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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...

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Detalles Bibliográficos
Autores principales: Chakraborty, Abhijit, Ghosh, Sudeshna, Chowdhary, Garisha, Maulik, Ujjwal, Chakrabarti, Saikat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
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.
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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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