DBETH

DBETH provides a curated repository of sequences, structures, interaction networks, and analytical results for 229 protein exotoxins from 26 genera to support research on human pathogenic bacterial exotoxins.

Key Features:

• Comprehensive Data Collection: Includes sequences, structures, interaction networks, and analytical results for a wide array of bacterial exotoxins.
• Dataset Scope: Contains detailed information on 229 protein toxins derived from 26 different genera of pathogenic bacteria.
• Toxin Classification: Categorizes toxins into 24 distinct classes based on mechanistic and activity types.
• Analytical Tools: Provides sequence-, structure-, and physico-chemical property-based analyses of known bacterial toxins.
• Prediction Server: Implements a support vector machine-based predictor to identify toxin-like sequences, establish homology with known toxin sequences or domains, and classify bacterial toxin-specific features.

Scientific Applications:

• Pathogenicity and Mechanism Studies: Supports investigations into how exotoxins contribute to bacterial survival and pathogenicity within host environments.
• Novel Toxin Discovery and Characterization: Enables identification and characterization of putative novel toxins, including those arising in emerging pathogens or via genetic rearrangements.

Methodology:

DBETH organizes and analyzes sequences, structures, interaction networks, and physico-chemical properties derived from high-throughput genome sequencing and structural determination techniques, and provides a prediction server based on support vector machine learning for identifying and classifying toxin-like sequences and features.