Background:The type Ⅲ secreted effectors (T3SEs) are one of the indispensable proteins in the growth and reproduction of Gram-negative bacteria.In particular,the pathogenesis of Gram-negative bacteria depends on the type Ⅲ secreted effectors,and by injecting T3SEs into a host cell,the host cell’s immunity can be destroyed.The high diversity of T3SE sequences and the lack of defined secretion signals make it difficult to identify and predict.Moreover,the related study of the pathological system associated with T3SE remains a hot topic in bioinformatics.Some computational tools have been developed to meet the growing demand for the recognition of T3SEs and the studies of type Ⅲll secretion systems (T3SS).Although these tools can help biological experiments in certain procedures,there is still room for improvement,even for the current best model,as the existing methods adopt hand-designed feature and traditional machine leing methods.Methods:In this study,we propose a powerful predictor based on deep leing methods,called WEDeepT3.Our work consists mainly of three key steps.First,we train word embedding vectors for protein sequences in a large-scale amino acid sequence database.Second,we combine the word vectors with traditional features extracted from protein sequences,like PSSM,to construct a more comprehensive feature representation.Finally,we construct a deep neural network model in the prediction of type Ⅲ secreted effectors.Results:The feature representation of WEDeepT3 consists of both word embedding and position-specific features.Working together with convolutional neural networks,the new model achieves superior performance to the state-of-the-art methods,demonstrating the effectiveness of the new feature representation and the powerful leing ability of deep models.Conclusion:WEDeepT3 exploits both semantic information of k-mer fragments and evolutional information of protein sequences to accurately differentiate between T3SEs and non-T3SEs.WEDeepT3 is available at bcmi.sjtu.edu.cn/～yangyang/WEDeepT3.html.