Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Nature Methods volume 17, pages 1125–1132 (2020)Cite this article

Subjects

Abstract

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Workflow of MSFragger-Glyco.
Fig. 2: Comparison of mass-offset-type and variable-modification-type searches for N-linked glycopeptides.
Fig. 3: Comparison of MSFragger-Glyco and original analysis for N-glycan datasets.
Fig. 4: Expanded O-glycan searches across tissues.

Similar content being viewed by others

Data availability

N- and O-linked glycoproteomics raw data were downloaded from the PRIDE Archive53 and Proteome Xchange54 with accession numbers PXD011533 (Riley et al.10 N-glycan data), PXD009476 (Yang et al.12 O-glycan data), and PXD005411, PXD005412, PXD005413, PXD005553 and PXD005555 (Liu et al.8 N-glycan data). Processed search results (raw data, MSFragger output files and processed peak tables) that support the findings of this study are available in PRIDE (accession number PXD021196).

Code availability

The MSFragger-Glyco program was developed in the cross-platform Java language, and incorporated in the MSFragger search engine starting with v.3.0, which can be accessed at https://msfragger.nesvilab.org/.

References

  1. Varki, A. Biological roles of glycans. Glycobiology 27, 3–49 (2017).

    CAS  PubMed  Google Scholar 

  2. Thaysen-Andersen, M., Packer, N. H. & Schulz, B. L. Maturing glycoproteomics technologies provide unique structural insights into the N-glycoproteome and its regulation in health and disease. Mol. Cell. Proteomics 15, 1773–1790 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Chang, D. & Zala, J. Why glycosylation matters in building a better flu vaccine. Mol. Cell. Proteomics 18, 2348–2358.

  4. Marsico, G., Russo, L., Quondamatteo, F. & Pandit, A. Glycosylation and integrin regulation in cancer. Trends Cancer 4, 537–552 (2018).

    CAS  PubMed  Google Scholar 

  5. Schedin-Weiss, S., Winblad, B. & Tjernberg, L. O. The role of protein glycosylation in Alzheimer disease. FEBS J. 281, 46–62 (2014).

    CAS  PubMed  Google Scholar 

  6. Wohlgemuth, J., Karas, M., Eichhorn, T., Hendriks, R. & Andrecht, S. Quantitative site-specific analysis of protein glycosylation by LC–MS using different glycopeptide-enrichment strategies. Anal. Biochem. 395, 178–188 (2009).

    CAS  PubMed  Google Scholar 

  7. Rudd, P. M. & Dwek, R. A. Glycosylation: heterogeneity and the 3D structure of proteins. Crit. Rev. Biochem. Mol. Biol. 32, 1–100 (1997).

    CAS  PubMed  Google Scholar 

  8. Liu, M. Q. et al. PGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification. Nat. Commun. 8, 438 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Suttapitugsakul, S., Sun, F. & Wu, R. Recent advances in glycoproteomicanalysis by mass spectrometry. Anal. Chem. 92, 267–291 (2020).

    CAS  PubMed  Google Scholar 

  10. Riley, N. M., Hebert, A. S., Westphall, M. S. & Coon, J. J. Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis. Nat. Communications 10, 1311–1311 (2019).

    Google Scholar 

  11. Reiding, K. R., Bondt, A., Franc, V. & Heck, A. J. R. The benefits of hybrid fragmentation methods for glycoproteomics. Trends Analyt. Chem. 108, 260–268 (2018).

    CAS  Google Scholar 

  12. Yang, W., Ao, M., Hu, Y., Li, Q. K. & Zhang, H. Mapping the O‐glycoproteome using site‐specific extraction of O‐linked glycopeptides (EXoO). Mol. Syst. Biol. 14, e8486 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. King, S. L. et al. Characterizing the O-glycosylation landscape of human plasma, platelets, and endothelial cells. Blood Adv. 1, 429–442 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Bollineni, R. C., Koehler, C. J., Gislefoss, R. E., Anonsen, J. H. & Thiede, B. Large-scale intact glycopeptide identification by Mascot database search. Sci. Rep 8, https://doi.org/10.1038/s41598-018-20331-2 (2018).

  15. Bern, M., Kil, Y. J. & Becker, C. Byonic: advanced peptide and protein identification software. Curr. Protoc. Bioinformatics 40, 13.20.1–13.20.14 (2012).

    Google Scholar 

  16. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spec. 5, 976–989 (1994).

    CAS  Google Scholar 

  17. Zhu, Z., Hua, D., Clark, D. F., Go, E. P. & Desaire, H. GlycoPep detector: a tool for assigning mass spectrometry data of N-linked glycopeptides on the basis of their electron transfer dissociation spectra. Anal. Chem. 85, 5023–5032 (2013).

    CAS  PubMed  Google Scholar 

  18. Yu, C. Y. et al. Automated glycan sequencing from tandem mass spectra of N-linked glycopeptides. Anal. Chem. 88, 5725–5732 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. He, L., Xin, L., Shan, B., Lajoie, G. A. & Ma, B. GlycoMaster DB: software to assist the automated identification of N-linked glycopeptides by tandem mass spectrometry. J. Proteome Res. 13, 3881–3895 (2014).

    CAS  PubMed  Google Scholar 

  20. Mayampurath, A. et al. Computational framework for identification of intact glycopeptides in complex samples. Anal. Chem. 86, 453–463 (2014).

    CAS  PubMed  Google Scholar 

  21. Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    CAS  PubMed  Google Scholar 

  22. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Solntsev, S. K., Shortreed, M. R., Frey, B. L. & Smith, L. M. Enhanced global post-translational modification discovery with metamorpheus. J. Proteome Res. 17, 1844–1851 (2018).

    CAS  PubMed  Google Scholar 

  24. Creasy, D. M. & Cottrell, J. S. Error tolerant searching of uninterpreted tandem mass spectrometry data. Proteomics 2, 1426–1434 (2002).

    CAS  PubMed  Google Scholar 

  25. Ma, C. W. M. & Lam, H. Hunting for unexpected post-translational modifications by spectral library searching with tier-wise scoring. J. Proteome Res. 13, 2262–2271 (2014).

    CAS  PubMed  Google Scholar 

  26. Chick, J. M. et al. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat. Biotech. 33, 743–749 (2015).

    CAS  Google Scholar 

  27. Ahrné, E., Nikitin, F., Lisacek, F. & Müller, M. QuickMod: a tool for open modification spectrum library searches. J. Proteome Res. 10, 2913–2921 (2011).

    PubMed  Google Scholar 

  28. Chi, H. et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotech. 36, 1059–1066 (2018).

    CAS  Google Scholar 

  29. Na, S., Bandeira, N. & Paek, E. Fast multi-blind modification search through tandem mass spectrometry. Mol. Cell. Proteomics 11, https://doi.org/10.1074/mcp.M111.010199 (2012).

  30. Swearingen, K. E. et al. A tandem mass spectrometry sequence database search method for identification of O-fucosylated proteins by mass spectrometry. J. Proteome Res. 18, 652–663 (2019).

    CAS  PubMed  Google Scholar 

  31. Trinidad, J. C., Schoepfer, R., Burlingame, A. L. & Medzihradszky, K. F. N- and O-Glycosylation in the murine synaptosome. Mol. Cell. Proteomics 12, 3474–3488 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Chalkley, R. J., Baker, P. R., Medzihardszky, K. F., Lynn, A. J. & Burlingame, A. L. In-depth analysis of tandem mass spectrometry data from disparate instrument types. Mol. Cell. Proteomics 7, 2386–2398 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Chalkley, R. J. & Baker, P. R. Use of a glycosylation site database to improve glycopeptide identification from complex mixtures. Anal. Bioanal. Chem. 409, 571–577 (2017).

    CAS  PubMed  Google Scholar 

  34. Medzihradszky, K. F., Kaasik, K. & Chalkley, R. J. Tissue-specific glycosylation at the glycopeptide level. Mol. Cell. Proteomics 14, 2103–2110 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Yu, F. et al. Identification of modified peptides using localization-aware open search. Nat. Communications 11, 4065 (2020).

    CAS  Google Scholar 

  36. Seipert, R. R. et al. Factors that influence fragmentation behavior of N-linked glycopeptide ions. Anal. Chem. 80, 3684–3692 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wuhrer, M., Deelder, A. M. & Van Der Burgt, Y. E. M. Mass spectrometric glycan rearrangements. Mass Spec. Rev. 30, 664–680 (2011).

    CAS  Google Scholar 

  38. Ledvina, A. R. et al. Infrared photoactivation reduces peptide folding and hydrogenatom migration following ETD tandem mass spectrometry. Angew. Chem. Int. Ed. 48, 8526–8528 (2009).

    CAS  Google Scholar 

  39. Vékey, K. et al. Fragmentation characteristics of glycopeptides. Int. J. Mass Spec. 345–347, 71–79 (2013).

    Google Scholar 

  40. Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

    CAS  PubMed  Google Scholar 

  41. Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

    CAS  PubMed  Google Scholar 

  42. Leprevost, F. D. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020).

    Google Scholar 

  43. Hang, H. C. & Bertozzi, C. R. The chemistry and biology of mucin-type O-linked glycosylation. Bioorg. Med. Chem. 13, 5021–5034 (2005).

    CAS  PubMed  Google Scholar 

  44. Jensen, P. H., Kolarich, D. & Packer, N. H. Mucin-type O-glycosylation—putting the pieces together. FEBS J. 277, 81–94 (2010).

    CAS  PubMed  Google Scholar 

  45. Yang, Z. et al. The GalNAc-type O-glycoproteome of CHO cells characterized by the simplecell strategy. Mol. Cell Proteomics 13, 3224–3235 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Potel, C. M., Lemeer, S. & Heck, A. J. R. Phosphopeptide fragmentation and site localization by mass spectrometry: an update. Anal. Chem. 91, 126–141 (2019).

    CAS  PubMed  Google Scholar 

  47. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Hu, H., Khatri, K., Klein, J., Leymarie, N. & Zaia, J. A review of methods for interpretation of glycopeptide tandem mass spectral data. Glycoconjugate J. 33, 285–296 (2016).

    CAS  Google Scholar 

  49. Hu, H., Khatri, K. & Zaia, J. Algorithms and design strategies towards automated glycoproteomics analysis. Mass Spec. Rev. 36, 475–498 (2017).

    CAS  Google Scholar 

  50. Li, K., Vaudel, M., Zhang, B., Ren, Y. & Wen, B. PDV: an integrative proteomics data viewer. Bioinformatics 35, 1249–1251 (2019).

    CAS  PubMed  Google Scholar 

  51. Röst, H. L., Schmitt, U., Aebersold, R. & Malmström, L. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14, 74–77 (2014).

    PubMed  Google Scholar 

  52. Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  Google Scholar 

  54. Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100–D1106 (2017).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was funded in part by NIH grant nos. R01-GM-094231 and U24-CA210967.

Author information

Authors and Affiliations

  1. Department of Pathology, University of Michigan, Ann Arbor, MI, USA

    Daniel A. Polasky, Fengchao Yu, Guo Ci Teo & Alexey I. Nesvizhskii

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Alexey I. Nesvizhskii

Authors
  1. Daniel A. Polasky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fengchao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guo Ci Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexey I. Nesvizhskii
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.A.P., F.Y. and G.C.T. developed the algorithm. D.A.P. analyzed the data. A.I.N. conceived and supervised the project. D.A.P. and A.I.N. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Alexey I. Nesvizhskii.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Arunima, Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Tables 1–9.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polasky, D.A., Yu, F., Teo, G.C. et al. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat Methods 17, 1125–1132 (2020). https://doi.org/10.1038/s41592-020-0967-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-020-0967-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing