Author/Authors :
Samadian, Esmaeil Department of Molecular Medicine - Golestan University of Medical Sciences , Khosravi, Ayyoob Department of Molecular Medicine - Golestan University of Medical Sciences , Gharae, Roghaye Laboratory Sciences Research Center - Golestan University of Medical Sciences , Mir, Mostafa Department of Clinical Biochemistry - Babol University of Medical Sciences , Sajjadi, Ahmad Reference Laboratory - Golestan University of Medical Sciences , Mohammad Abadi, Fahimeh Department of Clinical Biochemistry - Gorgan University of Medical Sciences , Hashemi, Nader Department of Clinical Biochemistry Faculty of medicine - Gorgan University of Medical Science , Alijanpour, Sahar department of medical genetics - Golestan university of medical sciences , Joshaghani, Hamid Reza Laboratory Sciences Research Center - Golestan University of Medical Sciences
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.
