ArticleNucleic acids research2012
DBETH: a Database of Bacterial Exotoxins for Human.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Characterization, genomic insights and anti-biofilm potential of phage vB_PaeM_PKMS3, a lytic Pbunavirus capable of infecting clinical Pseudomonas aeruginosa isolates.Antonie van Leeuwenhoek · 2026Article
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- Toxin data quality: a critical examination of bacterial exotoxins and animal toxins.BMC research notes · 2025Article
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- Hypothetical Proteins ofMicroorganisms · 2023Article
- I am better than I look: genome based safety assessment of the probiotic Lactiplantibacillus plantarum IS-10506.BMC genomics · 2023Article
- Characterization and comprehensive genome analysis of novel bacteriophage, vB_Kpn_ZCKp20p, with lytic and anti-biofilm potential against clinical multidrug-resistantFrontiers in cellular and infection microbiology · 2023Article
- Distinctive signatures of pathogenic and antibiotic resistant potentials in the hadal microbiome.Environmental microbiome · 2022Article
- Identification and Characterization of vB_PreP_EPr2, a Lytic Bacteriophage of Pan-Drug ResistantViruses · 2022Article
- Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation.PHAGE (New Rochelle, N.Y.) · 2021Article
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- Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.International journal of molecular sciences · 2021Article
- PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data.Microbiome · 2021Article
- Antibiotics create a shift from mutualism to competition in human gut communities with a longer-lasting impact on fungi than bacteria.Microbiome · 2020Article
- Advanced Proteomics as a Powerful Tool for Studying Toxins of Human Bacterial Pathogens.Toxins · 2019Review
- ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.Database : the journal of biological databases and curation · 2019Article
- Bioinformatic Exploration of Metal-Binding Proteome of Zoonotic PathogenFrontiers in genetics · 2019Article
- Gut colonization by a novel Clostridium species is associated with the onset of epizootic rabbit enteropathy.Veterinary research · 2018Article
- Recent Trends in System-Scale Integrative Approaches for Discovering Protective Antigens Against Mycobacterial Pathogens.Frontiers in genetics · 2018Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.