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.
Highlights
The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics
A mixture of five standard glycoproteins was analyzed by LC-MS/MS on an Orbitrap Fusion instrument using various MS/MS collision parameters, including collision-induced dissociation (CID) and higher-energy collision dissociation (HCD), each with nine different energies, as well as electron transfer dissociation (ETD) coupled with either CID or HCD (ETciD/ EThcD) (Supplementary Notes 1 and 2)
An example glycopeptide spectrum obtained under the optimized stepped collision energies (SCE)-MS/MS conditions is illustrated in Fig. 1a, along with a spectrum obtained under the default single-energy HCD-MS/MS conditions for the same glycopeptide (Fig. 1b)
Summary
The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. The current search engines can usually employ some quality control methods[10,11,12,13,14,15,16,17, 24,25,26,27,28,29], severe underestimation of the FDR has been reported[16] To address these limitations, we performed extensive analyses and developed novel methods, including a high-throughput MS acquisition method based on optimized MS/MS collision parameters, which generates comprehensive fragments of an intact glycopeptide in a single spectrum. We report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific Nglycans in five mouse tissues and compare our method with the latest method of comprehensive glycosylation analysis
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