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Binding interface change and cryptic variation in the evolution of protein-protein interactions

Overview of attention for article published in BMC Ecology and Evolution, February 2016
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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Title
Binding interface change and cryptic variation in the evolution of protein-protein interactions
Published in
BMC Ecology and Evolution, February 2016
DOI 10.1186/s12862-016-0608-1
Pubmed ID
Authors

Ryan M. Ames, David Talavera, Simon G. Williams, David L. Robertson, Simon C. Lovell

Abstract

Physical interactions between proteins are essential for almost all biological functions and systems. To understand the evolution of function it is therefore important to understand the evolution of molecular interactions. Of key importance is the evolution of binding specificity, the set of interactions made by a protein, since change in specificity can lead to "rewiring" of interaction networks. Unfortunately, the interfaces through which proteins interact are complex, typically containing many amino-acid residues that collectively must contribute to binding specificity as well as binding affinity, structural integrity of the interface and solubility in the unbound state. In order to study the relationship between interface composition and binding specificity, we make use of paralogous pairs of yeast proteins. Immediately after duplication these paralogues will have identical sequences and protein products that make an identical set of interactions. As the sequences diverge, we can correlate amino-acid change in the interface with any change in the specificity of binding. We show that change in interface regions correlates only weakly with change in specificity, and many variants in interfaces are functionally equivalent. We show that many of the residue replacements within interfaces are silent with respect to their contribution to binding specificity. We conclude that such functionally-equivalent change has the potential to contribute to evolutionary plasticity in interfaces by creating cryptic variation, which in turn may provide the raw material for functional innovation and coevolution.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Italy 1 3%
Canada 1 3%
Unknown 34 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 38%
Student > Ph. D. Student 10 27%
Student > Master 6 16%
Student > Bachelor 3 8%
Professor 2 5%
Other 0 0%
Unknown 2 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 46%
Agricultural and Biological Sciences 10 27%
Chemistry 3 8%
Computer Science 2 5%
Mathematics 1 3%
Other 2 5%
Unknown 2 5%
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 04 March 2016.
All research outputs
#7,355,930
of 25,373,627 outputs
Outputs from BMC Ecology and Evolution
#1,676
of 3,714 outputs
Outputs of similar age
#95,341
of 312,020 outputs
Outputs of similar age from BMC Ecology and Evolution
#30
of 72 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 52% 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 312,020 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 68% of its contemporaries.
We're also able to compare this research output to 72 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 58% of its contemporaries.