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Native structure-based modeling and simulation of biomolecular systems per mouse click

Overview of attention for article published in BMC Bioinformatics, August 2014
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3 X users
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1 Facebook page

Citations

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6 Dimensions

Readers on

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41 Mendeley
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3 CiteULike
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Title
Native structure-based modeling and simulation of biomolecular systems per mouse click
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-292
Pubmed ID
Authors

Benjamin Lutz, Claude Sinner, Stefan Bozic, Ivan Kondov, Alexander Schug

Abstract

Molecular dynamics (MD) simulations provide valuable insight into biomolecular systems at the atomic level. Notwithstanding the ever-increasing power of high performance computers current MD simulations face several challenges: the fastest atomic movements require time steps of a few femtoseconds which are small compared to biomolecular relevant timescales of milliseconds or even seconds for large conformational motions. At the same time, scalability to a large number of cores is limited mostly due to long-range interactions. An appealing alternative to atomic-level simulations is coarse-graining the resolution of the system or reducing the complexity of the Hamiltonian to improve sampling while decreasing computational costs. Native structure-based models, also called Gō-type models, are based on energy landscape theory and the principle of minimal frustration. They have been tremendously successful in explaining fundamental questions of, e.g., protein folding, RNA folding or protein function. At the same time, they are computationally sufficiently inexpensive to run complex simulations on smaller computing systems or even commodity hardware. Still, their setup and evaluation is quite complex even though sophisticated software packages support their realization.

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

Geographical breakdown

Country Count As %
Germany 1 2%
Brazil 1 2%
India 1 2%
United Kingdom 1 2%
Canada 1 2%
Unknown 36 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 24%
Student > Ph. D. Student 9 22%
Student > Master 8 20%
Student > Bachelor 4 10%
Professor 3 7%
Other 6 15%
Unknown 1 2%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 7 17%
Agricultural and Biological Sciences 6 15%
Physics and Astronomy 5 12%
Chemistry 4 10%
Other 8 20%
Unknown 3 7%
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 30 August 2014.
All research outputs
#15,304,580
of 22,761,738 outputs
Outputs from BMC Bioinformatics
#5,373
of 7,273 outputs
Outputs of similar age
#136,395
of 236,210 outputs
Outputs of similar age from BMC Bioinformatics
#82
of 108 outputs
Altmetric has tracked 22,761,738 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 7,273 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 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 236,210 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.