Recent Advances in the Analysis of Complex Glycoproteins.
Recent Advances in the Analysis of Complex Glycoproteins.
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DOI：
10.1021/acs.analchem.6b04343
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发表时间：
2017-01-03
影响因子：
7.4
通讯作者：
Novotny MV
中科院分区：
文献类型：
--
作者：
Gaunitz S;Nagy G;Pohl NL;Novotny MV
注释408传记408致谢409参考文献409糖缀合物的巨大结构多样性反映了它们在原核和真核系统中的无数生物功能。已知各种聚糖分子以许多一般和专门的方式参与微生物、真菌、植物和哺乳动物系统中几乎所有的调节途径。对糖基化过程的遗传和细胞组分的许多研究揭示了它们的核心重要性，从而将糖科学引导到现代生物医学研究的中心舞台。1，2最近的简短评论抓住了糖基化和聚糖-蛋白质相互作用在哺乳动物细胞生物学中的整体重要性; 3− 6国家研究理事会2012年向美国国家科学院提交的报告强调了发展糖科学及其支持工具的普遍重要性。7由于聚糖生物合成不直接受模板驱动的过程，因此需要有关聚糖类型及其分布的可靠结构和定量分析数据。因此，现代生物分析方法，技术和仪器，特别是质谱，对解决糖科学的奥秘变得越来越重要。正如下面部分强调的快速增加的出版账户数量所证明的那样，糖组学和糖蛋白质组学现在是快速增长的科学奋进。本文综述了近3年来分析糖科学方法和仪器的发展，特别是糖蛋白。虽然没有在这篇文章中具体介绍，许多这些方法也有其他重要类别的糖缀合物，如蛋白聚糖，糖脂（包括鞘糖脂，GSL）和多糖的分析中的应用。通过凝集素和聚糖微阵列探测聚糖-蛋白质和糖-糖相互作用的过程也将不在此讨论。8，9本综述还建立在先前对糖蛋白结构表征分析方法的全面综述的基础上，10包括对质谱技术11和其他仪器方面的深入描述。12− 14糖蛋白结构的极端复杂性和多样性继续要求新的方法来阐明它们。为此，质谱（MS）仍然是聚糖和糖肽结构表征的核心技术。仪器的改进和许多实验室获得可靠的商用仪器也推动了电离和碎裂技术以及选择性离子监测方面的新发展。由于同位素标记方法的新用途，定量而不仅仅是定性数据的采购已经取得了显着进展。近年来，离子迁移率/质谱（IM-MS）混合技术在糖蛋白表征问题上也有了大量的新应用。
Notes 408 Biographies 408 Acknowledgments 409 References 409 The enormous structural diversity of glycoconjugates mirrors their myriad biological functions in prokaryotic and eukaryotic systems. Various glycan molecules are known to participate in numerous general and specialized ways in virtually all regulatory pathways in microbes, fungi, plants, and mammalian systems. The many investigations into the genetic and cellular components of glycosylation processes are revealing their central importance and thereby are leading glycosciences on to the center stage of modern biomedical research. 1, 2 Recent short reviews capture the overall importance of glycosylation and glycan-protein interactions in mammalian cellular biology; 3− 6 the general importance of developing the glycosciences and its enabling tools is underscored in the 2012 report of the National Research Council to the US National Academies. 7 Since glycan biosynthesis is not directly subjected to a templatedriven process, solid structural and quantitative analytical data concerning glycan types and their distribution are needed. Therefore, modern bioanalytical methods, technologies, and instrumentation, in particular mass spectrometry, have become increasingly important to solving the mysteries of glycoscience. As documented by the rapidly increasing numbers of published accounts, in part highlighted below, glycomics and glycoproteomics are now fast-growing fields of scientific endeavor. This review focuses on the last 3 years of methodological and instrumental developments in analytical glycoscience with a particular focus on glycoproteins. Although not covered specifically in this article, many of these methods also have applications in the analysis of other important classes of glycoconjugates such as proteoglycans, glycolipids (including glycosphingolipids, GSLs), and polysaccharides. Processes to probe glycan− protein and sugar− sugar interactions through lectin and glycan microarrays also will not be discussed here. 8, 9 This review also builds upon prior comprehensive reviews of analytical approaches for the structural characterization of glycoproteins, 10 including an in-depth description of mass-spectrometric techniques 11 and other instrumental aspects. 12− 14The extreme complexity and diversity of glycoprotein structures continues to demand new processes for their elucidation. To this end, mass spectrometry (MS) continues to be the central technique in the structural characterization of glycans and glycopeptides. Instrumental improvements and the availability of reliable commercial instrumentation to numerous laboratories have also driven new developments in terms of ionization and fragmentation techniques and of selective ion monitoring. The procurement of quantitative and not only qualitative data has advanced markedly due to the novel uses of isotopic labeling methods. Recent years have also shown an abundance of new applications for ion mobility/mass spectrometry (IM-MS) hybrid techniques to the problems of glycoprotein characterization.
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影响因子：
2.9
作者：
Cao L;Tolić N;Qu Y;Meng D;Zhao R;Zhang Q;Moore RJ;Zink EM;Lipton MS;Paša-Tolić L;Wu S
通讯作者：
Wu S
影响因子：
7.4
作者：
Alley, William R., Jr.;Madera, Milan;Mechref, Yehia;Novotny, Milos V.
通讯作者：
Novotny, Milos V.
影响因子：
3
作者：
Campbell MP;Ranzinger R;Lütteke T;Mariethoz J;Hayes CA;Zhang J;Akune Y;Aoki-Kinoshita KF;Damerell D;Carta G;York WS;Haslam SM;Narimatsu H;Rudd PM;Karlsson NG;Packer NH;Lisacek F
通讯作者：
Lisacek F
影响因子：
2.9
作者：
Abou-Abbass H;Abou-El-Hassan H;Bahmad H;Zibara K;Zebian A;Youssef R;Ismail J;Zhu R;Zhou S;Dong X;Nasser M;Bahmad M;Darwish H;Mechref Y;Kobeissy F
通讯作者：
Kobeissy F
影响因子：
2.9
作者：
Bodnar, Judit;Szekrenyes, Akos;Guttman, Andras
通讯作者：
Guttman, Andras