Computational Identification of Discriminating Features of Pathogenic and Symbiotic Type III Secreted Effector Proteins

Abstract

Type III secretion systems (T3SS) deliver bacterial proteins, or “effectors”, into eukaryotic host cells, inducing physiological responses in the hosts. Effector proteins have been considered virulence factors of pathogenic bacteria, but T3SSs have now been found in symbiotic bacteria as well. Whether any physicochemical difference exists between the two types of effectors remains unknown. In this work, we combined computational statistical and machine learning methods to identify features that could be responsible for the difference. For computational statistical method we used generalized Bayesian information criterion and kernel logistic regression, and for machine learning method we used support vector machine. It was clearly shown that differences in amino acid composition exist between pathogenic and symbiotic effector proteins. All identified discriminating features were those of amino acid composition and average residue weight, and their classification performance could be nearly identical to that using all physicochemical features, with sensitivity and specificity of over 80%. Further analysis on the seven discriminating features by graphical modeling revealed three dominant features among them. Moreover, amino acid regions that were distinctive for the seven features were explored by sliding window analysis. This study provides a methodological basis and important insights into the functional differences between pathogenic and symbiotic T3SS effectors.