVEAN and PMut databases.Results: Validation results showed that missense single nucleotide polymorphisms change the polar groups, interaction patterns, and length of hydrogen bonds. rs387906771, rs387906772, and rs56298569 lead to Thr280Met, Met310Val, and Gln316Glu. According to PyMOL, all these SNPs caused to change hydrogen bonds length in protein structure. The results of the variants in the PROVEAN predict scores below -2.5. PMut showed all of these SNPs with a high percentage of 85% pathogenic.Conclusions: According to ROVEAN database, all scores less than -2.5 were considered deleterious. PMut expresses the degree of SNP s pathogenicity by percentage. Although in silico studies have contributed greatly to the progress of studies, it needs experimental researches to confirm the results.

کلیدواژه ها:

GATA4 ، missense single nucleotide polymorphisms ، PyMOL software ، PMut database ، PROVEAN database

نویسندگان

Mojgan Rezaii Adriani

Department of Biology, Faculty of science, Yazd University, Yazd, Iran.

Mehri Khatami,

Department of Biology, Faculty of science, Yazd University, Yazd, Iran.

Mohammad Mehdi Heidari

Department of Biology, Faculty of science, Yazd University, Yazd, Iran.