Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory - Yuan, Lu; Ma, Yuming; Liu, Yihui (Frontiers in Bioengineering and Biotechnology, 2023)
Protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve PSSP. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale BTCN (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state PSSP can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining BLSTM with temporal convolutional network (TCN), reverse TCN (RTCN), multi-scale TCN (MSTCN), BTCN and MSBTCN, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.