Skip to main content
Log in

Sequential fragment ion filtering and endoglycosidase-assisted identification of intact glycopeptides

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Detailed characterization of glycoprotein structures requires determining both the sites of glycosylation as well as the glycan structures associated with each site. In this work, we developed an analytical strategy for characterization of intact N-glycopeptides in complex proteome samples. In the first step, tryptic glycopeptides were enriched using ZIC-HILIC. Secondly, a portion of the glycopeptides was treated with endoglycosidase H (Endo H) to remove high-mannose (Man) and hybrid N-linked glycans. Thirdly, a fraction of the Endo H-treated glycopeptides was further subjected to PNGase F treatment in 18O water to remove the remaining complex glycans. The intact glycopeptides and deglycosylated peptides were analyzed by nano-RPLC–MS/MS, and the glycan structures and the peptide sequences were identified by using the Byonic or pFind tools. Sequential digestion by endoglycosidase provided candidate glycosites information and indication of the glycoforms on each glycopeptide, thus helping to confine the database search space and improve the confidence regarding intact glycopeptide identification. We demonstrated the effectiveness of this approach using RNase B and IgG and applied this sequential digestion strategy for the identification of glycopeptides from the HepG2 cell line. We identified 4514 intact glycopeptides coming from 947 glycosites and 1011 unique peptide sequences from HepG2 cells. The intensity of different glycoforms at a specific glycosite was obtained to reach the occupancy ratios of site-specific glycoforms. These results indicate that our method can be used for characterizing site-specific protein glycosylation in complex samples.

Through integrating the information of intact glycopeptide, fragment ions filters and endoglycosidase digestion, the reliability of the identification could be significantly improved. We quantified the site-specific glycoforms occupancy ratios through the MS response signaling of each glycopeptide at the same time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Mariño K, Bones J, Kattla JJ, Rudd PM. A systematic approach to protein glycosylation analysis: a path through the maze. Nat Chem Biol. 2010;6(10):713–23.

    Article  Google Scholar 

  2. Zhu Z, Desaire H. Carbohydrates on proteins: site-specific glycosylation analysis by mass spectrometry. Annu Rev Anal Chem. 2015;8:463–83.

    Article  CAS  Google Scholar 

  3. Kuo C-W, Wu I-L, Hsiao H-H, Khoo K-H. Rapid glycopeptide enrichment and N-glycosylation site mapping strategies based on amine-functionalized magnetic nanoparticles. Anal Bioanal Chem. 2012;402(9):2765–76.

    Article  CAS  Google Scholar 

  4. Dalziel M, Crispin M, Scanlan CN, Zitzmann N, Dwek RA. Emerging principles for the therapeutic exploitation of glycosylation. Science. 2014;343(6166):1235681.

    Article  Google Scholar 

  5. Kailemia MJ, Park D, Lebrilla CB. Glycans and glycoproteins as specific biomarkers for cancer. Analytical and bioanalytical chemistry. 2016:1–16.

  6. Hu W, Su X, Zhu Z, Go EP, Desaire H. GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses. Analytical and bioanalytical chemistry. 2016:1–10.

  7. Stowell SR, Ju T, Cummings RD. Protein glycosylation in cancer. Annu Rev Pathol. 2015;10:473.

    Article  CAS  Google Scholar 

  8. Liu M, Zhang Y, Chen Y, Yan G, Shen C, Cao J, et al. Efficient and accurate glycopeptide identification pipeline for high-throughput site-specific N-glycosylation analysis. J Proteome Res. 2014;13(6):3121–9.

    Article  CAS  Google Scholar 

  9. Kolli V, Schumacher KN, Dodds ED. Engaging challenges in glycoproteomics: recent advances in MS-based glycopeptide analysis. Bioanalysis. 2015;7(1):113–31.

    Article  CAS  Google Scholar 

  10. Desaire H. Glycopeptide analysis, recent developments and applications. Mol Cell Proteomics. 2013;12(4):893–901.

    Article  CAS  Google Scholar 

  11. He L, Xin L, Shan B, Lajoie GA, Ma B. GlycoMaster DB: software to assist the automated identification of N-linked glycopeptides by tandem mass spectrometry. J Proteome Res. 2014;13(9):3881–95.

    Article  CAS  Google Scholar 

  12. Toghi Eshghi S, Shah P, Yang W, Li X, Zhang H. GPQuest: a spectral library matching algorithm for site-specific assignment of tandem mass spectra to intact N-glycopeptides. Anal Chem. 2015;87(10):5181–8.

    Article  CAS  Google Scholar 

  13. Bern M, Kil YJ, Becker C. Byonic. Advanced peptide and protein identification software. Current Protocols in Bioinformatics. 2012:13.20. 1–13.20. 14.

  14. Becker C, Tang W, Kil YJ, Yin X, Mayr M, Khoo K-H, et al. Search strategies for glycopeptide identification. J Biomol Tech. 2013;24(Suppl):S33.

    Google Scholar 

  15. Zeng W, Liu M, Zhang Y, Wu J, Fang P, Peng C, et al. pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD-and CID-MS/MS and MS3. Sci Rep. 2016;6:25102.

    Article  CAS  Google Scholar 

  16. Lee LY, Moh ES, Parker BL, Bern M, Packer NH, Thaysen-Andersen M. Toward automated N-glycopeptide identification in glycoproteomics. J Proteome Res. 2016;15(10):3904–15.

    Article  CAS  Google Scholar 

  17. Hu H, Khatri K, Klein J, Leymarie N, Zaia J. A review of methods for interpretation of glycopeptide tandem mass spectral data. Glycoconj J. 2016;33(3):285–96.

    Article  CAS  Google Scholar 

  18. Alley Jr WR, Mann BF, Novotny MV. High-sensitivity analytical approaches for the structural characterization of glycoproteins. Chem Rev. 2013;113(4):2668–732.

    Article  CAS  Google Scholar 

  19. Zhang C, Ye Z, Xue P, Shu Q, Zhou Y, Ji Y, et al. Evaluation of different N-glycopeptide enrichment methods for N-glycosylation sites mapping in mouse brain. J Proteome Res. 2016;15(9):2960–8.

    Article  CAS  Google Scholar 

  20. Malerod H, Graham RL, Sweredoski MJ, Hess S. Comprehensive profiling of N-linked glycosylation sites in HeLa cells using hydrazide enrichment. J Proteome Res. 2013;12(1):248–59.

    Article  CAS  Google Scholar 

  21. Zhang W, Wang H, Zhang L, Yao J, Yang P. Large-scale assignment of N-glycosylation sites using complementary enzymatic deglycosylation. Talanta. 2011;85(1):499–505.

    Article  CAS  Google Scholar 

  22. Cao Q, Zhao X, Zhao Q, Lv X, Ma C, Li X, et al. Strategy integrating stepped fragmentation and glycan diagnostic ion-based spectrum refinement for the identification of core fucosylated glycoproteome using mass spectrometry. Anal Chem. 2014;86(14):6804–11.

    Article  CAS  Google Scholar 

  23. Wang L, Li DQ, Fu Y, Wang HP, Zhang JF, Yuan ZF, et al. pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry. Rapid Commun Mass Spectrom. 2007;21(18):2985–91.

    Article  CAS  Google Scholar 

  24. Li D, Fu Y, Sun R, Ling CX, Wei Y, Zhou H, et al. pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry. Bioinformatics. 2005;21(13):3049–50.

    Article  CAS  Google Scholar 

  25. Park G, Kim J, Hwang H, Lee J, Ahn Y, Lee H, et al. Integrated GlycoProteome Analyzer (I-GPA) for automated identification and quantitation of site-specific N-glycosylation. Sci Rep. 2016;6:21175.

    Article  CAS  Google Scholar 

  26. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc. 2016;11(12):2301–19.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful for the financial support from the National Key Program for Basic Research of China (2016YFA0501300, 2014CBA02001), the State Key Laboratory of NBC Protection of Civilian (SKLNBC03010), and the National Natural Science Foundation of China (81530021).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wantao Ying.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 109 kb)

ESM 2

(XLSX 457 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Z., Zhao, X., Tian, F. et al. Sequential fragment ion filtering and endoglycosidase-assisted identification of intact glycopeptides. Anal Bioanal Chem 409, 3077–3087 (2017). https://doi.org/10.1007/s00216-017-0195-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-017-0195-z

Keywords

Navigation