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Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction
Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Chinese Medical Association Publishing House. Published by Elsevier BV.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166638/ https://www.ncbi.nlm.nih.gov/pubmed/37362223 http://dx.doi.org/10.1016/j.bsheal.2023.04.003 |
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author | Xie, Pengfei Zhuang, Jujuan Tian, Geng Yang, Jialiang |
author_facet | Xie, Pengfei Zhuang, Jujuan Tian, Geng Yang, Jialiang |
author_sort | Xie, Pengfei |
collection | PubMed |
description | Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human–SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy. |
format | Online Article Text |
id | pubmed-10166638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Chinese Medical Association Publishing House. Published by Elsevier BV. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101666382023-05-09 Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction Xie, Pengfei Zhuang, Jujuan Tian, Geng Yang, Jialiang Biosaf Health Original Research Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human–SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy. Chinese Medical Association Publishing House. Published by Elsevier BV. 2023-06 2023-04-28 /pmc/articles/PMC10166638/ /pubmed/37362223 http://dx.doi.org/10.1016/j.bsheal.2023.04.003 Text en © 2023 Chinese Medical Association Publishing House. Published by Elsevier BV. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Xie, Pengfei Zhuang, Jujuan Tian, Geng Yang, Jialiang Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title | Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title_full | Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title_fullStr | Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title_full_unstemmed | Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title_short | Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction |
title_sort | emvirus: an embedding-based neural framework for human-virus protein-protein interactions prediction |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166638/ https://www.ncbi.nlm.nih.gov/pubmed/37362223 http://dx.doi.org/10.1016/j.bsheal.2023.04.003 |
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