Prediction of 8-state protein secondary structures by a novel deep learning architecture.

ABSTRACT:

Background

Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis.

Results

We present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the performance of protein secondary structure prediction. A local block comprised of convolutional filters and original input is designed for capturing local sequence features. The subsequent bidirectional recurrent neural network consisting of gated recurrent units can capture global conte

SUBMITTER: Zhang B 

PROVIDER: S-EPMC6090794 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature