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Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach
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 metaboli...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911368/ https://www.ncbi.nlm.nih.gov/pubmed/27379244 http://dx.doi.org/10.3389/fmolb.2016.00022 |
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author | Kaltdorf, Martin Srivastava, Mugdha Gupta, Shishir K. Liang, Chunguang Binder, Jasmin Dietl, Anna-Maria Meir, Zohar Haas, Hubertus Osherov, Nir Krappmann, Sven Dandekar, Thomas |
author_facet | Kaltdorf, Martin Srivastava, Mugdha Gupta, Shishir K. Liang, Chunguang Binder, Jasmin Dietl, Anna-Maria Meir, Zohar Haas, Hubertus Osherov, Nir Krappmann, Sven Dandekar, Thomas |
author_sort | Kaltdorf, Martin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4911368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49113682016-07-04 Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach Kaltdorf, Martin Srivastava, Mugdha Gupta, Shishir K. Liang, Chunguang Binder, Jasmin Dietl, Anna-Maria Meir, Zohar Haas, Hubertus Osherov, Nir Krappmann, Sven Dandekar, Thomas Front Mol Biosci Molecular Biosciences 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. Frontiers Media S.A. 2016-06-17 /pmc/articles/PMC4911368/ /pubmed/27379244 http://dx.doi.org/10.3389/fmolb.2016.00022 Text en Copyright © 2016 Kaltdorf, Srivastava, Gupta, Liang, Binder, Dietl, Meir, Haas, Osherov, Krappmann and Dandekar. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Kaltdorf, Martin Srivastava, Mugdha Gupta, Shishir K. Liang, Chunguang Binder, Jasmin Dietl, Anna-Maria Meir, Zohar Haas, Hubertus Osherov, Nir Krappmann, Sven Dandekar, Thomas Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title | Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title_full | Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title_fullStr | Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title_full_unstemmed | Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title_short | Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach |
title_sort | systematic identification of anti-fungal drug targets by a metabolic network approach |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911368/ https://www.ncbi.nlm.nih.gov/pubmed/27379244 http://dx.doi.org/10.3389/fmolb.2016.00022 |
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