Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics...

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Main Authors: Elena Bencurova, Shishir K. Gupta, Edita Sarukhanyan, Thomas Dandekar
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Journal of Fungi
Subjects:
Online Access:http://www.mdpi.com/2309-608X/4/3/81
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spelling doaj-5cca0356af654fc5800c235e5b3c772e2020-11-24T21:08:11ZengMDPI AGJournal of Fungi2309-608X2018-07-01438110.3390/jof4030081jof4030081Identification of Antifungal Targets Based on Computer ModelingElena Bencurova0Shishir K. Gupta1Edita Sarukhanyan2Thomas Dandekar3Department of Bioinformatics, Am Hubland, Biozentrum, University of Würzburg, 97074 Würzburg, GermanyDepartment of Bioinformatics, Am Hubland, Biozentrum, University of Würzburg, 97074 Würzburg, GermanyDepartment of Bioinformatics, Am Hubland, Biozentrum, University of Würzburg, 97074 Würzburg, GermanyDepartment of Bioinformatics, Am Hubland, Biozentrum, University of Würzburg, 97074 Würzburg, GermanyAspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host–pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.http://www.mdpi.com/2309-608X/4/3/81Aspergillusmetabolic pathwayscomputational modellingdrug design
collection DOAJ
language English
format Article
sources DOAJ
author Elena Bencurova
Shishir K. Gupta
Edita Sarukhanyan
Thomas Dandekar
spellingShingle Elena Bencurova
Shishir K. Gupta
Edita Sarukhanyan
Thomas Dandekar
Identification of Antifungal Targets Based on Computer Modeling
Journal of Fungi
Aspergillus
metabolic pathways
computational modelling
drug design
author_facet Elena Bencurova
Shishir K. Gupta
Edita Sarukhanyan
Thomas Dandekar
author_sort Elena Bencurova
title Identification of Antifungal Targets Based on Computer Modeling
title_short Identification of Antifungal Targets Based on Computer Modeling
title_full Identification of Antifungal Targets Based on Computer Modeling
title_fullStr Identification of Antifungal Targets Based on Computer Modeling
title_full_unstemmed Identification of Antifungal Targets Based on Computer Modeling
title_sort identification of antifungal targets based on computer modeling
publisher MDPI AG
series Journal of Fungi
issn 2309-608X
publishDate 2018-07-01
description Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host–pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
topic Aspergillus
metabolic pathways
computational modelling
drug design
url http://www.mdpi.com/2309-608X/4/3/81
work_keys_str_mv AT elenabencurova identificationofantifungaltargetsbasedoncomputermodeling
AT shishirkgupta identificationofantifungaltargetsbasedoncomputermodeling
AT editasarukhanyan identificationofantifungaltargetsbasedoncomputermodeling
AT thomasdandekar identificationofantifungaltargetsbasedoncomputermodeling
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