糖基化蛋白质的核心岩藻糖化修饰既参与多种重要生理过程（如转化生长因子-β1和表皮生长因子信号通路等）的调节，也与多种疾病特别是癌症（如肝癌、胰腺癌、肺癌、卵巢癌、前列腺癌等）密切相关。在疾病诊断上，越来越多的研究表明，监测某些糖基化蛋白质核心岩藻糖化形式的表达水平变化，较检测这些蛋白质的总体表达水平变化，具有更好的特异性和灵敏度。但现有的糖蛋白质组学研究方法无法实现核心岩藻糖化蛋白质的规模化鉴定，因而限制了该类糖蛋白作为疾病标志物的有效筛选。
为解决此问题，北京蛋白质组研究中心钱小红研究员课题组与中国科学院计算技术研究所贺思敏研究员课题组等合作，通过发展和优化目标糖肽富集方法、中性丢失依赖的三级质谱采集方法、不依赖数据库的图谱筛选方法和图谱优化方法，建立了一种规模化、精确鉴定核心岩藻糖化蛋白质的崭新策略，并经过临床样本的检验。结果表明，该研究策略不但鉴定结果可信度高，而且鉴定蛋白质及其修饰位点的数目均超过以往文献报道方法的4－6倍，实现了在复杂血浆体系中的规模化鉴定。
相关工作近期发表于国际蛋白质组学顶级刊物《分子与细胞蛋白质组学》。作为国际上第一个专门用于核心岩藻糖化蛋白质规模化精确鉴定的策略，人们预计它将在肿瘤标志物筛选中发挥重要作用。（生物谷Bioon.com）
生物谷推荐原始出处：
Molecular & Cellular Proteomics 8:913-923, 2009.
A Strategy for Precise and Large Scale Identification of Core Fucosylated Glycoproteins*,S
Wei Jia,,?, Zhuang Lu,?,||, Yan Fu?,**, Hai-Peng Wang**, Le-Heng Wang**, Hao Chi**, Zuo-Fei Yuan**, Zhao-Bin Zheng, Li-Na Song, Huan-Huan Han, Yi-Min Liang, Jing-Lan Wang, Yun Cai, Yu-Kui Zhang||, Yu-Lin Deng||, Wan-Tao Ying,, Si-Min He**, and Xiao-Hong Qian,??
From the State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China, Institute of Biophysics, Chinese Academy of Sciences, No. 15 Datun Road, Chaoyang District, Beijing 100101, China, || Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China, and ** Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
Core fucosylation (CF) patterns of some glycoproteins are more sensitive and specific than evaluation of their total respective protein levels for diagnosis of many diseases, such as cancers. Global profiling and quantitative characterization of CF glycoproteins may reveal potent biomarkers for clinical applications. However, current techniques are unable to reveal CF glycoproteins precisely on a large scale. Here we developed a robust strategy that integrates molecular weight cutoff, neutral loss-dependent MS3, database-independent candidate spectrum filtering, and optimization to effectively identify CF glycoproteins. The rationale for spectrum treatment was innovatively based on computation of the mass distribution in spectra of CF glycopeptides. The efficacy of this strategy was demonstrated by implementation for plasma from healthy subjects and subjects with hepatocellular carcinoma. Over 100 CF glycoproteins and CF sites were identified, and over 10,000 mass spectra of CF glycopeptide were found. The scale of identification results indicates great progress for finding biomarkers with a particular and attractive prospect, and the candidate spectra will be a useful resource for the improvement of database searching methods for glycopeptides.
