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A Quantitative Chemical Proteomic Strategy for Profiling Phosphoprotein Phosphatases from Yeast to Humans*

Overview of attention for article published in Molecular and Cellular Proteomics, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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27 X users

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Title
A Quantitative Chemical Proteomic Strategy for Profiling Phosphoprotein Phosphatases from Yeast to Humans*
Published in
Molecular and Cellular Proteomics, September 2018
DOI 10.1074/mcp.ra118.000822
Pubmed ID
Authors

Scott P Lyons, Nicole P Jenkins, Isha Nasa, Meng S Choy, Mark E Adamo, Rebecca Page, Wolfgang Peti, Greg B Moorhead, Arminja N Kettenbach

Abstract

A 'tug-of-war' between kinases and phosphatases establishes the phosphorylation states of proteins. While serine and threonine phosphorylation can be catalyzed by more than 400 protein kinases, the majority of serine and threonine dephosphorylation is carried out by seven phosphoprotein phosphatases (PPPs). The PPP family consists of Protein Phosphatases 1 (PP1), 2A (PP2A), 2B (PP2B), 4 (PP4), 5 (PP5), 6 (PP6), and 7 (PP7). The imbalance in numbers between serine- and threonine-directed kinases and phosphatases led to the early belief that PPPs are unspecific and that kinases are the primary determinants of protein phosphorylation. However, it is now clear that PPPs achieve specificity through association with non-catalytic subunits to form multimeric holoenzymes, which expands the number of functionally distinct signaling entities to several hundred. Although there has been great progress in deciphering signaling by kinases, much less is known about phosphatases.We have developed a chemical proteomic strategy for the systematic interrogation of endogenous PPP catalytic subunits and their interacting proteins, including regulatory and scaffolding subunits (the "PPPome"). PP1, PP2A, PP4, PP5, and PP6 were captured using an immobilized, specific but non-selective PPP inhibitor microcystin-LR (MCLR), followed by protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a single analysis. Here, we combine this approach of Phosphatase Inhibitor Bead profiling and Mass Spectrometry (PIB-MS) with label-free and Tandem Mass Tag (TMT) quantification to map the PPPome in human cancer cell lines, mouse tissues, and yeast species, through which we identify cell and tissue type specific PPP expression patterns and discover new PPP interacting proteins.

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The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 16%
Researcher 8 14%
Student > Bachelor 5 9%
Student > Doctoral Student 4 7%
Professor 3 5%
Other 11 20%
Unknown 16 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 36%
Agricultural and Biological Sciences 8 14%
Unspecified 2 4%
Chemistry 2 4%
Nursing and Health Professions 1 2%
Other 6 11%
Unknown 17 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 15 December 2018.
All research outputs
#2,226,879
of 25,385,509 outputs
Outputs from Molecular and Cellular Proteomics
#316
of 3,221 outputs
Outputs of similar age
#45,250
of 351,242 outputs
Outputs of similar age from Molecular and Cellular Proteomics
#15
of 47 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,221 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 90% 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 351,242 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 47 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 68% of its contemporaries.