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Structure-based Comparative Analysis and Prediction of N-linked Glycosylation Sites in Evolutionarily Distant Eukaryotes

机译:基于结构的比较分析和预测远距离真核生物中N-联糖基化位点。

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摘要

The asparagine-X-serine/threonine (NXS/T) motif, where X is any amino acid except proline, is the consensus motif for N-linked glycosylation. Significant numbers of high-resolution crystal structures of glycosylated proteins allow us to carry out structural analysis of the N-linked glycosylation sites (NGS). Our analysis shows that there is enough structural information from diverse glycoproteins to allow the development of rules which can be used to predict NGS. A Python-based tool was developed to investigate asparagines implicated in N-glycosylation in five species: Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana and Saccharomyces cerevisiae. Our analysis shows that 78% of all asparagines of NXS/T motif involved in N-glycosylation are localized in the loop/turn conformation in the human proteome. Similar distribution was revealed for all the other species examined. Comparative analysis of the occurrence of NXS/T motifs not known to be glycosylated and their reverse sequence (S/TXN) shows a similar distribution across the secondary structural elements, indicating that the NXS/T motif in itself is not biologically relevant. Based on our analysis, we have defined rules to determine NGS. Using machine learning methods based on these rules we can predict with 93% accuracy if a particular site will be glycosylated. If structural information is not available the tool uses structural prediction results resulting in 74% accuracy. The tool was used to identify glycosylation sites in 108 human proteins with structures and 2247 proteins without structures that have acquired NXS/T site/s due to non-synonymous variation. The tool, Structure Feature Analysis Tool (SFAT), is freely available to the public at .
机译:天冬酰胺-X-丝氨酸/苏氨酸(NXS / T)基序(其中X是脯氨酸以外的任何氨基酸)是N-联糖基化的共有基序。糖基化蛋白的大量高分辨率晶体结构使我们能够进行N-连接糖基化位点(NGS)的结构分析。我们的分析表明,来自各种糖蛋白的结构信息足够多,可以发展可用于预测NGS的规则。开发了一个基于Python的工具来研究与以下5种物种的N-糖基化有关的天冬酰胺:智人,小家鼠,果蝇,拟南芥和酿酒酵母。我们的分析表明,参与N-糖基化的NXS / T基序的所有天冬酰胺中有78%位于人蛋白质组中的loop / turn构象中。对于所有其他检查物种,发现相似的分布。对未知被糖基化的NXS / T母题及其反向序列(S / TXN)的发生情况进行的比较分析显示,二级结构元素之间的分布相似,这表明NXS / T母题本身与生物学无关。根据我们的分析,我们定义了确定NGS的规则。使用基于这些规则的机器学习方法,我们可以以93%的准确度预测特定位点是否会被糖基化。如果没有结构信息,该工具将使用结构预测结果,从而获得74%的准确性。该工具用于鉴定108个具有结构的人蛋白质中的糖基化位点,以及2247个由于非同义变异而没有结构而获得NXS / T位点的无结构蛋白。该工具称为“结构特征分析工具(SFAT)”,可从公众免费获得。

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