In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model.

IF 1.6 4区 生物学 Q4 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Iranian Journal of Biotechnology Pub Date : 2024-04-01 DOI:10.30498/ijb.2024.413800.3787
Eric Jonathan Maciel-Cruz, Luis Eduardo Figuera-Villanueva, Liliana Gómez-Flores-Ramos, Rubiceli Hernández-Peña, Martha Patricia Gallegos-Arreola
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Abstract

Background: In-silico analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.

Objective: To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online in-silico (IS) tools with AURKA gene as a model.

Materials and methods: We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using AURKA gene as model. We predicted a protein model and analyzed 209 out of 64,369 AURKA variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.

Results: Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.

Conclusion: This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.

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利用在线工具预测致病性错义变异的硅学方法:以 AURKA 基因为模型
背景:在确定潜在的致病性单核苷酸变异方面,体内分析提供了一种快速、简单且无需成本的方法:以 AURKA 基因为模型,利用免费的在线硅学(IS)工具,提出一种简单、相对快速的变异致病性预测方法:我们的目的是以AURKA基因为模型,提出一种利用计算分析预测高致病性变异体的方法。我们预测了一个蛋白质模型,并分析了从 Ensembl 数据库中获得的 64,369 个 AURKA 变异中的 209 个。我们使用生物信息学工具预测致病性。结果通过VarSome网站进行了比较,该网站包括自己的致病性评分和美国医学遗传学会(ACMG)的分类:在分析的 209 个变异中,16 个被认为具有致病性,13 个位于催化结构域。最常见的蛋白质变化是氨基酸的大小和疏水性修饰。脯氨酸和甘氨酸氨基酸置换是最常见的致病性变化。这些生物信息学工具预测了功能性变化,如蛋白质的上调或下调、分子相互作用的增加或减少以及蛋白质的结构修饰。与 ACMG 分类相比,16 个变异中有 10 个被认为可能是致病性的,其中 10 个变异中有 7 个是脯氨酸/甘氨酸取代:结论:这种方法可以快速、无成本地进行大量变异筛选,以确定具有致病潜力的变异,并进行进一步的关联和/或功能研究。
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来源期刊
Iranian Journal of Biotechnology
Iranian Journal of Biotechnology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
CiteScore
2.60
自引率
7.70%
发文量
20
期刊介绍: Iranian Journal of Biotechnology (IJB) is published quarterly by the National Institute of Genetic Engineering and Biotechnology. IJB publishes original scientific research papers in the broad area of Biotechnology such as, Agriculture, Animal and Marine Sciences, Basic Sciences, Bioinformatics, Biosafety and Bioethics, Environment, Industry and Mining and Medical Sciences.
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