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Deep interactome profiling of membrane proteins by co-interacting protein identification technology

Nature Protocols volume 11, pages 2515–2528 (2016)Cite this article

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Abstract

Affinity purification coupled to mass spectrometry (AP–MS) is the method of choice for analyzing protein–protein interactions, but common protocols frequently recover only the most stable interactions and tend to result in low bait yield for membrane proteins. Here, we present a novel, deep interactome sequencing approach called CoPIT (co-interacting protein identification technology), which allows comprehensive identification and analysis of membrane protein interactomes and their dynamics. CoPIT integrates experimental and computational methods for a coimmunoprecipitation (Co-IP)-based workflow from sample preparation for mass spectrometric analysis to visualization of protein–protein interaction networks. The approach particularly improves the results for membrane protein interactomes, which have proven to be difficult to identify and analyze. CoPIT was used successfully to identify the interactome of the cystic fibrosis transmembrane conductance regulator (CFTR), demonstrating its validity and performance. The experimental step in this case achieved up to 100-fold-higher bait yield than previous methods by optimizing lysis, elution, sample clean-up and detection of interacting proteins by multidimensional protein identification technology (MudPIT). Here, we further provide evidence that CoPIT is applicable to other types of proteins as well, and that it can be successfully used as a general Co-IP method. The protocol describes all steps, ranging from considerations for experimental design, Co-IP, preparation of the sample for mass spectrometric analysis, and data analysis steps, to the final visualization of interaction networks. Although the experimental part can be performed in <3 d, data analysis may take up to a few weeks.

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Figure 1: Schematic outline and workflow of CoPIT.
Figure 2: Sensitivity of CoPIT.
Figure 3: Determination of a specific interactome.
Figure 4: Schematic depicting a Radial Topology Viewer map of the ΔF508 CFTR interactome.

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Change history

  • 30 November 2017

    In the version of this article initially published, Steps 11 and 20 mentioned an incorrect reagent. The correct text is: "Immediately add 1.5 ml of ice-cold TNI lysis buffer per 15-cm cell culture dish” (Step 11) and "Remove the supernatant, transfer the beads to a new tube and wash the beads three times with 20–100 bead volumes of TNI lysis buffer" (Step 20). The TNI lysis buffer was also not listed in the Materials. These errors have been corrected, and the following text was added to the Materials section: "TNI lysis buffer TNI lysis buffer is 0.5% Igepal CA-630, 50 mM Tris (pH 7.5), 250 mM NaCl, 1 mM EDTA, 1× Complete ULTRA EDTA-free Protease Inhibitor cocktail and 1× PhosStop." These errors, made during production, have been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank J. Clancy (University of Alabama, Birmingham, AL) for kindly providing the cell lines used in this work. We thank D. Cociorva and T. Xu for suggestions and comments on data analysis strategies, Y.L. Tan from J. Kelly's laboratory for performing the GC IPs and N. Huang from A. Rao's laboratory for performing the Tet2 IPs. This work was supported by NIH grants 5R01HL079442 (J.R.Y.), P01AG031097 (J.R.Y.), P41 RR011823 (J.R.Y.), DK075295 (J.W.K.) and HHSN268201000035C (J.R.Y.), and CFF mass spectrometry fellowship BALCH050X6 (S.P. and J.R.Y.).

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Authors and Affiliations

  1. Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California, USA

    Sandra Pankow, Casimir Bamberger, Diego Calzolari & John R Yates III

  2. Department of Physics, Albert-Ludwigs-University Freiburg, Freiburg, Germany

    Andreas Bamberger

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Contributions

S.P. and C.B. developed experimental methods and performed experiments, data analysis and mass spectrometric measurements. C.B., A.B. and S.P. developed the statistical analysis. D.C. and C.B. developed the Radial Topology Viewer. J.R.Y., S.P. and C.B. designed the study and wrote the manuscript.

Corresponding author

Correspondence to John R Yates III.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Sequence coverage achieved by CoPIT.

A. Sequence coverage of SMG1, GC and Tet2 indicating identified peptides (green). B. Sequence coverage of Calnexin indicating identified peptides (yellow), transmembrane domain (green) and cleaved signal peptide (turquoise).

Supplementary Figure 2 Recovery of hydrophobic CFTR peptides by MudPIT.

High salt concentrations are necessary for elution of hydrophobic CFTR peptides from SCX. The graph shows the distribution of Kyte-Dolittle (KD) values indicating hydrophobicity of identified ∆F508 CFTR peptides over the different MudPIT steps and corresponding salt concentrations. Error bars indicate mean with s.e.m.

Supplementary information

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Supplementary Figures 1 and 2 (PDF 576 kb)

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Pankow, S., Bamberger, C., Calzolari, D. et al. Deep interactome profiling of membrane proteins by co-interacting protein identification technology. Nat Protoc 11, 2515–2528 (2016). https://doi.org/10.1038/nprot.2016.140

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