Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment. In recent years, there was a significant improvement observed in deep neural networks, which had been applied in many research 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. While comparing with the existing methods the proposed method showed better prediction accuracy. Based on the proposed method, a web server has been built to provide protein secondary structure prediction service.

蛋白质二级结构提供了丰富的结构信息，因此对蛋白质结构的描述和理解在很大程度上依赖于它。因此，二级结构的鉴定或预测在蛋白质研究中起着重要作用。在蛋白质核磁共振研究中，与 NOESY 实验提供的基于核间距的传统测定方法相比，从化学位移预测二级结构更方便。近年来，深度神经网络取得了显着的进步，已应用于许多研究领域。在这里，我们提出了一种基于双向长短期记忆 (biLSTM) 的深度神经网络，以使用主干核的 NMR 化学位移来预测蛋白质的三态二级结构。与现有方法相比，该方法显示出更好的预测精度。基于所提出的方法，建立了一个网络服务器，提供蛋白质二级结构预测服务。