Volume 10, Issue 3 (May-Jun 2016 2016)                   mljgoums 2016, 10(3): 1-5 | Back to browse issues page


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Samadian E, Khosravi A, Gharae R, Mir M, Sajjadi S A, Mohammad Abadi F, et al . Computational Prediction of the Effects of Single Nucleotide Polymorphisms of the Gene Encoding Human Endothelial Nitric Oxide Synthase. mljgoums 2016; 10 (3) :1-5
URL: http://mlj.goums.ac.ir/article-1-856-en.html
1- Department of Molecular Medicine
2- Laboratory Sciences Research Center
3- Department of Clinical Biochemistry
4- Reference Laboratory
5- department of medical genetics, Faculty of advanced medical technologies, Golestan university of medical sciences, Gorgan, Iran
6- Laboratory Sciences Research Center, Golestan University of Medical Science, Gorgan, Iran
Abstract:   (12070 Views)

ABSTRACT

          Background and Objective: Genetic variations in the gene encoding endothelial nitric oxide synthase (eNOS) enzyme affect the susceptibility to cardiovascular disease. Identification of the way these changes affect eNOS structure and function in laboratory conditions is difficult and time-consuming. Thus, it seems essential to perform bioinformatics studies prior to laboratory studies to find  the variants that are more important. This study aimed to predict the damaging effect of changes in the coding region of eNOS using homology- and structure-based algorithms (SIFT and PolyPhen).

           Methods: First, the single nucleotide polymorphisms in the coding region (cSNPs) of the human eNOS gene were extracted from dbSNP. Resulting amino acid changes were reported as primary data required for the study. Then, position and type of amino acid changes along with the complete amino acid sequence were separately entered into the SIFT and PolyPhen tools for analysis.

         Results: Of 144 single nucleotide changes, 38 changes by the SIFT, 47 changes by the PolyPhen and 18 amino acid substitutions by both tools were predicted as damaging.

          Conclusion: It is predicted that 18 amino acid changes may have damaging phenotypic effects on the structure of the eNOS enzyme that may affect its performance by potentially affecting the enzyme’s various functional regions. Therefore, computational prediction of potentially damaging nsSNPs and prioritizing amino acid changes may be useful for investigating protein performance using targeted re-sequencing and gene mutagenesis experiments.

        

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Research Article: Original Paper |
Received: 2016/08/21 | Accepted: 2016/08/21 | Published: 2016/08/21 | ePublished: 2016/08/21

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.