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Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways

Overview of attention for article published in Frontiers in Genetics, May 2014
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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways
Published in
Frontiers in Genetics, May 2014
DOI 10.3389/fgene.2014.00115
Pubmed ID
Authors

Francesca Sacco, Karsten Boldt, Alberto Calderone, Simona Panni, Serena Paoluzi, Luisa Castagnoli, Marius Ueffing, Gianni Cesareni

Abstract

Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 4%
Netherlands 1 2%
France 1 2%
Italy 1 2%
Unknown 45 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 28%
Student > Ph. D. Student 14 28%
Student > Master 6 12%
Student > Postgraduate 4 8%
Student > Bachelor 3 6%
Other 6 12%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 34%
Biochemistry, Genetics and Molecular Biology 16 32%
Medicine and Dentistry 3 6%
Chemistry 2 4%
Computer Science 2 4%
Other 7 14%
Unknown 3 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2015.
All research outputs
#6,695,780
of 22,754,104 outputs
Outputs from Frontiers in Genetics
#2,004
of 11,758 outputs
Outputs of similar age
#64,580
of 227,497 outputs
Outputs of similar age from Frontiers in Genetics
#41
of 114 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 11,758 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 227,497 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.