Title |
Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach
|
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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. |
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Geographical breakdown
Country | Count | As % |
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United States | 1 | 25% |
Switzerland | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 66 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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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 % |
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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% |