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An integration of deep learning with feature embedding for protein–protein interaction prediction

Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing compu...

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Autores principales: Yao, Yu, Du, Xiuquan, Diao, Yanyu, Zhu, Huaixu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585896/
https://www.ncbi.nlm.nih.gov/pubmed/31245182
http://dx.doi.org/10.7717/peerj.7126
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author Yao, Yu
Du, Xiuquan
Diao, Yanyu
Zhu, Huaixu
author_facet Yao, Yu
Du, Xiuquan
Diao, Yanyu
Zhu, Huaixu
author_sort Yao, Yu
collection PubMed
description Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.
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spelling pubmed-65858962019-06-26 An integration of deep learning with feature embedding for protein–protein interaction prediction Yao, Yu Du, Xiuquan Diao, Yanyu Zhu, Huaixu PeerJ Bioinformatics Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI. PeerJ Inc. 2019-06-17 /pmc/articles/PMC6585896/ /pubmed/31245182 http://dx.doi.org/10.7717/peerj.7126 Text en ©2019 Yao et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Yao, Yu
Du, Xiuquan
Diao, Yanyu
Zhu, Huaixu
An integration of deep learning with feature embedding for protein–protein interaction prediction
title An integration of deep learning with feature embedding for protein–protein interaction prediction
title_full An integration of deep learning with feature embedding for protein–protein interaction prediction
title_fullStr An integration of deep learning with feature embedding for protein–protein interaction prediction
title_full_unstemmed An integration of deep learning with feature embedding for protein–protein interaction prediction
title_short An integration of deep learning with feature embedding for protein–protein interaction prediction
title_sort integration of deep learning with feature embedding for protein–protein interaction prediction
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585896/
https://www.ncbi.nlm.nih.gov/pubmed/31245182
http://dx.doi.org/10.7717/peerj.7126
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