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DBETH: A Database of Bacterial Exotoxins for Human
Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the host's defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244994/ https://www.ncbi.nlm.nih.gov/pubmed/22102573 http://dx.doi.org/10.1093/nar/gkr942 |
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author | Chakraborty, Abhijit Ghosh, Sudeshna Chowdhary, Garisha Maulik, Ujjwal Chakrabarti, Saikat |
author_facet | Chakraborty, Abhijit Ghosh, Sudeshna Chowdhary, Garisha Maulik, Ujjwal Chakrabarti, Saikat |
author_sort | Chakraborty, Abhijit |
collection | PubMed |
description | Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the host's defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of action of the bacterial toxins at the cellular and molecular levels leading to pathogenicity. It is fair to assume that with time more and more unknown toxins will emerge not only by the discovery of newer species but also due to the genetic rearrangement of existing bacterial genomes. Hence, it is crucial to organize a systematic compilation and subsequent analyses of the inherent features of known bacterial toxins. We developed a Database for Bacterial ExoToxins (DBETH, http://www.hpppi.iicb.res.in/btox/), which contains sequence, structure, interaction network and analytical results for 229 toxins categorized within 24 mechanistic and activity types from 26 bacterial genuses. The main objective of this database is to provide a comprehensive knowledgebase for human pathogenic bacterial toxins where various important sequence, structure and physico-chemical property based analyses are provided. Further, we have developed a prediction server attached to this database which aims to identify bacterial toxin like sequences either by establishing homology with known toxin sequences/domains or by classifying bacterial toxin specific features using a support vector based machine learning techniques. |
format | Online Article Text |
id | pubmed-3244994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-32449942012-01-10 DBETH: A Database of Bacterial Exotoxins for Human Chakraborty, Abhijit Ghosh, Sudeshna Chowdhary, Garisha Maulik, Ujjwal Chakrabarti, Saikat Nucleic Acids Res Articles Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the host's defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of action of the bacterial toxins at the cellular and molecular levels leading to pathogenicity. It is fair to assume that with time more and more unknown toxins will emerge not only by the discovery of newer species but also due to the genetic rearrangement of existing bacterial genomes. Hence, it is crucial to organize a systematic compilation and subsequent analyses of the inherent features of known bacterial toxins. We developed a Database for Bacterial ExoToxins (DBETH, http://www.hpppi.iicb.res.in/btox/), which contains sequence, structure, interaction network and analytical results for 229 toxins categorized within 24 mechanistic and activity types from 26 bacterial genuses. The main objective of this database is to provide a comprehensive knowledgebase for human pathogenic bacterial toxins where various important sequence, structure and physico-chemical property based analyses are provided. Further, we have developed a prediction server attached to this database which aims to identify bacterial toxin like sequences either by establishing homology with known toxin sequences/domains or by classifying bacterial toxin specific features using a support vector based machine learning techniques. Oxford University Press 2012-01 2011-11-17 /pmc/articles/PMC3244994/ /pubmed/22102573 http://dx.doi.org/10.1093/nar/gkr942 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Chakraborty, Abhijit Ghosh, Sudeshna Chowdhary, Garisha Maulik, Ujjwal Chakrabarti, Saikat DBETH: A Database of Bacterial Exotoxins for Human |
title | DBETH: A Database of Bacterial Exotoxins for Human |
title_full | DBETH: A Database of Bacterial Exotoxins for Human |
title_fullStr | DBETH: A Database of Bacterial Exotoxins for Human |
title_full_unstemmed | DBETH: A Database of Bacterial Exotoxins for Human |
title_short | DBETH: A Database of Bacterial Exotoxins for Human |
title_sort | dbeth: a database of bacterial exotoxins for human |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3244994/ https://www.ncbi.nlm.nih.gov/pubmed/22102573 http://dx.doi.org/10.1093/nar/gkr942 |
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