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

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 Dong, Bi-Yun Jiang, Jiang-Ming Huang, Hua-Li Shen, Catherine C. L. Wong (), Si-Min He () and Peng-Yuan Yang ()
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Ming-Qi Liu: Fudan University
Wen-Feng Zeng: Institute of Computing Technology, CAS
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
Xiao-Jin Zhang: Institute of Computing Technology, CAS
Hui-Jun Tu: Institute of Computing Technology, CAS
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
Peng-Yuan Yang: Fudan University

Nature Communications, 2017, vol. 8, issue 1, 1-14

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00535-2

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