Sensitive prediction of bacterial type IV effectors

Abstract

Diverse human pathogens secret effector proteins into host cells via the type IV secretion system (T4SS). Effector proteins are important elements in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction will be of great value. This paper introduces five types of feature representation schemes for prediction of effectors from sequence namely, amino acid composition, dipeptide composition, three-peptide composition, BLAST similarity scores and pseudo amino acid composition. SVM, k-NN, Naïve Bayes and Fisher LDA classification methods were performed in a newly established the dataset to predict T4SS effectors with using generated features. The experimental results indicate that classification methods we used are useful to discriminate IVA and IVB effectors with positive rates 83,3%, 96,5% respectively. The overall accuracy of 95.5% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences.