糖基化蛋白質(zhì)的核心巖藻糖化修飾既參與多種重要生理過(guò)程（如轉(zhuǎn)化生長(zhǎng)因子-β1和表皮生長(zhǎng)因子信號(hào)通路等）的調(diào)節(jié)，也與多種疾病特別是癌癥（如肝癌,、胰腺癌、肺癌,、卵巢癌,、前列腺癌等）密切相關(guān)。在疾病診斷上,，越來(lái)越多的研究表明，監(jiān)測(cè)某些糖基化蛋白質(zhì)核心巖藻糖化形式的表達(dá)水平變化,，較檢測(cè)這些蛋白質(zhì)的總體表達(dá)水平變化,，具有更好的特異性和靈敏度。但現(xiàn)有的糖蛋白質(zhì)組學(xué)研究方法無(wú)法實(shí)現(xiàn)核心巖藻糖化蛋白質(zhì)的規(guī)?；b定,，因而限制了該類(lèi)糖蛋白作為疾病標(biāo)志物的有效篩選。
為解決此問(wèn)題,，北京蛋白質(zhì)組研究中心錢(qián)小紅研究員課題組與中國(guó)科學(xué)院計(jì)算技術(shù)研究所賀思敏研究員課題組等合作,，通過(guò)發(fā)展和優(yōu)化目標(biāo)糖肽富集方法、中性丟失依賴(lài)的三級(jí)質(zhì)譜采集方法,、不依賴(lài)數(shù)據(jù)庫(kù)的圖譜篩選方法和圖譜優(yōu)化方法,，建立了一種規(guī)模化,、精確鑒定核心巖藻糖化蛋白質(zhì)的嶄新策略,，并經(jīng)過(guò)臨床樣本的檢驗(yàn)。結(jié)果表明,，該研究策略不但鑒定結(jié)果可信度高,，而且鑒定蛋白質(zhì)及其修飾位點(diǎn)的數(shù)目均超過(guò)以往文獻(xiàn)報(bào)道方法的4－6倍，實(shí)現(xiàn)了在復(fù)雜血漿體系中的規(guī)?；b定,。
相關(guān)工作近期發(fā)表于國(guó)際蛋白質(zhì)組學(xué)頂級(jí)刊物《分子與細(xì)胞蛋白質(zhì)組學(xué)》,。作為國(guó)際上第一個(gè)專(zhuān)門(mén)用于核心巖藻糖化蛋白質(zhì)規(guī)模化精確鑒定的策略,，人們預(yù)計(jì)它將在腫瘤標(biāo)志物篩選中發(fā)揮重要作用,。（生物谷Bioon.com）
生物谷推薦原始出處：
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.
