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Estimation of protein function using template-based alignment of enzyme active sites

Overview of attention for article published in BMC Bioinformatics, March 2014
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3 X users

Citations

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29 Mendeley
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6 CiteULike
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Title
Estimation of protein function using template-based alignment of enzyme active sites
Published in
BMC Bioinformatics, March 2014
DOI 10.1186/1471-2105-15-87
Pubmed ID
Authors

Brett Hanson, Charles Westin, Mario Rosa, Alexander Grier, Mikhail Osipovitch, Madolyn L MacDonald, Greg Dodge, Paule M Boli, Cyprian W Corwin, Haeja Kessler, Talia McKay, Herbert J Bernstein, Paul A Craig

Abstract

The accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues.

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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
France 1 3%
India 1 3%
Czechia 1 3%
Japan 1 3%
United States 1 3%
Unknown 23 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Researcher 7 24%
Student > Master 6 21%
Student > Bachelor 3 10%
Professor 2 7%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 41%
Biochemistry, Genetics and Molecular Biology 5 17%
Computer Science 3 10%
Chemistry 3 10%
Physics and Astronomy 1 3%
Other 2 7%
Unknown 3 10%
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 28 March 2014.
All research outputs
#16,099,609
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#5,488
of 7,454 outputs
Outputs of similar age
#134,819
of 227,015 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 100 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 227,015 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.