EP3

EP3 predicts type III secreted effectors (T3SEs) in pathogenic bacteria to enable identification of proteins secreted via Type III secretion systems (T3SS).

Key Features:

• Ensemble predictor: Integrates multiple predictive models to identify T3SEs.
• High accuracy and robustness: Demonstrates high accuracy and robustness in distinguishing T3SEs from unknown proteins.
• Training and testing: Models were rigorously trained and tested on datasets to support predictive performance.
• Overfitting mitigation: Addresses overfitting to ensure reliable performance across diverse datasets.
• Model integration: Predictive capabilities are enhanced by integrating outputs from multiple models.
• Pathogen relevance: Applicable to T3SS-bearing pathogens, including Dysentery bacillus, Salmonella typhimurium, Vibrio cholerae, and pathogenic Escherichia coli.

Scientific Applications:

• T3SE identification: Identification of type III secreted effectors in bacterial proteomes.
• Effector discrimination: Differentiation of T3SEs from non-secreted or unknown proteins.
• Pathogenesis analysis: Analysis of T3SS-mediated bacterial-host interactions and contributions to pathogenicity.
• Candidate prioritization: Prioritization of candidate effector proteins for experimental validation.

Methodology:

Ensemble prediction integrating multiple trained models; models were rigorously trained and tested and measures were applied to mitigate overfitting.