Complementary ion information provided by HCD- and CID-MS/MS. Both HCD- and CID-MS/MS

could be used to optimize the glycopeptide identification. Recently reported software tools used HCD- or CID-MS/MS to identify glycopeptides, especially the glycan moiety of glycopeptides, but few of them integrated these two fragmentations to further improve the glycopeptide identification5,20. GlycoFragWork combined HCDand CID-MS/MS for the glycan identification11, but the complementarity should be further investigated. For the glycopeptide identification, trimannosyl core ions, especially the Y0, Y1, Y1_ and Y2 ions, are critical for detection of the mass of the peptide backbone12,21. We analyzed the different fragmentation behaviors of trimannosyl core and non-trimannosyl core ions in HCD- and CID-MS/MS spectra, and the results were shown in Fig. 5. More trimannosyl core ions could be provided to improve the glycan identification performance by combining these two fragmentations, as shown in Fig. 5a. And as illustrated in Fig. 5b, HCD-MS/MS (@NCE = 40%) preferentially produced innermost Y ions like Y0, Y1_ and Y1 ions, especially the Y1_ ion, which was almost not observed in CID-MS/MS. The outer trimannosyl core ions such as Y2, Y3 (Y-12000) and Y4 (Y-22000) were more common in CID-MS/MS spectra. From Fig. 5c, we could find out that non-trimannosyl core ions were predominantly produced by CID-MS/MS. For HCD-MS/MS (@NCE = 40%), almost all identified glycopeptides had less than 4 non-trimannosyl core ions matched.

MS3 fragmentation modes — HCD or CID. We have also investigated the efficiency of different MS3 fragmentation modes for the glycopeptide analysis. In the Orbitrap Fusion, parent ions of MS3 could be fragmented either in the HCD collision cell or in the ion trap (CID). Intuitively, HCD could provide more complete dissociation of precursors than CID. We compared the matched ions of peptides “NEEYJ[ + HexNAc]K” and “LVPVPITJ[ + HexNAc]ATLDR” with close basepeak intensities in HCD- and CID-MS3 spectra, and the results showed that HCD had more peaks matched, implying that HCD was a better fragmentation mode for MS3 identification comparing with CID (see the section “MS3 fragmentation mode comparison” and Figs S-6 and S-7 in the Supporting Information).

A potential pitfall of DDA-MS3 for the identification of glycopeptides is the requirement that Y1 ion must be one of the three most intense peaks in the mass range above 700 m/z in an HCD-MS/MS spectrum. This requirement is not surely guaranteed for every glycopeptide due to the different fragmentation behaviors of different glycopeptides. Some efforts could be made to get more confident identifications, such as the supplementary targeted MS3 for the unidentified peptide backbones with identified glycans5.

Conclusions

Taking the advantage of advanced settings provided by new instruments, data acquisition for both glycans and peptides becomes more flexible. With the Orbitrap Fusion, MS3 precursors could be acquired for the Y1 ions in HCD-MS/MS (@NCE = 40%) in a data-dependent mode. And in HCD (@NCE = 40%) spectra of glycopeptides, Y_1 ions are frequently observed, which is counted as one of the trimannosyl core ions in pGlyco to improve the glycan identification. The complementarity of HCD-MS/MS and CID-MS/MS was used to improve the identification performance as well. By integrating the information of HCD-MS/MS, CID-MS/MS and MS3, glycopeptides could be identified with complete spectral information of glycans and peptides.

Another contribution of this work is that we proposed a practical method to estimate the false discovery rate for glycan identifications. By employing the finite mixture model, the score distribution of correct and incorrect identifications could be deconvoluted, and the FDR could be estimated. The method has been tested and proved to work well on two complex protein datasets and a standard protein dataset, and has also been tested on the manually checked glycopeptide data. If any new decoy method is developed, the finite mixture model could be qualified to estimate the FDR as well.

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Acknowledgements

The study was supported by grants from the National Basic Research Program of China (973 grants 2011CB910600 to P.Y.Y., 2013CB911203 to R.-X.S., 2010CB912701 to P.Y.Y. and S.-M.H., 2011CB910604 to P.Y.Y.), National Natural Science Foundation of China project (21227805 to P.Y.Y. and R.-X.S., 31300680 to Y.Z.), Hi-Tech Research and Development Program of China (863 grants 2014AA020902 to P.Y.Y. and S.-M.H.,

2012AA020203 to P.Y.Y.).

Author Contributions

W.-F.Z. developed the algorithms and the software pGlyco, M.-Q.L. designed the mass spectrometric pipeline, W.-F.Z. and M.-Q.L. analyzed the data and wrote the main manuscript text. Y.Z. help develop pGlyco and J.-Q.W.processed the glycan database of pGlyco. P.F. prepared the biological samples. A.N., G.Y. and C.P. conducted the mass spectrometric experiments. W.Q.C., H.C., C.L. and R.-X.S. took part in designing the pipeline and pGlyco. C.C.L.W., S.-M.H. and P.Y. directed this study and revised the manuscript.

Additional Information

Supplementary information accompanies this paper at http://www.nature.com/srep

Competing financial interests: The authors declare no competing financial interests.

How to cite this article: Zeng, W.-F. et al. pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3. Sci. Rep. 6, 25102; doi: 10.1038/srep25102 (2016).

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