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对潜在具有病理重要性的血友病 A 和血友病 B 的有害氨基酸取代进行计算机分析。

In silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B.

机构信息

School of Bio Sciences and Technology, VIT University, Vellore, Tamil Nadu, India.

出版信息

J Biomed Sci. 2012 Mar 16;19(1):30. doi: 10.1186/1423-0127-19-30.

Abstract

BACKGROUND

In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in F8 and F9 gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of F8 and F9 are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of F8 and F9 from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.

METHODS

We performed an in silico analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.

RESULTS

Out of 510 nsSNPs in F8, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In F9, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both F8 and F9.

CONCLUSIONS

The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These in silico tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.

摘要

背景

在这项研究中,我们没有采用当前的生化方法,而是通过计算方法和数据库中的信息来检测 F8 和 F9 基因中有害氨基酸取代对蛋白质结构和功能的影响。F8 和 F9 的有害突变是导致血友病 A 和血友病 B 的原因,这是血液中最常见的凝血障碍遗传疾病。然而,将 F8 和 F9 中的有害变异与单个基因组中大量无功能变异区分开来是一项重大挑战。

方法

我们对有害突变及其蛋白质结构变化进行了计算机分析,以分析突变与疾病之间的相关性。根据基于经验的方法和基于支持向量机的方法,对有害 nsSNP 进行分类,以预测对蛋白质功能的影响。此外,我们构建了突变蛋白,并将其与天然蛋白进行比较,以分析蛋白质结构的稳定性。

结果

在 F8 中的 510 个 nsSNP 中,378 个 nsSNP(74%)被 SIFT 预测为“不耐受”,371 个 nsSNP(73%)被 PolyPhen 预测为“有害”,445 个 nsSNP(87%)被 I-Mutant2.0 预测为“稳定性降低”。在 F9 中,129 个 nsSNP(78%)被 SIFT 预测为不耐受,131 个 nsSNP(79%)被 PolyPhen 预测为有害,150 个 nsSNP(90%)被 I-Mutant2.0 预测为稳定性降低。总体而言,我们发现,I-Mutant 强调基于支持向量机的方法在预测 F8 和 F9 中的有害 nsSNP 方面优于 SIFT 和 PolyPhen。

结论

本研究构建的模型可用于预测基因调控中有害氨基酸取代及其功能,这将有助于进一步的基因型-表型研究以及药物遗传学研究。这些计算机工具虽然有助于提供有关突变性质的信息,但也可以作为第一道筛选,以确定值得进一步实验研究的取代,以用于其他引起凝血障碍的基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87d0/3361463/388864b83591/1423-0127-19-30-1.jpg

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