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NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes
Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025766/ https://www.ncbi.nlm.nih.gov/pubmed/36526218 http://dx.doi.org/10.1016/j.gpb.2022.11.009 |
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author | Xu, Haodong Zhao, Zhongming |
author_facet | Xu, Haodong Zhao, Zhongming |
author_sort | Xu, Haodong |
collection | PubMed |
description | Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE. |
format | Online Article Text |
id | pubmed-10025766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100257662023-03-21 NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes Xu, Haodong Zhao, Zhongming Genomics Proteomics Bioinformatics Method Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE. Elsevier 2022-10 2022-12-13 /pmc/articles/PMC10025766/ /pubmed/36526218 http://dx.doi.org/10.1016/j.gpb.2022.11.009 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Method Xu, Haodong Zhao, Zhongming NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title | NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title_full | NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title_fullStr | NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title_full_unstemmed | NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title_short | NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes |
title_sort | netbce: an interpretable deep neural network for accurate prediction of linear b-cell epitopes |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025766/ https://www.ncbi.nlm.nih.gov/pubmed/36526218 http://dx.doi.org/10.1016/j.gpb.2022.11.009 |
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