High-Throughput De Novo Glycan Sequencing

糖基化完成了重要的生理功能，包括蛋白质折叠、胚胎发生、细胞黏附、 病原体识别和免疫反应。糖基化所起的多方面作用源于 存在一系列的葡聚糖表位，其中微小的结构变化可以对 功能。此外，糖糖由许多密切相关的结构组成，它们的相对数量是确定的。 通过特定细胞和生长方式的新陈代谢条件。糖基化改变与许多疾病有关， 包括心血管、肺、神经和自身免疫性疾病，以及癌症。因此，有一个明确的 需要分析方法来快速识别和定量不同来源的糖类中的多种糖形式 健康和疾病状况。最后，由于无脚本的性质，不存在基因组预测的糖链数据库 糖的生物合成和新的糖结构的发现必须通过从头开始的方法来实现。 尽管基于串联质谱学的生物聚合物测序是最近 组学的快速发展，流行的碰撞激活解离方法往往不能提供足够的 MS2水平的葡聚糖结构细节，而MSN方法缺乏速度、灵敏度和量化 高通量糖糖分析的潜力。我们最近开发了一种电子激发解离。 (EED)方法，可以在MS/MS分析的单一阶段产生丰富的结构信息。然而，其影响 目前EED对糖组学研究的局限性在于其可获得性差，与在线糖链的偶联不足 分离方法，以及复杂的多糖EED串联质谱图的解释困难。 在这里，我们建议开发一种将EED和在线高效液相色谱(LC)相结合的综合方法 分离和一种新的生物信息学工具以实现高通量、从头开始和全面的糖链 人物刻画。我们将探索EED在分析各种衍生形式的多糖方面的潜力 其碎片化行为，并为生物信息学软件的开发建立碎片化规则。 我们将优化EED高效偶联到反相和多孔石墨炭LC的条件，以及 建立LC-EED-MS/MS同时表征和定量多聚糖混合物的方法。我们 将在Q-TOF仪器上实施EED，以改善其进入糖科学界的机会。最后，我们会 开发并严格测试一种快速准确的新型生物信息学软件的性能 根据串联的MS波谱确定每个多糖的结构。所提出的算法是完全不同的 从大多数现有的软件中，它不再仅仅依赖于糖苷和交叉环片段来进行拓扑 和链接分析，而是采用考虑各种类型的上下文的机器学习方法 以及与不同连接构型和结构相关联的光谱特征 图案。这种高通量的从头测序工具的可用性将产生巨大的影响 在许多生物医学研究领域，糖基化在几乎所有的生物途径中都起着关键作用。

Glycosylation fulfills important physiological functions, including protein folding, embryogenesis, cell adhesion, pathogen recognition, and immune response. The multifaceted roles glycosylation plays derive from the presence of a range of glycan epitopes, where a small structural variation can have a profound impact on functions. Further, a glycome consists of many closely related structures, with their relative amounts determined by metabolic conditions in a cell- and growth-specific manner. Altered glycosylation is linked to many diseases, including cardiovascular, pulmonary, neurological and autoimmune disorders, and cancer. Thus, there is a clear need for analytical methods that can rapidly identify and quantify the many glycoforms in a glycome from different health and disease states. Finally, no genome-predicted glycan database exists due to the unscripted nature of glycan biosynthesis, and discovery of new glycan structures must be achieved by de novo methods. Although tandem mass spectrometry-based biopolymer sequencing has been the major catalyst to the recent rapid advance of 'omics, the prevailing collisionally activated dissociation method often fails to provide sufficient glycan structural detail at the MS2 level, whereas the MSn approach lacks the speed, sensitivity, and quantitative potential for high-throughput glycome analysis. We have recently developed an electronic excitation dissociation (EED) method that can yield rich structural information in a single stage of MS/MS analysis. However, the impact of EED on glycomics research is currently limited by its poor accessibility, insufficient coupling to on-line glycan separation methods, and difficulty in interpretation of complex glycan EED tandem mass spectra. Here, we propose to develop an integrated approach that combines EED with on-line liquid chromatography (LC) separation and a novel bioinformatics tool to achieve high-throughput, de novo, and comprehensive glycome characterization. We will explore the potential of EED for analysis of glycans in various derivatized forms, study their fragmentation behaviors, and establish fragmentation rules for the development of bioinformatics software. We will optimize conditions for efficient coupling of EED to reversed-phase, and porous graphitic carbon LC, and develop an LC-EED-MS/MS approach for simultaneous characterization and quantitation of glycan mixtures. We will implement EED on a Q-TOF instrument to improve its access to the glycoscience community. Finally, we will develop and rigorously test the performance of a novel bioinformatics software that can rapidly and accurately determine each glycan's structure from its tandem MS spectra. The proposed algorithm is fundamentally different from most existing software, in that it no longer relies solely on glycosidic and cross-ring fragments for topology and linkage analysis, but rather adopts a machine learning approach that considers the contexts of various types of fragment peaks, and the spectral features associated with different linkage configurations and structural motifs. The availability of such a high-throughput, de novo glycan sequencing tool will have an immense impact on many biomedical research fields, as glycosylation plays critical roles in almost all biological pathways.