HGDiscovery:一个在线工具,提供均质1,2-二氧合酶新变异的功能和表型信息

IF 2.7 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Current Research in Structural Biology Pub Date : 2022-01-01 DOI:10.1016/j.crstbi.2022.08.001
Malancha Karmakar , Vittoria Cicaloni , Carlos H.M. Rodrigues , Ottavia Spiga , Annalisa Santucci , David B. Ascher
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引用次数: 3

摘要

Alkaptonuria (AKU)是一种罕见的遗传性疾病,其特征是均质酸(HGA)在体内积累。受影响的个体缺乏分解HGA所需的酶的功能水平。均质化1,2-双加氧酶(HGD)基因的突变导致AKU,并导致功能性HGD水平不足,进而导致HGA水平过剩。虽然HGA会被肾脏迅速从体内清除,但长期来看,它会开始在各种组织中积累,尤其是软骨。随着时间的推移(很少在成年之前),它最终会改变受影响组织的颜色为石板蓝或黑色。在这里,我们报告了利用蛋白质结构信息对111个致病性和190个非致病性HGD错义突变进行综合突变分析。利用我们全面的基于图的签名方法,mCSM与基于序列的工具相辅相成,我们研究了每个突变对蛋白质稳定性、相互作用和进化保护的功能和分子后果。基于结构和序列的工具生成的分数用于训练有监督的机器学习算法,准确率为89%。利用经验分类器生成HGD错义突变的变异表型。所有这些信息都部署在名为HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/)的用户友好的免费web服务器上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural information. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, interaction and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/).

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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
33
审稿时长
104 days
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