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Short linear motifs – ex nihilo evolution of protein regulation

Overview of attention for article published in Cell Communication and Signaling, November 2015
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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 (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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8 X users
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1 Wikipedia page

Citations

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173 Dimensions

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104 Mendeley
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Title
Short linear motifs – ex nihilo evolution of protein regulation
Published in
Cell Communication and Signaling, November 2015
DOI 10.1186/s12964-015-0120-z
Pubmed ID
Authors

Norman E. Davey, Martha S. Cyert, Alan M. Moses

Abstract

Short sequence motifs are ubiquitous across the three major types of biomolecules: hundreds of classes and thousands of instances of DNA regulatory elements, RNA motifs and protein short linear motifs (SLiMs) have been characterised. The increase in complexity of transcriptional, post-transcriptional and post-translational regulation in higher Eukaryotes has coincided with a significant expansion of motif use. But how did the eukaryotic cell acquire such a vast repertoire of motifs? In this review, we curate the available literature on protein motif evolution and discuss the evidence that suggests SLiMs can be acquired by mutations, insertions and deletions in disordered regions. We propose a mechanism of ex nihilo SLiM evolution - the evolution of a novel SLiM from "nothing" - adding a functional module to a previously non-functional region of protein sequence. In our model, hundreds of motif-binding domains in higher eukaryotic proteins connect simple motif specificities with useful functions to create a large functional motif space. Accessible peptides that match the specificity of these motif-binding domains are continuously created and destroyed by mutations in rapidly evolving disordered regions, creating a dynamic supply of new interactions that may have advantageous phenotypic novelty. This provides a reservoir of diversity to modify existing interaction networks. Evolutionary pressures will act on these motifs to retain beneficial instances. However, most will be lost on an evolutionary timescale as negative selection and genetic drift act on deleterious and neutral motifs respectively. In light of the parallels between the presented model and the evolution of motifs in the regulatory segments of genes and (pre-)mRNAs, we suggest our understanding of regulatory networks would benefit from the creation of a shared model describing the evolution of transcriptional, post-transcriptional and post-translational regulation.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 104 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 13%
Student > Bachelor 14 13%
Researcher 12 12%
Student > Master 7 7%
Student > Doctoral Student 3 3%
Other 6 6%
Unknown 48 46%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 41 39%
Agricultural and Biological Sciences 3 3%
Immunology and Microbiology 2 2%
Engineering 2 2%
Computer Science 1 <1%
Other 5 5%
Unknown 50 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 2016.
All research outputs
#3,790,587
of 22,833,393 outputs
Outputs from Cell Communication and Signaling
#74
of 991 outputs
Outputs of similar age
#63,130
of 386,452 outputs
Outputs of similar age from Cell Communication and Signaling
#3
of 6 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 991 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 92% 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 386,452 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 83% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.