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Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad

Overview of attention for article published in Cell Communication and Signaling, November 2015
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Title
Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad
Published in
Cell Communication and Signaling, November 2015
DOI 10.1186/s12964-015-0121-y
Pubmed ID
Authors

Toby J. Gibson, Holger Dinkel, Kim Van Roey, Francesca Diella

Abstract

It has become clear in outline though not yet in detail how cellular regulatory and signalling systems are constructed. The essential machines are protein complexes that effect regulatory decisions by undergoing internal changes of state. Subcomponents of these cellular complexes are assembled into molecular switches. Many of these switches employ one or more short peptide motifs as toggles that can move between one or more sites within the switch system, the simplest being on-off switches. Paradoxically, these motif modules (termed short linear motifs or SLiMs) are both hugely abundant but difficult to research. So despite the many successes in identifying short regulatory protein motifs, it is thought that only the "tip of the iceberg" has been exposed. Experimental and bioinformatic motif discovery remain challenging and error prone. The advice presented in this article is aimed at helping researchers to uncover genuine protein motifs, whilst avoiding the pitfalls that lead to reports of false discovery.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Germany 1 1%
Canada 1 1%
Brazil 1 1%
Unknown 94 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 21%
Student > Bachelor 17 17%
Researcher 16 16%
Student > Master 16 16%
Professor > Associate Professor 6 6%
Other 13 13%
Unknown 9 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 36%
Agricultural and Biological Sciences 26 27%
Computer Science 4 4%
Medicine and Dentistry 3 3%
Chemistry 3 3%
Other 13 13%
Unknown 14 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 December 2015.
All research outputs
#18,431,664
of 22,834,308 outputs
Outputs from Cell Communication and Signaling
#768
of 991 outputs
Outputs of similar age
#278,394
of 386,431 outputs
Outputs of similar age from Cell Communication and Signaling
#5
of 5 outputs
Altmetric has tracked 22,834,308 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 991 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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