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Affinity purification–mass spectrometry and network analysis to understand protein-protein interactions

Nature Protocols volume 9, pages 2539–2554 (2014)Cite this article

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Abstract

By determining protein-protein interactions in normal, diseased and infected cells, we can improve our understanding of cellular systems and their reaction to various perturbations. In this protocol, we discuss how to use data obtained in affinity purification–mass spectrometry (AP-MS) experiments to generate meaningful interaction networks and effective figures. We begin with an overview of common epitope tagging, expression and AP practices, followed by liquid chromatography–MS (LC-MS) data collection. We then provide a detailed procedure covering a pipeline approach to (i) pre-processing the data by filtering against contaminant lists such as the Contaminant Repository for Affinity Purification (CRAPome) and normalization using the spectral index (SIN) or normalized spectral abundance factor (NSAF); (ii) scoring via methods such as MiST, SAInt and CompPASS; and (iii) testing the resulting scores. Data formats familiar to MS practitioners are then transformed to those most useful for network-based analyses. The protocol also explores methods available in Cytoscape to visualize and analyze these types of interaction data. The scoring pipeline can take anywhere from 1 d to 1 week, depending on one's familiarity with the tools and data peculiarities. Similarly, the network analysis and visualization protocol in Cytoscape takes 2–4 h to complete with the provided sample data, but we recommend taking days or even weeks to explore one's data and find the right questions.

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Figure 1: Screenshot of a network import dialog showing the import of Supplementary Table 4 from Jäger et al.12.
Figure 2: Screenshot of a table import dialog showing the import of Supplementary Table 4 from Jäger et al.12.
Figure 3: Screenshot showing the merging of the IntAct imported network with our original AP-MS data set.
Figure 4: The merged and filtered network.
Figure 5: Screenshot of the JTreeView viewer from clusterMaker2 after a hierarchical cluster of the PE scores from the Collins et al.63 data set.
Figure 6: Cytoscape screenshot showing the clustered network resulting from an MCL cluster on the high-density data set from Collins et al.63.
Figure 7: Final visualized network from the Jäger et al.12 data set showing the Jurkat (purple gradient on the right-hand side of the nodes) and HEK293 scores (blue gradient on the left).

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Acknowledgements

The work of J.H.M. and A.R.P. is supported by grant no. P41 GM103504 (the National Resource for Network Biology (NRNB)). J.H.M. is also supported by grant no. P41 GM103311 (Resource for Biocomputing, Visualization, and Informatics (RBVI)). G.M.K. is supported by the National Institute of General Medical Sciences (NIGMS) grant no. 8P41 GM103481. E.V. and J.R.J. are supported by US National Institutes of Health grant nos. P50 GM082250, P01 AI090935, P01AI091575 and P01 AI106754. P.C. is supported by a Howard Hughes Medical Institute Predoctoral Fellowship. A.L.G. is supported by the Walter K. Evans Prememorial Fellowship.

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Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA

    John H Morris & Giselle M Knudsen

  2. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA

    Erik Verschueren, Jeffrey R Johnson & Peter Cimermancic

  3. Graduate Group in Bioinformatics, University of California, San Francisco, San Francisco, California, USA

    Peter Cimermancic

  4. School of Medicine, University of California, San Francisco, San Francisco, California, USA

    Alexander L Greninger

  5. Gladstone Institutes, University of California, San Francisco, San Francisco, California, USA

    Alexander R Pico

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  1. John H Morris
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Contributions

G.M.K., A.L.G. and J.R.J. contributed the Introduction and Experimental considerations; E.V. and P.C. contributed to Part 1 of the protocol (scoring pipeline) and its associated Supplementary Data; J.H.M. and A.R.P. contributed to Part 2 of the protocol (network analysis) including Cytoscape files and associated Supplementary Methods.

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Correspondence to Alexander R Pico.

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Supplementary information

Supplementary Data 1

Excel spreadsheet of the raw AP-MS data before scoring. This spreadsheet was reproduced from ref. 12, Macmillan Publishers Limited, and corresponds to Supplementary Data 1 in that paper. (XLS 10905 kb)

Supplementary Data 2

Excel spreadsheet of the scored AP-MS data. This spreadsheet was reproduced from ref. 12, Macmillan Publishers Limited, and corresponds to Supplementary Data 3 in that paper. (XLS 1631 kb)

Supplementary Data 3

Cytoscape session file with data adapted from ref. 63 (Collins, S. R. et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell. Proteomics 6, 439–450, 2007). (ZIP 1522 kb)

Supplementary Data 4

Cytoscape session file with imported network from Supplementary Data 2.xls (>0.75 CUTOFF), imported network from IntAct (IntAct…), merged network (Merged Network), and filtered merged network (Merged Network(1)). The final visualization of the data for ref. 12 shown in Figure 7 is the view for Merged Network(1). (ZIP 4840 kb)

Supplementary Methods

A step-by-step tutorial of the network analysis protocol using Cytoscape 3.1.1. (PDF 11709 kb)

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Morris, J., Knudsen, G., Verschueren, E. et al. Affinity purification–mass spectrometry and network analysis to understand protein-protein interactions. Nat Protoc 9, 2539–2554 (2014). https://doi.org/10.1038/nprot.2014.164

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