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An integrated mass spectrometric and computational framework for the analysis of protein interaction networks

Abstract

Biological systems are controlled by protein complexes that associate into dynamic protein interaction networks. We describe a strategy that analyzes protein complexes through the integration of label-free, quantitative mass spectrometry and computational analysis. By evaluating peptide intensity profiles throughout the sequential dilution of samples, the MasterMap system identifies specific interaction partners, detects changes in the composition of protein complexes and reveals variations in the phosphorylation states of components of protein complexes. We use the complexes containing the human forkhead transcription factor FoxO3A to demonstrate the validity and performance of this technology. Our analysis identifies previously known and unknown interactions of FoxO3A with 14-3-3 proteins, in addition to identifying FoxO3A phosphorylation sites and detecting reduced 14-3-3 binding following inhibition of phosphoinositide-3 kinase. By improving specificity and sensitivity of interaction networks, assessing post-translational modifications and providing dynamic interaction profiles, the MasterMap system addresses several limitations of current approaches for protein complexes.

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Figure 1: Schematic overview of the experimental approach.
Figure 2: Enrichment profiles of protein groups separate specific interaction partners from contaminant proteins.
Figure 3: The MasterMap reveals quantitative changes in the abundance of FoxO3A interaction partners upon PI3K inhibition.
Figure 4: Extended annotation of MS1 features and peptide-to-protein associations.

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Acknowledgements

The authors thank Luis Mendoza for helpful discussions. Alexander Schmidt for help with the FT-ICR spectrometer. We thank M. Greenberg for the gift of pcDNA3-HA-FoxO3A. The work was funded in part by ETH Zurich with Federal (USA) funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under contract No. N01-HV-28179. Oliver Rinner was supported by fellowships of the Roche Research Foundation and the Deutsche Forschungsgemeinschaft (DFG).

Author information

Affiliations

Authors

Contributions

O.R. conducted most of the experimental work and developed analytical concepts, L.N.M. conceptualized and implemented most of the SuperHirn software, M.H. did most of the LTQ-FT-ICR measurements, M.M., M.G. and R.A. shared senior authorship responsibilities and have conceived computational, biological and mass spectrometric concepts, respectively.

Corresponding authors

Correspondence to Markus Müller, Matthias Gstaiger or Ruedi Aebersold.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Dilution curve of lysozyme mixed into a background of β-lactoglobulin and β-casein. (PDF 37 kb)

Supplementary Fig. 2

Simple ratios between bait and control sample fail to separate interaction partners from contaminant proteins. (PDF 69 kb)

Supplementary Fig. 3

K-means clustering of feature profiles from a dilution series. (PDF 81 kb)

Supplementary Fig. 4

The heterotrimeric protein phosphatase PP2A was identified as FoxO3A interaction partner under cross-linking conditions. (PDF 65 kb)

Supplementary Fig. 5

Transfer of MS2 information across aligned MS1 features compensates MS2 undersampling. (PDF 174 kb)

Supplementary Fig. 6

Dynamic range of quantified MS1 feature profiles. (PDF 73 kb)

Supplementary Table 1

Identified interaction partners of FoxO3A using DSP cross-linking in FCS condition. (DOC 49 kb)

Supplementary Table 2

Growth state specific changes in the FoxO3A phosphorylation pattern. (DOC 60 kb)

Supplementary Results (DOC 120 kb)

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Rinner, O., Mueller, L., Hubálek, M. et al. An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nat Biotechnol 25, 345–352 (2007). https://doi.org/10.1038/nbt1289

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