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基于深度学习的八类蛋白质二级结构预测算法

     

摘要

蛋白质二级结构预测是结构生物学中的一个重要问题.针对八类蛋白质二级结构预测,提出了一种基于递归神经网络和前馈神经网络的深度学习预测算法.该算法通过双向递归神经网络建模氨基酸间的局部和长程相互作用,递归神经网络的隐层输出进一步送入到三层的前馈神经网络以便进行八类蛋白质二级结构预测.实验结果表明,提出的算法在CB513数据集上达到了67.9%的Q8预测精度,显著地优于SSpro8和SC-GSN.%Predicting protein secondary structure is an important issue in structural biology.Aiming at the prediction of eight-class protein secondary structure,a novel deep learning prediction algorithm was proposed by combining recurrent neural network and feed-forward neural network.A bidirectional recurrent neural network was used to model locality and long-range interaction between amino acid residues in protein.In order to predict the eight-class protein secondary structure,the outputs of the hidden layer in the bidirectional recurrent neural network were further fed to the three-layer feed-forward neural network.Experimental results show that the proposed method achieves Q8 accuracy of 67.9% on the CB513 dataset,which is significantly better than SSpro8 and SC-GSN (Supervised Convolutional-Generative Stochastic Network).

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