OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

被引:8
|
作者
Zhao, Yawu [1 ]
Liu, Yihui [1 ]
机构
[1] Qilu Univ Technol, Shandong Acad Sci, Sch Comp Sci & Technol, Jinan, Peoples R China
来源
PLOS ONE | 2021年 / 16卷 / 02期
基金
中国国家自然科学基金;
关键词
MACHINE;
D O I
10.1371/journal.pone.0245982
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.
引用
收藏
页数:14
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