Grain protein function prediction based on improved FCN and bidirectional LSTM

With the development of high-throughput sequencing technologies, predicting grain protein function from amino acid sequences based on intelligent model has become one of the significant tasks in bioinformatics. The soybean, maize, indica, and japonica are selected as grain dataset from the UniProtKB. Aiming at the problem of neglecting the sequence order of amino acids and the long-term dependence between amino acids, the PBiLSTM-FCN model is proposed for predicting grain protein function in this paper. The sequence of amino acid sequences is considered in the Fully Convolutional Networks (FCN), and the long-term dependence between amino acids is addressed by the bidirectional Long Short-Term Memory network (BiLSTM). The experimental results show that the PBiLSTM-FCN model is superior to existing models, and can predict more accurately by solving the problem of capturing long-range dependencies and the order of amino acid sequences. Finally, the interpretability analyses are performed by the actual protein function compared with the predicted protein function which proves the effectiveness of the PBiLSTM-FCN model in predicting grain protein function.