Predicting subcellular location of protein with evolution information and sequence-based deep learning

Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins....

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Bibliographic Details
Published in:BMC bioinformatics 2021-10, Vol.22 (1), p.1-515, Article 515
Main Authors: Liao, Zhijun, Pan, Gaofeng, Sun, Chao, Tang, Jijun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amino acid sequence
Amino acids
Artificial intelligence
Artificial neural networks
Classification
Datasets
Deep learning
Evolution
Evolution information
Feature extraction
Learning algorithms
Localization
Long short-term memory
Machine learning
Methods
Multiple label classification
Neural networks
Predictions
Protein research
Protein sequence
Proteins
Subcellular prediction
Support vector machines
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Summary:Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations. Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848. The experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04404-0