综述: N-连接完整糖肽的质谱分析策略和研究方法

蛋白质糖基化作为最普遍、最重要的蛋白质修饰，一直是组学研究的焦点之一．近十几年来，N-连接糖蛋白质组学研究普遍采用的方法是将糖链与所修饰的多肽分开进行分析．该策略虽降低了分析难度，却也丢失了糖链与蛋白质糖基化位点间重要的对应关系信息．近年来，完整糖肽的质谱分析策略和方法逐步建立起来．总体而言，要实现对完整糖肽的直接质谱分析，首先需要从复杂样品中富集完整糖肽以消除非糖基化多肽对完整糖肽分析的影响，然后在质谱分析中还需要根据糖肽特性调整相应质谱分析参数，最后在后续数据分析中还需要开发相应的分析软件以完成完整糖肽中多肽序列和糖链组成或结构的鉴定．本文即从以上三个主要方面系统阐述目前N-完整糖肽分析中常用的质谱和数据分析策略和方法，并进一步在糖肽谱图识别、母离子单同位素分子质量校正、数据库选择以及假阳性率评估和控制等方面都进行了逐一探讨．完整糖肽的直接质谱分析有助于获取糖链和糖基化位点间的对应关系信息，可为生物标志物发现和疾病致病机理等研究提供更有力的糖蛋白质组学研究工具．

Review: Mass Spectrometry-based Strategies and Methods for N-linked Intact Glycopeptide Analysis

As one of the most common and important protein modifications, glycosylation has been one of the focuses of the proteomic researches. In the last decades, most of N-linked glycoproteomic studies focused mainly on the analysis of either released glycans or de-glycosylated peptides. While this strategy reduced the complexity of glycoprotein analysis, it lost glycosite-specific glycosylation information. Several strategies and methods for intact N-glycopeptide analysis have been established during the last few years. Generally, to achieve the identification and quantification of intact glycopeptides, the first step is to enrich glycopeptides from complex samples to reduce the affects from non-glycosylated peptides, then the mass spectrometry parameter settings need to be adjusted to satisfy the fragment features of glycopeptides, importantly the related software also need to be developed for the precise identification of the peptide sequence and glycan structures or compositions of the intact glycopeptides. These three main aspects of the strategies for mass spectrometry-based intact glycopeptide analysis are discussed in this paper. Some further details, such as the recognition of intact glycopeptide spectra, precursor monoisotopic mass correction, database selection, as well as the false discovery rate (FDR) evaluation and control, are further discussed. Direct intact glycopeptide analyses, with the recovery of the glycosite-specific glycosylation information, will provides a powerful tool for biomarker discovery and the mechanism studies on various diseases.