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pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

Author

Listed:
  • Ming-Qi Liu

    (Fudan University
    Fudan University)

  • Wen-Feng Zeng

    (Institute of Computing Technology, CAS
    University of Chinese Academy of Sciences)

  • Pan Fang

    (Fudan University)

  • Wei-Qian Cao

    (Fudan University)

  • Chao Liu

    (Institute of Computing Technology, CAS)

  • Guo-Quan Yan

    (Fudan University)

  • Yang Zhang

    (Fudan University)

  • Chao Peng

    (Shanghai Institutes for Biological Sciences, CAS)

  • Jian-Qiang Wu

    (Institute of Computing Technology, CAS
    University of Chinese Academy of Sciences)

  • Xiao-Jin Zhang

    (Institute of Computing Technology, CAS
    University of Chinese Academy of Sciences)

  • Hui-Jun Tu

    (Institute of Computing Technology, CAS
    University of Chinese Academy of Sciences)

  • Hao Chi

    (Institute of Computing Technology, CAS)

  • Rui-Xiang Sun

    (Institute of Computing Technology, CAS)

  • Yong Cao

    (National Institute of Biological Sciences (Beijing))

  • Meng-Qiu Dong

    (National Institute of Biological Sciences (Beijing))

  • Bi-Yun Jiang

    (Fudan University)

  • Jiang-Ming Huang

    (Fudan University)

  • Hua-Li Shen

    (Fudan University)

  • Catherine C. L. Wong

    (Shanghai Institutes for Biological Sciences, CAS)

  • Si-Min He

    (Institute of Computing Technology, CAS
    University of Chinese Academy of Sciences)

  • Peng-Yuan Yang

    (Fudan University
    Fudan University)

Abstract

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.

Suggested Citation

  • Ming-Qi Liu & Wen-Feng Zeng & Pan Fang & Wei-Qian Cao & Chao Liu & Guo-Quan Yan & Yang Zhang & Chao Peng & Jian-Qiang Wu & Xiao-Jin Zhang & Hui-Jun Tu & Hao Chi & Rui-Xiang Sun & Yong Cao & Meng-Qiu D, 2017. "pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00535-2
    DOI: 10.1038/s41467-017-00535-2
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