The description and understanding of protein structure rely on secondary structure heavily. Secondary structure determination and prediction are widely used in protein structure related research. The secondary structure prediction methods based on NMR chemical shifts are convenient to use, so they are popular in protein NMR research. In recent years, there is significant improvement in deep neural network, which is consequently applied in many search fields. Here we proposed a deep neural network based on bidirectional long short term memory (biLSTM) to predict protein 3-state secondary structure using NMR chemical shifts of backbone nuclei. Compared with the existing methods of the same sort, the accuracy of the proposed method was improved. And a web server was built to provide secondary structure prediction service using this method.
supporting information v1
Supporting Information 2. proteins in training and validation dataset
Supporting Information 3. protein information of test dataset and prediction by 3 methods