pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCDand CID-MS/MS and MS3
Wen-Feng Zeng1,2,*, Ming-Qi Liu3,*, Yang Zhang3,*, Jian-Qiang Wu1,2,*, Pan Fang3,
Chao Peng4, Aiying Nie5, Guoquan Yan3, Weiqian Cao3, Chao
Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics.Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans, and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion, and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.
Confident characterization of the microheterogeneity of protein glycosylation remains one of the toughest analytical challenges1,2. Interpretation of intact glycopeptides by using liquid chromatography coupled with mass spectrometry (LC-MS) is one of the most promising methods for site-specific glycosylation study so far3. Different kinds of MS techniques and corresponding bioinformatic tools have been developed for the interpretation of intact glycopeptides.
One approach is direct interpretation of intact glycopeptides by using CID-MS/MS coupled with ETD-MS/MS or targeted MS34,5. Generally, in a CID-MS/MS spectrum, sufficient Y ions could be observed to deduce the glycan of a glycopeptide (In glycoproteomics, a Y ion of a glycopeptide is the peptide backbone ion carrying a glycan fragment from the glycosidic bond cleavage, and a y ion of a glycopeptide is the y ion of its peptide backbone).Some software tools have been developed to identify glycans by CID-MS/MS5–9. However, the b and y ions of the peptide backbone are usually undetectable in a CID-MS/MS spectrum4, so the peptide backbone identification should be performed by using some other MS techniques. One of them is ETD-MS/MS, which has extensive peptide backbone cleavage. By integrating the complementary information of CID- and ETD-MS/MS, intact glycopeptides could be confidently identified10. However, the sensitivity and the applicable scope of ETD-MS/MS are arguably limited as compared with HCD- and CID-MS/MS in current generation of MS instruments11–13, though some supercharging methods such as TMT tagging have been used to improve the sensitivity of glycopeptide identification in ETD-MS/MS analysis14,15. Another interesting MS technique for peptide backbone identification is targeted MS3, and the integrated identification pipeline is named as Sweet-Heart5, in which theoretical Y1 ions are firstly predicted by CID-MS/MS, and then multiple rounds of targeted MS3 are performed based on these Y1 ion predictions. Peptide backbones are confirmed after identifying these MS3 spectra.
The other popular method for the identification of intact glycopeptides is HCD-product-dependent-ETD (HCD-pd-ETD), which has been widely adopted in recent years12,16,17. Diagnostic glyco-oxonium ions in HCD-MS/MS spectra could be used to trigger the succeeding ETD dissociation, which could restrict the ETD-MS/MS data acquisition to only true glycopeptide precursors. HCD-MS/MS has additional two advantages for identification of intact glycopeptides: 1) Y1 ions are recognizable through fine-tuning the normalized collision energy (NCE)18, which could help trigger the MS3 fragmentation of Y1 ions easily from an HCD-MS/MS spectrum. And in an HCD-MS/MS spectrum, some Y ions could also be detected for the identification of the glycan19,20; 2) additional b and y ions of the peptide backbones of some glycopeptides in HCD-MS/MS spectra enable the Y1-based peptide search such as “Sweet-Heart for HCD” or MAGIC, which replaces the precursor mass of an HCD-MS/MS spectrum with the mass of the Y1 ion, and then the peptide backbone may be identified with a conventional protein identification search engine12,21. An alternative search strategy for the identification of intact glycopeptides with ETD-MS/MS or HCD-MS/MS is the direct protein database search by considering each glycan as a common variable modification attached on the glycosylation site12,14,22. However, it has been explicitly shown that this strategy would result in a high false-positive rate even if the peptide-spectrum match score is high, because the FDR control is just applied at the peptide level, with no control for the glycan identification12.
As discussed above, peptide backbone identification and glycan FDR estimation are two of the most challenging problems in glycoproteomics. To address these two issues, we proposed a new pipeline called pGlyco, which included two new features: 1) complementary fragments from both HCD-MS/MS and CID-MS/MS were used to identify glycans, and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition (DDA) of MS3 for some most intense peaks in the HCD-MS/MS spectrum was used to identify peptide backbones. In the HCD-MS/MS spectrum of a glycopeptide, the presence of the Y1 ion as one of the most intense ions above 700 m/z allows an MS instrument to perform the MS3 data acquisition of the Y1 ion in the data-dependent acquisition mode, resulting in a fully automated MS3 acquisition with no need to predict the prior Y1 ion information as in the targeted-MS3. And MS3 spectra of Y1 ions could generate sufficient fragments to identify peptide backbones. By combining these two features, intact glycopeptides could be identified with detailed spectral information for both glycans and peptides. We applied pGlyco to the study of a mixture of 6 standard glycoproteins and identified 309 non-redundant intact glycopeptides. pGlyco is currently available for free download at http://pfind.ict.ac.cn/software/pGlyco1505/.
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