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Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

Overview of attention for article published in Frontiers in Molecular Biosciences, June 2016
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Title
Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach
Published in
Frontiers in Molecular Biosciences, June 2016
DOI 10.3389/fmolb.2016.00022
Pubmed ID
Authors

Martin Kaltdorf, Mugdha Srivastava, Shishir K. Gupta, Chunguang Liang, Jasmin Binder, Anna-Maria Dietl, Zohar Meir, Hubertus Haas, Nir Osherov, Sven Krappmann, Thomas Dandekar

Abstract

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

X Demographics

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The data shown below were collected from the profiles of 4 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Brazil 1 1%
Unknown 66 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 29%
Student > Master 10 15%
Student > Ph. D. Student 9 13%
Student > Bachelor 6 9%
Student > Doctoral Student 5 7%
Other 8 12%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 29%
Biochemistry, Genetics and Molecular Biology 16 24%
Immunology and Microbiology 6 9%
Chemistry 4 6%
Engineering 3 4%
Other 6 9%
Unknown 13 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 February 2017.
All research outputs
#14,266,546
of 22,877,793 outputs
Outputs from Frontiers in Molecular Biosciences
#1,126
of 3,804 outputs
Outputs of similar age
#201,504
of 352,647 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#10
of 22 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,804 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 66% 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 352,647 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 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 50% of its contemporaries.