A prediction model of nonclassical secreted protein based on deep learning

Most of the current nonclassical proteins prediction methods involve manual feature selection, such as constructing features of samples based on the physicochemical properties of proteins and position-specific scoring matrix (PSSM). However, these tasks require researchers to perform some tedious search work to obtain the physicochemical properties of proteins. This paper proposes an end-to-end nonclassical secreted protein prediction model based on deep learning, named DeepNCSPP, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long- and short-term memory and convolutional neural networks, respectively. Among the experiments conducted on the independent test dataset, DeepNCSPP achieved excellent results with an accuracy of 88.24%, Matthews coefficient (MCC) of 77.01%, and F1-score of 87.50%. Independent test dataset testing and 10-fold cross-validation show that DeepNCSPP achieves competitive performance with state-of-the-art methods and can be used as a reliable nonclassical secreted protein prediction model. A web server has been constructed for the convenience of researchers. The web link is . The source code of DeepNCSPP has been hosted on GitHub and is available online (). This paper proposes an end-to-end nonclassical secreted protein prediction model DeepNCSPP based on deep learning, which employs the protein sequence information and sequence statistics information as input to predict whether it is a nonclassical secreted protein. The protein sequence information and sequence statistics information are extracted using bidirectional long- and short-term memory and convolutional neural networks, respectively.