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Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies

Overview of attention for article published in PLoS Computational Biology, March 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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2 blogs
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12 X users
googleplus
1 Google+ user

Citations

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90 Dimensions

Readers on

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195 Mendeley
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18 CiteULike
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Title
Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002403
Pubmed ID
Authors

Nicholas Furnham, Ian Sillitoe, Gemma L. Holliday, Alison L. Cuff, Roman A. Laskowski, Christine A. Orengo, Janet M. Thornton

Abstract

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 5%
United Kingdom 7 4%
France 3 2%
Portugal 2 1%
Switzerland 1 <1%
Austria 1 <1%
Australia 1 <1%
Italy 1 <1%
Germany 1 <1%
Other 7 4%
Unknown 162 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 60 31%
Student > Ph. D. Student 47 24%
Student > Bachelor 15 8%
Student > Master 12 6%
Professor 11 6%
Other 39 20%
Unknown 11 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 91 47%
Biochemistry, Genetics and Molecular Biology 58 30%
Computer Science 9 5%
Chemistry 9 5%
Environmental Science 2 1%
Other 9 5%
Unknown 17 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 08 June 2015.
All research outputs
#1,757,664
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#1,510
of 8,960 outputs
Outputs of similar age
#9,449
of 168,139 outputs
Outputs of similar age from PLoS Computational Biology
#14
of 109 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 83% 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 168,139 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.