Faculty of Economics and Administrative Sciences, Management Information Systems, Sivas Cumhuriyet University, Sivas, Turkey.
Department of Computer Engineering, Birjand University of Technology, Birjand, Iran.
Proteins. 2021 Oct;89(10):1277-1288. doi: 10.1002/prot.26149. Epub 2021 May 25.
There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.
蛋白质的三级结构与其功能密切相关。确定蛋白质三级结构的重要步骤之一是蛋白质二级结构预测（PSSP）。因此，更高精度地预测二级结构将为三级结构提供有价值的信息。最近，深度学习技术在包括 PSSP 在内的多个机器学习应用中取得了有希望的改进。在本文中，提出了一种基于卷积神经网络和图卷积网络的新型深度学习模型。PSIBLAST PSSM、HHMAKE PSSM、氨基酸理化性质与结构特征相结合，生成丰富的特征集。此外，使用贝叶斯优化来优化所提出的网络的超参数。所提出的模型 IGPRED 分别在 CullPDB、EVAset、CASP10、CASP11 和 CASP12 数据集上获得了 89.19%、86.34%、87.87%、85.76%和 86.54%的 Q3 准确率。
