Abstract
Abstract—The type III secretion system (T3SS) is a complex structure which allows gram-negative pathogens to destroy eukaryotic cell biology by injecting virulence factors directly into the host cell cytoplasm. Composed of around 30 proteins, T3SS is among the most complex secretion systems identified in Gram-negative bacteria. Since type III secreted effectors (T3SEs) are essential for the pathogenicity, identification of T3SEs is one of the core problems in computational biology. This paper puts forward a new method for the prediction of T3SEs. The method is a sequence-based approach which can extract useful features from amino acid sequences. By calculating the frequency of the features from different segments of protein sequences, the data set is represented by the feature vectors and classified by Support Vector Machine (SVM). The experimental results show superiority over other available approaches on classification accuracy.
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