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一种整合核磁共振光谱和毛细管电泳用于肝素及硫酸乙酰肝素寡糖结构鉴定的新型计算方法。

A novel computational approach to integrate NMR spectroscopy and capillary electrophoresis for structure assignment of heparin and heparan sulfate oligosaccharides.

作者信息

Guerrini Marco, Raman Rahul, Venkataraman Ganesh, Torri Giangiacomo, Sasisekharan Ram, Casu Benito

机构信息

Institute for Chemical and Biochemical Research, G. Ronzoni, via G. Colombo 81, 20133 Milan, Italy.

出版信息

Glycobiology. 2002 Nov;12(11):713-9. doi: 10.1093/glycob/cwf084.

DOI:10.1093/glycob/cwf084
PMID:12460939
Abstract

Heparin and heparan sulfate (HS) glycosaminoglycans (GAGs) are cell surface polysaccharides that bind to a multitude of signaling molecules, enzymes, and pathogens and modulate critical biological processes ranging from cell growth and development to anticoagulation and viral invasion. Heparin has been widely used as an anticoagulant in a variety of clinical applications for several decades. The heterogeneity and complexity of HS GAGs pose significant challenges to their purification and characterization of structure-function relationships. Nuclear magnetic resonance (NMR) spectroscopy is a promising tool that provides abundant sequence and structure information for characterization of HS GAGs. However, complex NMR spectra and low sensitivity often make analysis of HS GAGs a daunting task. We report the development of a novel methodology that incorporates distinct linkage information between adjacent monosaccharides obtained from NMR and capillary electrophoresis (CE) data using a property encoded nomenclature (PEN) computational framework to facilitate a rapid and unbiased procedure for sequencing HS GAG oligosaccharides. We demonstrate that the integration of NMR and CE data sets with the help of the PEN framework dramatically reduces the number of experimental constraints required to arrive at an HS GAG oligosaccharide sequence.

摘要

肝素和硫酸乙酰肝素(HS)糖胺聚糖(GAGs)是细胞表面多糖,可与多种信号分子、酶和病原体结合,并调节从细胞生长发育到抗凝和病毒入侵等关键生物学过程。几十年来,肝素在各种临床应用中一直被广泛用作抗凝剂。HS GAGs的异质性和复杂性对其纯化以及结构 - 功能关系的表征提出了重大挑战。核磁共振(NMR)光谱是一种很有前景的工具,可为HS GAGs的表征提供丰富的序列和结构信息。然而,复杂的NMR光谱和低灵敏度常常使HS GAGs的分析成为一项艰巨的任务。我们报告了一种新方法的开发,该方法使用属性编码命名法(PEN)计算框架,结合从NMR和毛细管电泳(CE)数据中获得的相邻单糖之间的独特连接信息,以促进HS GAG寡糖测序的快速且无偏差的程序。我们证明,在PEN框架的帮助下,NMR和CE数据集的整合显著减少了确定HS GAG寡糖序列所需的实验约束数量。

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