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The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery
With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avo...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570205/ https://www.ncbi.nlm.nih.gov/pubmed/28472505 http://dx.doi.org/10.1093/nar/gkx360 |
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author | Alanjary, Mohammad Kronmiller, Brent Adamek, Martina Blin, Kai Weber, Tilmann Huson, Daniel Philmus, Benjamin Ziemert, Nadine |
author_facet | Alanjary, Mohammad Kronmiller, Brent Adamek, Martina Blin, Kai Weber, Tilmann Huson, Daniel Philmus, Benjamin Ziemert, Nadine |
author_sort | Alanjary, Mohammad |
collection | PubMed |
description | With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avoid suicide, an antibiotic producer harbors resistance genes often found within the same biosynthetic gene cluster (BGC) responsible for manufacturing the antibiotic. Existing mining tools are excellent at detecting BGCs or resistant genes in general, but provide little help in prioritizing and identifying gene clusters for compounds active against specific and novel targets. Here we introduce the ‘Antibiotic Resistant Target Seeker’ (ARTS) available at https://arts.ziemertlab.com. ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets. The aim of this web server is to automate the screening of large amounts of sequence data and to focus on the most promising strains that produce antibiotics with new modes of action. ARTS integrates target directed genome mining methods, antibiotic gene cluster predictions and ‘essential gene screening’ to provide an interactive page for rapid identification of known and putative targets in BGCs. |
format | Online Article Text |
id | pubmed-5570205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-55702052017-08-29 The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery Alanjary, Mohammad Kronmiller, Brent Adamek, Martina Blin, Kai Weber, Tilmann Huson, Daniel Philmus, Benjamin Ziemert, Nadine Nucleic Acids Res Web Server Issue With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avoid suicide, an antibiotic producer harbors resistance genes often found within the same biosynthetic gene cluster (BGC) responsible for manufacturing the antibiotic. Existing mining tools are excellent at detecting BGCs or resistant genes in general, but provide little help in prioritizing and identifying gene clusters for compounds active against specific and novel targets. Here we introduce the ‘Antibiotic Resistant Target Seeker’ (ARTS) available at https://arts.ziemertlab.com. ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets. The aim of this web server is to automate the screening of large amounts of sequence data and to focus on the most promising strains that produce antibiotics with new modes of action. ARTS integrates target directed genome mining methods, antibiotic gene cluster predictions and ‘essential gene screening’ to provide an interactive page for rapid identification of known and putative targets in BGCs. Oxford University Press 2017-07-03 2017-05-02 /pmc/articles/PMC5570205/ /pubmed/28472505 http://dx.doi.org/10.1093/nar/gkx360 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Web Server Issue Alanjary, Mohammad Kronmiller, Brent Adamek, Martina Blin, Kai Weber, Tilmann Huson, Daniel Philmus, Benjamin Ziemert, Nadine The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title | The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title_full | The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title_fullStr | The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title_full_unstemmed | The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title_short | The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
title_sort | antibiotic resistant target seeker (arts), an exploration engine for antibiotic cluster prioritization and novel drug target discovery |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570205/ https://www.ncbi.nlm.nih.gov/pubmed/28472505 http://dx.doi.org/10.1093/nar/gkx360 |
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