Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring.Signal peptides play an important role in guiding and transferring transmembrane proteins and secreted proteins.  In this work, we present an improved approach, Signal-3L 3.0, for signal peptide recognition and cleavage-site prediction using a 3-layer hybrid method of integrating deep learning algorithms and window-based scoring.  There are three main components in the Signal-3L 3.0 prediction engine: (1) a deep bidirectional long short-term memory (Bi-LSTM) network with a soft self-attention learns abstract features from sequences to determine whether a query protein contains a signal peptide; (2) the statistics propensity window-based cleavage site screening method is applied to generate the set of candidate cleavage sites; (3) the prediction of a conditional random field with a hybrid convolutional neural network (CNN) and Bi-LSTM is fused with the window-based score for identifying the final unique cleavage site.  Experimental results on the benchmark datasets show that the new deep learning-driven Signal-3L 3.0 yields promising performance.  The online server of Signal-3L 3.0 is available at http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/.
Signal-3L
Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring.Signal peptides play an important role in guiding and transferring transmembrane proteins and secreted proteins.  In this work, we present an improved approach, Signal-3L 3.0, for signal peptide recognition and cleavage-site prediction using a 3-layer hybrid method of integrating deep learning algorithms and window-based scoring.  There are three main components in the Signal-3L 3.0 prediction engine: (1) a deep bidirectional long short-term memory (Bi-LSTM) network with a soft self-attention learns abstract features from sequences to determine whether a query protein contains a signal peptide; (2) the statistics propensity window-based cleavage site screening method is applied to generate the set of candidate cleavage sites; (3) the prediction of a conditional random field with a hybrid convolutional neural network (CNN) and Bi-LSTM is fused with the window-based score for identifying the final unique cleavage site.  Experimental results on the benchmark datasets show that the new deep learning-driven Signal-3L 3.0 yields promising performance.  The online server of Signal-3L 3.0 is available at http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/.
Signal-3L 3.0