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

Ming-Qi LiuWen-Feng ZengPan FangWei-Qian CaoChao LiuGuo-Quan YanYang ZhangChao PengJian-Qiang WuXiao-Jin ZhangHui-Jun TuHao ChiRui-Xiang SunYong CaoMeng-Qiu DongBi-Yun JiangJiang-Ming HuangHua-Li ShenCatherine C L WongSi-Min HePeng-Yuan Yang
Published in: Nature communications (2017)
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.Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.
Keyphrases
  • quality control
  • high throughput
  • mass spectrometry
  • bioinformatics analysis
  • gene expression
  • small molecule
  • big data
  • quality improvement
  • artificial intelligence
  • study protocol
  • ms ms