Prediction of Type III Secreted Effectors Based on Word Embeddings for Protein Sequences
The type III secreted effectors (T3SEs) are virulence proteins that play an important role in the pathogenesis of Gram-negative bacteria. They are injected into the host cells by the pathogens, interfere with the immune system of the host cells, and help the growth and reproduction of the pathogens. It is a very challenging task to identify T3SEs because of the high diversity of their sequences and the lack of defined secretion signals. Moreover, their working mechanisms have not been fully understood yet. In order to speed up the recognition of T3SEs and the studies of type III secretion systems, computational tools for the prediction of T3SEs are in great demand. In this study, we regard the protein sequences as a special language. Inspired by the word2vec model in natural language processing, we convert the sequences into word embedding vectors in a similar manner with a specific segmentation strategy for protein sequences. And then we construct the T3SE predictor based on the new sequence feature representation. We conduct experiments on both mono-species data and multi-species data. The experimental results show that the new feature representation model has a competitive performance and can work together with the traditional features to enhance the identification of T3SEs.
KeywordsType III secreted effectors Word2vector Feature representation
This work has been supported by the Shanghai Municipal Natural Science Foundation (No. 16ZR1448700).
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