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Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software

Nature Methods volume 3, pages 533–539 (2006)Cite this article

Abstract

Tandem mass spectrometry (MS/MS) allows for the rapid identification of many types of post-translational modifications (PTMs), especially those that can be detected by a diagnostic mass shift in one or more peptide fragment ions (for example, phosphorylation). But some PTMs (for example, SUMOs and other ubiquitin-like modifiers) themselves produce multiple fragment ions; combined with fragments from the modified target peptide, a complex overlapping fragmentation pattern is thus generated, which is uninterpretable by standard peptide sequencing software. Here we introduce SUMmOn, an automated pattern recognition tool that detects diagnostic PTM fragment ion series within complex MS/MS spectra, to identify modified peptides and modification sites within these peptides. Using SUMmOn, we demonstrate for the first time that human SUMO-1 multimerizes in vitro primarily via three N-terminal lysines, Lys7, Lys16 and Lys17. Notably, our method is theoretically applicable to any type of modification or chemical moiety generating a unique fragment ion pattern.

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Figure 1: Unmodified, phosphorylated and SUMOylated peptides.
Figure 2: SUMO protein sequences.
Figure 3: SUMmOn algorithm overview.
Figure 4: SUMmOn identifies a known RanGAP1 SUMOylation site.
Figure 5: SUMmOn analysis identifies SUMO-1 multimerization sites.

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Acknowledgements

We thank A.-C. Gingras, J. Eng, O. Vitek, X. Li, A. Nesvizhskii and N. Zhang for technical advice and critical reading of the manuscript. This project was funded in part by federal funds from the US National Heart, Lung and Blood Institute, National Institutes of Health, under contract number N01-HV-28179. X.D.Z. and M.J.M. were supported by a grant from the National Institutes of Health (GM060980). B.R. holds the Canada Research Chair (Tier 2) in Proteomics and Molecular Medicine, with support from the Canada Foundation for Innovation, the Ontario Innovation Trust, and the McLaughlin Centre for Molecular Medicine.

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Author notes

  1. Patrick G A Pedrioli and Brian Raught: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Systems Biology, 1441 N. 34th Street, Seattle, 98103, Washington, USA

    Patrick G A Pedrioli, Richard Rogers, John Aitchison & Ruedi Aebersold

  2. University Health Network, Ontario Cancer Institute and McLaughlin Centre for Molecular Medicine, MaRS TMDT 9-805, 101 College Street, Toronto, M5G 1L7, Ontario, Canada

    Brian Raught

  3. Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, 21205, Maryland, USA

    Xiang-Dong Zhang & Michael Matunis

  4. Institute for Molecular Systems Biology, Swiss Federal Institute of Technology, ETH Hönggerberg HPT E 78, CH-8093 Zürich, Switzerland and Faculty of Natural Sciences, University of Zurich, Switzerland

    Ruedi Aebersold

Authors
  1. Patrick G A Pedrioli
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Corresponding author

Correspondence to Brian Raught.

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Supplementary information

Supplementary Fig. 1

Screenshot of the introductory SUMmOn user interface. (PDF 456 kb)

Supplementary Fig. 2

SUMmOn identifies a known RanGAP1 conjugation site modified with SUMO-2 and a known RanGAP1 conjugation site modified with SUMO-3. (PDF 1788 kb)

Supplementary Fig. 3

SUMmOn identifies a known SUMO-2 conjugation site within SUMO-2. (PDF 320 kb)

Supplementary Fig. 4

SUMmOn identifies a known RanGAP1 conjugation site modified with Smt3. (PDF 1018 kb)

Supplementary Fig. 5

SUMmOn identifies SUMO conjugation sites in RanGAP1 and SUMO-1. (PDF 996 kb)

Supplementary Fig. 6

Identification of SUMO-1 multimerization sites. (PDF 540 kb)

Supplementary Table 1

Data used for modification site identification in this work. (PDF 93 kb)

Supplementary Table 2

SUMO sequence comparison. (PDF 218 kb)

Supplementary Methods (PDF 84 kb)

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Pedrioli, P., Raught, B., Zhang, XD. et al. Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software. Nat Methods 3, 533–539 (2006). https://doi.org/10.1038/nmeth891

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