A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure
A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure
复制标题
提高蛋白质二级结构预测的深度卷积神经网络
DOI：
10.2174/1574893615666200120103050
复制
发表时间：
2020-01-01
影响因子：
4
通讯作者：
Wang, Yun
中科院分区：
文献类型：
--
作者：
Guo, Lin;Jiang, Qian;Wang, Yun
工作背景：蛋白质二级结构预测（PSSP）是生物信息学中的一项基本任务，有助于了解蛋白质的三维结构和生物学功能。许多基于神经网络的蛋白质二级结构预测方法已经被开发出来。目的：为了促进蛋白质二级结构预测的发展，提出一种基于深度卷积神经网络的蛋白质二级结构预测方法，用于蛋白质二级结构的八态和三态预测。方法：在该模型中，蛋白质的序列和进化信息经过预处理后作为多个输入特征。然后构建一个没有池化层和连接层的深度卷积神经网络来预测蛋白质的二级结构。采用L2正则化、批量归一化和dropout技术来避免过拟合，获得更好的预测性能，并采用改进的交叉熵作为损失函数。在CullPDB、CB 513、CASP 10和CASP 11数据集上，该模型的Q3预测结果分别为86.2%、84.5%、87.8%和84.7%。Q8预测结果分别为74.1%、70.5%、74.9%和71.3%。结论：提出了基于DCNN-SS深度卷积网络的PSSP方法，实验结果表明，DCNN-SS方法具有较好的性能。
Background: Protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy.Objective: To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure.Methods: In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function.Results: Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%.Conclusion: We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.