S4TE

S4TE predicts Type IV effector proteins (T4Es) in bacterial genomes and analyzes their eukaryotic-like domains, localization signals, C-terminal translocation signals, amino acid characteristics, genomic G+C composition, and local gene density to support studies of bacterial pathogenesis and host–pathogen interactions.

Key Features:

• Prediction of T4Es: Predicts Type IV effector proteins and candidate effectors from bacterial genome sequences using signatures such as eukaryotic-like domains, localization signals, and C-terminal translocation signals.
• Customizable search parameters: Supports adjustment of search parameters and thresholds to tune predictions for specific genome sequences.
• Effector feature characterization: Analyzes amino acid characteristics, eukaryotic-like domains, localization signals, C-terminal translocation signals, genomic G+C composition, and local gene density for candidate effectors.
• Comparative genomics: Compares putative T4E repertoires across up to four bacterial strains, identifies orthologous T4Es, and outputs intersection gene lists (Venn diagram outputs).
• Model updating with validated effectors: Integrates newly published experimentally validated T4Es to update predictive data and improve predictive accuracy over time.
• Amino acid–based identification: Identifies candidate effectors based on amino acid characteristics rather than strict sequence conservation.

Scientific Applications:

• Effector characterization: Characterizing bacterial Type IV effector proteins and their molecular features relevant to infection processes.
• Host–pathogen interaction studies: Investigating host specificity and manipulation of host cell processes by T4Es secreted via the type IV secretion system.
• Virulence factor identification: Identifying putative virulence factors and candidate targets for downstream experimental validation.
• Comparative repertoire analysis: Comparing T4E repertoires across strains to study effector distribution, orthology, and potential evolutionary adaptations.

Methodology:

Predicts T4Es by detecting eukaryotic-like domains, localization signals, and C-terminal translocation signals; analyzes amino acid characteristics, genomic G+C composition, and local gene density; identifies orthologous T4Es across up to four strains and produces Venn diagram intersection gene lists; updates predictive data with newly published experimentally validated T4Es.