TY - JOUR
T1 - Protein secondary structure prediction by using deep learning method
AU - Wang, Yangxu
AU - Mao, Hua
AU - Yi, Zhang
PY - 2017/2/15
Y1 - 2017/2/15
N2 - The prediction of protein structures directly from amino acid sequences is one of the biggest challenges in computational biology. It can be divided into several independent sub-problems in which protein secondary structure (SS) prediction is fundamental. Many computational methods have been proposed for SS prediction problem. Few of them can model well both the sequence-structure mapping relationship between input protein features and SS, and the interaction relationship among residues which are both important for SS prediction. In this paper, we proposed a deep recurrent encoder–decoder networks called Secondary Structure Recurrent Encoder–Decoder Networks (SSREDNs) to solve this SS prediction problem. Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain. A series of techniques are also used in this paper to refine the model’s performance. The proposed model is applied to the open dataset CullPDB and CB513. Experimental results demonstrate that our method can improve both Q3 and Q8 accuracy compared with some public available methods. For Q8 prediction problem, it achieves 68.20% and 73.1% accuracy on CB513 and CullPDB dataset in fewer epochs better than the previous state-of-art method.
AB - The prediction of protein structures directly from amino acid sequences is one of the biggest challenges in computational biology. It can be divided into several independent sub-problems in which protein secondary structure (SS) prediction is fundamental. Many computational methods have been proposed for SS prediction problem. Few of them can model well both the sequence-structure mapping relationship between input protein features and SS, and the interaction relationship among residues which are both important for SS prediction. In this paper, we proposed a deep recurrent encoder–decoder networks called Secondary Structure Recurrent Encoder–Decoder Networks (SSREDNs) to solve this SS prediction problem. Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain. A series of techniques are also used in this paper to refine the model’s performance. The proposed model is applied to the open dataset CullPDB and CB513. Experimental results demonstrate that our method can improve both Q3 and Q8 accuracy compared with some public available methods. For Q8 prediction problem, it achieves 68.20% and 73.1% accuracy on CB513 and CullPDB dataset in fewer epochs better than the previous state-of-art method.
KW - Deep learning
KW - Secondary structure prediction
KW - Encoder-decoder networks
KW - Recurrent neural networks
U2 - 10.1016/j.knosys.2016.11.015
DO - 10.1016/j.knosys.2016.11.015
M3 - Article
VL - 118
SP - 115
EP - 123
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
SN - 0950-7051
ER -