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Computational mining of MHC class II epitopes for the development of universal immunogenic proteins
The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role i...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963548/ https://www.ncbi.nlm.nih.gov/pubmed/35349604 http://dx.doi.org/10.1371/journal.pone.0265644 |
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author | Saylor, Kyle Donnan, Ben Zhang, Chenming |
author_facet | Saylor, Kyle Donnan, Ben Zhang, Chenming |
author_sort | Saylor, Kyle |
collection | PubMed |
description | The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts. |
format | Online Article Text |
id | pubmed-8963548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89635482022-03-30 Computational mining of MHC class II epitopes for the development of universal immunogenic proteins Saylor, Kyle Donnan, Ben Zhang, Chenming PLoS One Research Article The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts. Public Library of Science 2022-03-29 /pmc/articles/PMC8963548/ /pubmed/35349604 http://dx.doi.org/10.1371/journal.pone.0265644 Text en © 2022 Saylor et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Saylor, Kyle Donnan, Ben Zhang, Chenming Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title | Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title_full | Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title_fullStr | Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title_full_unstemmed | Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title_short | Computational mining of MHC class II epitopes for the development of universal immunogenic proteins |
title_sort | computational mining of mhc class ii epitopes for the development of universal immunogenic proteins |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963548/ https://www.ncbi.nlm.nih.gov/pubmed/35349604 http://dx.doi.org/10.1371/journal.pone.0265644 |
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