T3_MM

T3_MM predicts Type III Secretion System (T3SS) effector proteins in gram-negative bacteria by analyzing N-terminal amino acid composition to distinguish effectors from non-effectors.

Key Features:

• Amino Acid Composition (Aac) Analysis: Analyzes the amino acid composition of the N-terminal 100 amino acids and compares compositions between known T3S effectors and non-T3S proteins, with attention to how each residue influences adjacent residues.
• Markov Model Framework: Employs a Markov model to calculate total Aac conditional probability differences and to assess likelihood ratios that a sequence is T3S versus non-T3S based on composition and positional constraints.
• Statistical Modeling: Constructs statistical models in which known T3S and non-T3S protein scores approximate two distinct normal distributions to facilitate classification.
• Performance Metrics: Validated by 5-fold cross-validation with reported sensitivity of 83.9% and specificity of 90.3%.
• Comparative Performance: Reported as more robust, accurate, simple, and statistically quantitative compared to other existing models for predicting T3S proteins.

Scientific Applications:

• Bacterial pathogenesis research: Identification of T3SS effectors to investigate mechanisms by which gram-negative bacteria modulate host cellular processes.
• Host–pathogen interaction studies: Characterization of effector repertoires to study bacterial strategies for immune evasion and host manipulation.
• Genomic effector discovery: Screening of proteomes or genomic datasets to uncover novel T3SS effector candidates for experimental validation.

Methodology:

Compute amino acid composition (Aac) of the N-terminal 100 residues, compare compositions between known T3S and non-T3S proteins with attention to residue adjacency, apply a Markov model to calculate conditional probability differences and likelihood ratios under positional constraints, fit statistical models where T3S and non-T3S scores approximate two normal distributions, and evaluate performance by 5-fold cross-validation (sensitivity 83.9%, specificity 90.3%).