  Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverages convolutional neural networks with different kernel
sizes to extract multiscale local contextual features. In addition, considering
long-range dependencies existing in amino acid sequences, we set up a
bidirectional neural network consisting of gated recurrent unit to capture
global contextual features. Furthermore, multi-task learning is utilized to
predict secondary structure labels and amino-acid solvent accessibility
simultaneously. Our proposed deep network demonstrates its effectiveness by
achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public
benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11.
Our model and results are publicly available.
