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The evolutionary rate of antibacterial drug targets

Overview of attention for article published in BMC Bioinformatics, February 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
5 Wikipedia pages

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
66 Mendeley
citeulike
1 CiteULike
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Title
The evolutionary rate of antibacterial drug targets
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-36
Pubmed ID
Authors

Arkadiusz Gladki, Szymon Kaczanowski, Pawel Szczesny, Piotr Zielenkiewicz

Abstract

One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Belgium 2 3%
India 1 2%
Netherlands 1 2%
United Kingdom 1 2%
Poland 1 2%
Unknown 58 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Student > Bachelor 10 15%
Researcher 10 15%
Student > Master 10 15%
Student > Postgraduate 4 6%
Other 11 17%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 41%
Biochemistry, Genetics and Molecular Biology 10 15%
Medicine and Dentistry 6 9%
Computer Science 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 8 12%
Unknown 10 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 11 June 2020.
All research outputs
#5,526,326
of 18,144,179 outputs
Outputs from BMC Bioinformatics
#2,292
of 6,361 outputs
Outputs of similar age
#73,131
of 257,462 outputs
Outputs of similar age from BMC Bioinformatics
#78
of 195 outputs
Altmetric has tracked 18,144,179 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 6,361 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 62% 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 257,462 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 195 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 57% of its contemporaries.