WEDeepT3: predicting type Ⅲ secreted effectors based on word embedding and deep learning  

作　　者：Xiaofeng Fu Yang Yang 

机构地区：[1]Department of Computer Science and Engineering,Shanghai Jiao Tbng University,and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering,Shanghai 200240,China

出　　处：《Quantitative Biology》2019年第4期293-301,共9页定量生物学（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61972251).

摘　　要：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 Ⅲ 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 handdesigned feature and traditional machine learning methods.Methods:In this study,we propose a powerful predictor based on deep learning 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-ofthe-art methods,demonstrating the effectiveness of the new feature representation and the powerful learning ability of deep models.Conclusion:WEDeepT3 exploits both semantic information of Ar-mer fragments and evolutional information of protein sequences to accurately difYerentiate between T3SEs and non-T3SEs.WEDeepT3 is available at bcmi.sjtu.edu.cn/~yangyang/WEDeep

关 键 词：typeⅢsecreted effectors word2vector PSSM feature representation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]