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A quantitative protein interaction network for the ErbB receptors using protein microarrays

Author

Listed:
  • Richard B. Jones

    (Department of Chemistry and Chemical Biology)

  • Andrew Gordus

    (Department of Chemistry and Chemical Biology
    Harvard University)

  • Jordan A. Krall

    (Department of Chemistry and Chemical Biology)

  • Gavin MacBeath

    (Department of Chemistry and Chemical Biology)

Abstract

Although epidermal growth factor receptor (EGFR; also called ErbB1) and its relatives initiate one of the most well-studied signalling networks, there is not yet a genome-wide view of even the earliest step in this pathway: recruitment of proteins to the activated receptors. Here we use protein microarrays comprising virtually every Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain encoded in the human genome to measure the equilibrium dissociation constant of each domain for 61 peptides representing physiological sites of tyrosine phosphorylation on the four ErbB receptors. This involved 77,592 independent biochemical measurements and provided a quantitative protein interaction network that reveals many new interactions, including ones that fall outside of our current view of domain selectivity. By slicing through the network at different affinity thresholds, we found surprising differences between the receptors. Most notably, EGFR and ErbB2 become markedly more promiscuous as the threshold is lowered, whereas ErbB3 does not. Because EGFR and ErbB2 are overexpressed in many human cancers, our results suggest that the extent to which promiscuity changes with protein concentration may contribute to the oncogenic potential of receptor tyrosine kinases, and perhaps other signalling proteins as well.

Suggested Citation

  • Richard B. Jones & Andrew Gordus & Jordan A. Krall & Gavin MacBeath, 2006. "A quantitative protein interaction network for the ErbB receptors using protein microarrays," Nature, Nature, vol. 439(7073), pages 168-174, January.
  • Handle: RePEc:nat:nature:v:439:y:2006:i:7073:d:10.1038_nature04177
    DOI: 10.1038/nature04177
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    Cited by:

    1. Doron Betel & Kevin E Breitkreuz & Ruth Isserlin & Danielle Dewar-Darch & Mike Tyers & Christopher W V Hogue, 2007. "Structure-Templated Predictions of Novel Protein Interactions from Sequence Information," PLOS Computational Biology, Public Library of Science, vol. 3(9), pages 1-7, September.
    2. Jacques Colinge & Keiryn L Bennett, 2007. "Introduction to Computational Proteomics," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-10, July.

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