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:

Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

Abstract

Protein complexes and protein interaction networks are essential mediators of most biological functions. Complexes supporting transient functions such as signal transduction processes are frequently subject to dynamic remodeling. Currently, the majority of studies on the composition of protein complexes are carried out by affinity purification and mass spectrometry (AP-MS) and present a static view of the system. For a better understanding of inherently dynamic biological processes, methods to reliably quantify temporal changes of protein interaction networks are essential. Here we used affinity purification combined with sequential window acquisition of all theoretical spectra (AP-SWATH) mass spectrometry to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: AP-SWATH workflow schematic.
Figure 2: Complete quantitative data matrix from AP-SWATH.
Figure 3: 14-3-3β protein interactions are confidently identified by enrichment over control APs.
Figure 4: Quantification of 14-3-3β interactome after perturbation.
Figure 5: AP-SWATH time-resolved data highlight sequestering of the mTORC1 inhibitory subunit AKTS1 but no change in mTORC2 bound to 14-3-3β.
Figure 6: Dynamic range of 14-3-3β–interacting proteins.

Similar content being viewed by others

References

  1. Hartwell, L.H., Hopfield, J.J., Leibler, S. & Murray, A.W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Robinson, C.V., Sali, A. & Baumeister, W. The molecular sociology of the cell. Nature 450, 973–982 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Pawson, T. Dynamic control of signaling by modular adaptor proteins. Curr. Opin. Cell Biol. 19, 112–116 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Good, M.C., Zalatan, J.G. & Lim, W.A. Scaffold proteins: hubs for controlling the flow of cellular information. Science 332, 680–686 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen, S., Synowsky, S., Tinti, M. & MacKintosh, C. The capture of phosphoproteins by 14-3-3 proteins mediates actions of insulin. Trends Endocrinol. Metab. 22, 429–436 (2011).

    Article  PubMed  CAS  Google Scholar 

  6. Jin, J. et al. Proteomic, functional, and domain-based analysis of in vivo 14-3-3 binding proteins involved in cytoskeletal regulation and cellular organization. Curr. Biol. 14, 1436–1450 (2004).

    Article  CAS  PubMed  Google Scholar 

  7. Ballif, B.A. et al. Identification of 14-3-3ɛ substrates from embryonic murine brain. J. Proteome Res. 5, 2372–2379 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. He, Y.F. et al. Biotin tagging coupled with amino acid-coded mass tagging for efficient and precise screening of interaction proteome in mammalian cells. Proteomics 9, 5414–5424 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Johnson, C. et al. Visualization and biochemical analyses of the emerging mammalian 14-3-3-phosphoproteome. Mol. Cell. Proteomics 10, M110.005751 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Rinner, O. et al. An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nat. Biotechnol. 25, 345–352 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Dubois, F. et al. Differential 14-3-3 affinity capture reveals new downstream targets of phosphatidylinositol 3-kinase signaling. Mol. Cell. Proteomics 8, 2487–2499 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Larance, M. et al. Global phosphoproteomics identifies a major role for AKT and 14-3-3 in regulating EDC3. Mol. Cell. Proteomics 9, 682–694 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gingras, A.C., Gstaiger, M., Raught, B. & Aebersold, R. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 8, 645–654 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Gavin, A.C., Maeda, K. & Kühner, S. Recent advances in charting protein-protein interaction: mass spectrometry-based approaches. Curr. Opin. Biotechnol. 22, 42–49 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Krogan, N.J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Kühner, S. et al. Proteome organization in a genome-reduced bacterium. Science 326, 1235–1240 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Glatter, T., Wepf, A., Aebersold, R. & Gstaiger, M. An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol. Syst. Biol. 5, 237 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Hubner, N.C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Varjosalo, M. et al. Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Nat. Methods 10, 307–314 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Ideker, T. & Krogan, N.J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wepf, A. et al. Quantitative interaction proteomics using mass spectrometry. Nat. Methods 6, 203–205 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. von Kriegsheim, A. et al. Cell fate decisions are specified by the dynamic ERK interactome. Nat. Cell Biol. 11, 1458–1464 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Bennett, E.J., Rush, J., Gygi, S.P. & Harper, J.W. Dynamics of cullin-RING ubiquitin ligase network revealed by systematic quantitative proteomics. Cell 143, 951–965 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Glatter, T. et al. Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome. Mol. Syst. Biol. 7, 547 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Domon, B. & Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 28, 710–721 (2010).

    Article  CAS  PubMed  Google Scholar 

  28. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29, 653–658 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Zheng, Y. et al. Temporal regulation of EGF signalling networks by the scaffold protein Shc1. Nature 499, 166–171 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Purvine, S., Eppel, J.T., Yi, E.C. & Goodlett, D.R. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3, 847–850 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Venable, J.D. et al. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Plumb, R.S. et al. UPLC/MS(E); a new approach for generating molecular fragment information for biomarker structure elucidation. Rapid Commun. Mass Spectrom. 20, 1989–1994 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Panchaud, A. et al. Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 81, 6481–6488 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Egertson, J.D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods 10, 744–746 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat. Methods 8, 430–435 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Lam, H. et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7, 655–667 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Chang, C.Y. et al. Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol. Cell. Proteomics 11, M111.014662 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Kovacina, K.S. et al. Identification of a proline-rich Akt substrate as a 14-3-3 binding partner. J. Biol. Chem. 278, 10189–10194 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Laplante, M. & Sabatini, D.M. mTOR signaling at a glance. J. Cell Sci. 122, 3589–3594 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Dibble, C.C., Asara, J.M. & Manning, B.D. Characterization of Rictor phosphorylation sites reveals direct regulation of mTOR complex 2 by S6K1. Mol. Cell. Biol. 29, 5657–5670 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Silva, J.C. et al. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Liu, Y. et al. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 13, 1247–1256 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics 11, M111.013987 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Lambert, J.-P. et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat. Methods 10.1038/nmeth.2702 (27 October 2013).

  47. Kristensen, A.R., Gsponer, J. & Foster, L.J. A high-throughput approach for measuring temporal changes in the interactome. Nat. Methods 9, 907–909 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Roux, K.J., Kim, D.I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266–270 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Marx, V. Targeted proteomics. Nat. Methods 10, 19–22 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Villén, J. & Gygi, S.P. The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. Nat. Protoc. 3, 1630–1638 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kessner, D. et al. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. MacLean, B., Eng, J.K., Beavis, R.C. & McIntosh, M. General framework for developing and evaluating database scoring algorithms using the TANDEM search engine. Bioinformatics 22, 2830–2832 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Craig, R. & Beavis, R.C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. 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).

    Article  CAS  PubMed  Google Scholar 

  56. Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 10, M111 007690 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gillet, L. et al. SWATH MS targeted data extraction: a powerful method to resolve false phospho-site assignments in phosphopeptides. in Proc. 61st ASMS Conf. Mass Spectrom. (ASMS, 2013).

  59. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  Google Scholar 

  61. Futschik, M.E. & Carlisle, B. Noise-robust soft clustering of gene expression time-course data. J. Bioinform. Comput. Biol. 3, 965–988 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Hornbeck, P.V. et al. PhosphoSite: A bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4, 1551–1561 (2004).

    Article  CAS  PubMed  Google Scholar 

  63. Wu, J. et al. Integrated network analysis platform for protein-protein interactions. Nat. Methods 6, 75–77 (2009).

    Article  CAS  PubMed  Google Scholar 

  64. Turner, B. et al. iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford) 2010, baq023 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Navarro for support with data analysis and spectral library generation; C. Ludwig, N. Selevsek and Y. Liu for helpful discussions on SWATH-MS; O. Schubert for the MTB SWATH-MS data file for target assay comparison; S. Hauri and A. van Drogen for advice on cell-line generation, stimulation and APs; A. Kahraman for discussions on structural aspects of 14-3-3; V. Chang for support with SRMstats; and the PRIDE team for assistance with the upload of associated data. We gratefully acknowledge financial support from the SystemsX.ch project PhosphonetX and European Research Council advanced grant Proteomics v3.0 (grant 233226) from the European Union.

Author information

Authors and Affiliations

Authors

Contributions

B.C.C. performed the cell-line generation, cell perturbation, AP and mass spectrometry experiments. L.C.G. co-developed the SWATH-MS methodology and provided critical input on analytical strategy. G.R. and H.L.R. developed the OpenSWATH software and provided critical input on SWATH-MS data analysis strategies. A.V. performed reciprocal AP-MS experiments. M.G. and R.A. conceived of and cosupervised the project. B.C.C. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Ruedi Aebersold.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Notes 1 and 2 and Supplementary Results (PDF 6572 kb)

Supplementary Table 1

Target assays for extraction of SWATH-MS data (XLSX 19778 kb)

Supplementary Table 2

567 high confidence 14-3-3 beta protein interactions (high confidence interactions highlighted in blue, bait in yellow, and control bait in green) (XLSX 264 kb)

Supplementary Table 3

Protein kinases selected for verification by reciprocal AP-MS (XLSX 9 kb)

Supplementary Table 4

Time course dynamics of 567 high confidence 14-3-3 beta protein interactions (XLSX 127 kb)

Supplementary Table 5

Soft clusters from fuzzy c-means clustering of time course (XLSX 24 kb)

Supplementary Table 6

Dynamic range of abundances in 14-3-3 beta protein interactions (bait highlighted in yellow) (XLSX 122 kb)

Supplementary Table 7

Phosphopeptides identified by shotgun-MS analysis of IMAC enrichment from a large pool of 14-3-3 beta affinity purifications from across the time course experiment (XLSX 382 kb)

Supplementary Table 8

Target assays for preliminary extraction of SWATH-MS data from phosphopeptides (XLSX 1699 kb)

Supplementary Table 9

Phosphopeptides identified by AP-SWATH (XLSX 111 kb)

Supplementary Table 10

Phosphosite mapping to 14-3-3 motifs on interacting proteins (XLSX 13 kb)

Supplementary Table 11

Enriched Molecular Function Gene Ontology FAT terms for 567 14-3-3 beta interacting proteins (XLSX 11 kb)

Supplementary Table 12

Enriched InterPro domains for 567 14-3-3 beta interacting proteins (XLSX 12 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Cite this article

Collins, B., Gillet, L., Rosenberger, G. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat Methods 10, 1246–1253 (2013). https://doi.org/10.1038/nmeth.2703

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2703

This article is cited by

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