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Protein Scaffolds Can Enhance the Bistability of Multisite Phosphorylation Systems

Overview of attention for article published in PLoS Computational Biology, June 2012
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2 X users

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Title
Protein Scaffolds Can Enhance the Bistability of Multisite Phosphorylation Systems
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002551
Pubmed ID
Authors

Carlo Chan, Xinfeng Liu, Liming Wang, Lee Bardwell, Qing Nie, Germán Enciso

Abstract

The phosphorylation of a substrate at multiple sites is a common protein modification that can give rise to important structural and electrostatic changes. Scaffold proteins can enhance protein phosphorylation by facilitating an interaction between a protein kinase enzyme and its target substrate. In this work we consider a simple mathematical model of a scaffold protein and show that under specific conditions, the presence of the scaffold can substantially raise the likelihood that the resulting system will exhibit bistable behavior. This phenomenon is especially pronounced when the enzymatic reactions have sufficiently large K(M), compared to the concentration of the target substrate. We also find for a closely related model that bistable systems tend to have a specific kinetic conformation. Using deficiency theory and other methods, we provide a number of necessary conditions for bistability, such as the presence of multiple phosphorylation sites and the dependence of the scaffold binding/unbinding rates on the number of phosphorylated sites.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Australia 1 2%
Argentina 1 2%
Canada 1 2%
Japan 1 2%
Russia 1 2%
Unknown 47 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 35%
Student > Ph. D. Student 12 22%
Professor 6 11%
Student > Doctoral Student 4 7%
Student > Postgraduate 2 4%
Other 6 11%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 28%
Biochemistry, Genetics and Molecular Biology 12 22%
Mathematics 6 11%
Computer Science 4 7%
Physics and Astronomy 4 7%
Other 5 9%
Unknown 8 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 June 2012.
All research outputs
#16,061,963
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#6,970
of 8,964 outputs
Outputs of similar age
#108,125
of 177,501 outputs
Outputs of similar age from PLoS Computational Biology
#79
of 106 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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 177,501 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.