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Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

Overview of attention for article published in BMC Bioinformatics, February 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters
googleplus
1 Google+ user

Citations

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14 Dimensions

Readers on

mendeley
75 Mendeley
citeulike
2 CiteULike
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Title
Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0898-8
Pubmed ID
Authors

Ruben Cloete, Ekow Oppon, Edwin Murungi, Wolf-Dieter Schubert, Alan Christoffels

Abstract

Increasing resistance to anti-tuberculosis drugs has driven the need for developing new drugs. Resources such as the tropical disease research (TDR) target database and AssessDrugTarget can help to prioritize putative drug targets. Hower, these resources do not necessarily map to metabolic pathways and the targets are not involved in dormancy. In this study, we specifically identify drug resistance pathways to allow known drug resistant mutations in one target to be offset by inhibiting another enzyme of the same metabolic pathway. One of the putative targets, Rv1712, was analysed by modelling its three dimensional structure and docking potential inhibitors. We mapped 18 TB drug resistance gene products to 15 metabolic pathways critical for mycobacterial growth and latent TB by screening publicly available microarray data. Nine putative targets, Rv1712, Rv2984, Rv2194, Rv1311, Rv1305, Rv2195, Rv1622c, Rv1456c and Rv2421c, were found to be essential, to lack a close human homolog, and to share >67 % sequence identity and >87 % query coverage with mycobacterial orthologs. A structural model was generated for Rv1712, subjected to molecular dynamic simulation, and identified 10 compounds with affinities better than that for the ligand cytidine-5'-monophosphate (C5P). Each compound formed more interactions with the protein than C5P. We focused on metabolic pathways associated with bacterial drug resistance and proteins unique to pathogenic bacteria to identify novel putative drug targets. The ten compounds identified in this study should be considered for experimental studies to validate their potential as inhibitors of Rv1712.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Argentina 1 1%
Brazil 1 1%
Unknown 72 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Student > Bachelor 14 19%
Researcher 13 17%
Student > Ph. D. Student 13 17%
Student > Postgraduate 5 7%
Other 10 13%
Unknown 6 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 28%
Agricultural and Biological Sciences 13 17%
Pharmacology, Toxicology and Pharmaceutical Science 6 8%
Medicine and Dentistry 6 8%
Engineering 5 7%
Other 14 19%
Unknown 10 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 February 2016.
All research outputs
#5,103,720
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#2,235
of 4,169 outputs
Outputs of similar age
#128,864
of 344,114 outputs
Outputs of similar age from BMC Bioinformatics
#93
of 147 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. This one has received more attention than most of these and is in the 50th percentile.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 44th percentile – i.e., 44% 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 344,114 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 61% of its contemporaries.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.