基于卷积长短时记忆神经网络的蛋白质二级结构预测

doi:10.16451/j.cnki.issn1003-6059.201806009

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Abstract

Since the interaction of different types of amino acid has an influence on the prediction of protein structure, convolutional neural networks and long short-term memory neural networks are integrated. A convolutional long short-term memory neural network is proposed to predict 8-class protein secondary structures. Firstly, the protein sequence is represented based on the amino acid sequence class feature and the amino acid structure profile feature. The local correlation characteristics between amino acid residues are extracted by the convolutional operations, and then the long-range interactions between the residues on protein sequences are extracted by the bi-directional long short-term memory network. Finally, the local correlation characteristics and long-range interactions between amino acid residues are employed to predict protein secondary structures. Experimental results show that the proposed model achieves a higher accuracy than the baselines and the framework has good scalability.

Received: 03 January 2018

ZTFLH:

TP 391

Fund:

Supported by National Natural Science Foundation of China(No.11661081), Integration of Cloud Computing and Big Data, Innovation of Science and Education(No.2017B00016), Training Project of Scientific and Technological Innovation Talents in Yunnan, Project of Innovative Research Team of Yunnan Province

About author:: (GUO Yanbu, master student. His research interests include deep learning and bioinformatics.)(LI Weihua(Corresponding author), Ph.D., associate professor. Her research interests include data mining and machine learning.)(WANG Bingyi, Ph.D., associate researcher. His research interests include plant molecular biology.)(JIN Chen, master student. His research interests include natural language processing and machine learning.)

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	GUO Yanbu
	LI Weihua
	WANG Bingyi
	JIN Chen

Cite this article:

GUO Yanbu,LI Weihua,WANG Bingyi等. Protein Secondary Structure Prediction Based on Convolutional Long Short-Time Memory Neural Networks[J]. , 2018, 31(6): 562-568.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201806009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I6/562

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