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ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures

Overview of attention for article published in BMC Bioinformatics, September 2014
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Title
ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures
Published in
BMC Bioinformatics, September 2014
DOI 10.1186/1471-2105-15-307
Pubmed ID
Authors

Jungkap Park, Kazuhiro Saitou

Abstract

Multibody potentials accounting for cooperative effects of molecular interactions have shown better accuracy than typical pairwise potentials. The main challenge in the development of such potentials is to find relevant structural features that characterize the tightly folded proteins. Also, the side-chains of residues adopt several specific, staggered conformations, known as rotamers within protein structures. Different molecular conformations result in different dipole moments and induce charge reorientations. However, until now modeling of the rotameric state of residues had not been incorporated into the development of multibody potentials for modeling non-bonded interactions in protein structures.

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X Demographics

The data shown below were collected from the profiles of 2 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 33%
Researcher 6 20%
Student > Bachelor 3 10%
Other 3 10%
Student > Postgraduate 2 7%
Other 5 17%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 30%
Chemistry 7 23%
Computer Science 5 17%
Biochemistry, Genetics and Molecular Biology 3 10%
Engineering 2 7%
Other 0 0%
Unknown 4 13%
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 18 September 2014.
All research outputs
#18,345,702
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#6,094
of 7,418 outputs
Outputs of similar age
#169,823
of 251,401 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 111 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 12th percentile – i.e., 12% 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 251,401 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.