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A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures
Rapid determination of the antigenicity of influenza A virus could help identify the antigenic variants in time. Currently, there is a lack of computational models for predicting antigenic variants of some common hemagglutinin (HA) subtypes of influenza A viruses. By means of sequence analysis, we d...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292743/ https://www.ncbi.nlm.nih.gov/pubmed/28165025 http://dx.doi.org/10.1038/srep42051 |
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author | Peng, Yousong Wang, Dayan Wang, Jianhong Li, Kenli Tan, Zhongyang Shu, Yuelong Jiang, Taijiao |
author_facet | Peng, Yousong Wang, Dayan Wang, Jianhong Li, Kenli Tan, Zhongyang Shu, Yuelong Jiang, Taijiao |
author_sort | Peng, Yousong |
collection | PubMed |
description | Rapid determination of the antigenicity of influenza A virus could help identify the antigenic variants in time. Currently, there is a lack of computational models for predicting antigenic variants of some common hemagglutinin (HA) subtypes of influenza A viruses. By means of sequence analysis, we demonstrate here that multiple HA subtypes of influenza A virus undergo similar mutation patterns of HA1 protein (the immunogenic part of HA). Further analysis on the antigenic variation of influenza A virus H1N1, H3N2 and H5N1 showed that the amino acid residues’ contribution to antigenic variation highly differed in these subtypes, while the regional bands, defined based on their distance to the top of HA1, played conserved roles in antigenic variation of these subtypes. Moreover, the computational models for predicting antigenic variants based on regional bands performed much better in the testing HA subtype than those did based on amino acid residues. Therefore, a universal computational model, named PREDAV-FluA, was built based on the regional bands to predict the antigenic variants for all HA subtypes of influenza A viruses. The model achieved an accuracy of 0.77 when tested with avian influenza H9N2 viruses. It may help for rapid identification of antigenic variants in influenza surveillance. |
format | Online Article Text |
id | pubmed-5292743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52927432017-02-10 A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures Peng, Yousong Wang, Dayan Wang, Jianhong Li, Kenli Tan, Zhongyang Shu, Yuelong Jiang, Taijiao Sci Rep Article Rapid determination of the antigenicity of influenza A virus could help identify the antigenic variants in time. Currently, there is a lack of computational models for predicting antigenic variants of some common hemagglutinin (HA) subtypes of influenza A viruses. By means of sequence analysis, we demonstrate here that multiple HA subtypes of influenza A virus undergo similar mutation patterns of HA1 protein (the immunogenic part of HA). Further analysis on the antigenic variation of influenza A virus H1N1, H3N2 and H5N1 showed that the amino acid residues’ contribution to antigenic variation highly differed in these subtypes, while the regional bands, defined based on their distance to the top of HA1, played conserved roles in antigenic variation of these subtypes. Moreover, the computational models for predicting antigenic variants based on regional bands performed much better in the testing HA subtype than those did based on amino acid residues. Therefore, a universal computational model, named PREDAV-FluA, was built based on the regional bands to predict the antigenic variants for all HA subtypes of influenza A viruses. The model achieved an accuracy of 0.77 when tested with avian influenza H9N2 viruses. It may help for rapid identification of antigenic variants in influenza surveillance. Nature Publishing Group 2017-02-06 /pmc/articles/PMC5292743/ /pubmed/28165025 http://dx.doi.org/10.1038/srep42051 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Peng, Yousong Wang, Dayan Wang, Jianhong Li, Kenli Tan, Zhongyang Shu, Yuelong Jiang, Taijiao A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title | A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title_full | A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title_fullStr | A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title_full_unstemmed | A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title_short | A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures |
title_sort | universal computational model for predicting antigenic variants of influenza a virus based on conserved antigenic structures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292743/ https://www.ncbi.nlm.nih.gov/pubmed/28165025 http://dx.doi.org/10.1038/srep42051 |
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