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Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets

Overview of attention for article published in BMC Bioinformatics, January 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
2 tweeters
patent
1 patent

Citations

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12 Dimensions

Readers on

mendeley
59 Mendeley
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4 CiteULike
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Title
Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-39
Pubmed ID
Authors

Paul Ashford, David S Moss, Alexander Alex, Siew K Yeap, Alice Povia, Irene Nobeli, Mark A Williams

Abstract

Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 5%
Germany 2 3%
United States 2 3%
Belarus 1 2%
South Africa 1 2%
Spain 1 2%
Czechia 1 2%
Unknown 48 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 34%
Researcher 16 27%
Student > Master 10 17%
Professor > Associate Professor 3 5%
Student > Bachelor 2 3%
Other 7 12%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 29%
Chemistry 14 24%
Biochemistry, Genetics and Molecular Biology 13 22%
Computer Science 6 10%
Engineering 4 7%
Other 3 5%
Unknown 2 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 June 2016.
All research outputs
#3,000,524
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,305
of 4,576 outputs
Outputs of similar age
#30,020
of 137,781 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 31 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 70% 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 137,781 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 77% of its contemporaries.
We're also able to compare this research output to 31 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 70% of its contemporaries.