Calculating Glycoprotein Similarities From Mass Spectrometric Data.
Calculating Glycoprotein Similarities From Mass Spectrometric Data.
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DOI：
10.1074/mcp.r120.002223
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Zaia J
中科院分区：
文献类型：
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作者：
Hackett WE;Zaia J
复杂的蛋白质糖基化通过分泌途径中的生物合成步骤发生，从而产生结构和功能的宏观和微观异质性。所有生命形式都需要糖基化，使蛋白质与结合伙伴的相互作用多样化并适应，从而支撑细胞表面、细胞周和细胞外环境的相互作用。由于这些生物效应是由结构和功能的异质性引起的，因此有必要测量它们的变化，作为理解自然的一部分。然而，蛋白质组学背后的假设，即翻译后修饰是可以使用基因组作为模板进行建模的离散添加，通常并不适用于蛋白质糖基化。相反，有必要量化每个糖位点的糖基化分布，并将这些信息聚合到给定生物系统中存在的成熟糖蛋白群体中。迄今为止，用于指定单糖基化肽的质谱方法已经很成熟。但有必要准确量化糖基化异质性，以衡量生物过程中发生的变化。任务是尽可能准确地量化糖基化肽的形式，然后应用适当的生物信息学算法来计算微观和宏观相似性。在这篇综述中，我们总结了当前用于解决糖蛋白相似性问题的蛋白质定量方法。使用 HCD LC-MS 可以明确鉴定单糖基化肽。为了计算糖蛋白相似性，量化所有糖肽糖型。糖蛋白相似度可以使用 Tanimoto 系数计算。相似性计算需要糖蛋白组学 LC-MS 数据的高重现性。为了了解糖蛋白在生物过程中的作用，有必要量化各个位点和整个分子糖基化发生的变化。糖蛋白糖基化本质上是异质的，这意味着必须量化每个糖位点的糖型分布，以便为分子相似性的计算提供信息。我们回顾了从糖蛋白质组学数据确定糖蛋白分子相似性的分析和统计方法。
Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function. Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature. Quite often, however, the assumption behind proteomics that posttranslational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation. Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system. To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established. But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes. The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities. In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem. Singly glycosylated peptides can be identified unambiguously using HCD LC-MS. For calculation of glycoprotein similarity, quantify all glycopeptide glycoforms. Glycoprotein similarities can be calculated using the Tanimoto coefficient. Similarity calculations require high reproducibility of glycoproteomics LC-MS data. To understand the roles of glycoproteins in biological processes, it is necessary to quantify the changes that occur to glycosylation at individual sites and to the whole molecule. That glycoprotein glycosylation is inherently heterogeneous means that the distribution of glycoforms at each glycosite must be quantified in order to inform calculation of molecular similarities. We review analytical and statistical methods for determining glycoprotein molecular similarities from glycoproteomics data.