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GPU-based detection of protein cavities using Gaussian surfaces

Overview of attention for article published in BMC Bioinformatics, November 2017
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3 tweeters

Citations

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9 Dimensions

Readers on

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15 Mendeley
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Title
GPU-based detection of protein cavities using Gaussian surfaces
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1913-4
Pubmed ID
Authors

Sérgio E. D. Dias, Ana Mafalda Martins, Quoc T. Nguyen, Abel J. P. Gomes

Abstract

Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity. We solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them. This article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 40%
Student > Ph. D. Student 4 27%
Student > Bachelor 2 13%
Student > Doctoral Student 2 13%
Professor > Associate Professor 1 7%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 27%
Computer Science 3 20%
Biochemistry, Genetics and Molecular Biology 3 20%
Environmental Science 1 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Other 2 13%
Unknown 1 7%

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 20 November 2017.
All research outputs
#7,604,283
of 12,167,359 outputs
Outputs from BMC Bioinformatics
#3,075
of 4,424 outputs
Outputs of similar age
#184,247
of 332,534 outputs
Outputs of similar age from BMC Bioinformatics
#120
of 208 outputs
Altmetric has tracked 12,167,359 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,424 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 332,534 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 208 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.