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Phospho‐tyrosine dependent protein–protein interaction network

Authors: Grossmann, A.; Benlasfer, N.; Birth, P.; Hegele, A.; Wachsmuth, F.; Apelt, L.; Stelzl, U.;

Phospho‐tyrosine dependent protein–protein interaction network

Abstract

AbstractPost‐translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two‐hybrid system employing human protein kinases for the analyses of phospho‐tyrosine (pY)‐dependent PPIs in a direct experimental, large‐scale approach. We identified 292 mostly novel pY‐dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co‐immunoprecipitation experiments from mammalian cells. About one‐sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY‐PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY‐mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY‐dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY‐dependent PPIs dictate cellular cancer phenotypes.

Keywords

Medicine (General), QH301-705.5, network biology, Articles, Phosphoproteins, R5-920, cancer signaling, Two-Hybrid System Techniques, Humans, Tyrosine, Protein Interaction Maps, post‐translational protein modification, yeast two‐hybrid, Biology (General), Phosphorylation, Protein Kinases, Protein Processing, Post-Translational, Protein Binding

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    73
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
73
Top 10%
Top 10%
Top 10%
Green
gold
Related to Research communities
Cancer Research