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Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity

Cross-reactivity between different human coronaviruses (HCoVs) might contribute to COVID-19 outcomes. Here, we aimed to predict conserved peptides among different HCoVs that could elicit cross-reacting B cell and T cell responses. Three hundred fifty-one full-genome sequences of HCoVs, including SAR...

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Autores principales: Mathew, Shilu, Fakhroo, Aisha D., Smatti, Maria, Al Thani, Asmaa A., Yassine, Hadi M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744044/
https://www.ncbi.nlm.nih.gov/pubmed/35006282
http://dx.doi.org/10.1007/s00251-021-01250-5
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author Mathew, Shilu
Fakhroo, Aisha D.
Smatti, Maria
Al Thani, Asmaa A.
Yassine, Hadi M.
author_facet Mathew, Shilu
Fakhroo, Aisha D.
Smatti, Maria
Al Thani, Asmaa A.
Yassine, Hadi M.
author_sort Mathew, Shilu
collection PubMed
description Cross-reactivity between different human coronaviruses (HCoVs) might contribute to COVID-19 outcomes. Here, we aimed to predict conserved peptides among different HCoVs that could elicit cross-reacting B cell and T cell responses. Three hundred fifty-one full-genome sequences of HCoVs, including SARS-CoV-2 (51), SARS-CoV-1 (50), MERS-CoV (50), and common cold species OC43 (50), NL63 (50), 229E (50), and HKU1 (50) were downloaded aligned using Geneious Prime 20.20. Identification of epitopes in the conserved regions of HCoVs was carried out using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. Further, we identified sequences that bind multiple common MHC and modeled the three-dimensional structures of the protein regions. The search yielded 73 linear and 35 discontinuous epitopes. A total of 16 B-cell and 19 T-cell epitopes were predicted through a comprehensive bioinformatic screening of conserved regions derived from HCoVs. The 16 potentially cross-reactive B-cell epitopes included 12 human proteins and four viral proteins among the linear epitopes. Likewise, we identified 19 potentially cross-reactive T-cell epitopes covering viral proteins. Interestingly, two conserved regions: LSFVSLAICFVIEQF (NSP2) and VVHSVNSLVSSMEVQSL (spike), contained several matches that were described epitopes for SARS-CoV. Most of the predicted B cells were buried within the SARS-CoV-2 protein regions’ functional domains, whereas T-cell stretched close to the functional domains. Additionally, most SARS-CoV-2 predicted peptides (80%) bound to different HLA types associated with autoimmune diseases. We identified a set of potential B cell and T cell epitopes derived from the HCoVs that could contribute to different diseases manifestation, including autoimmune disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00251-021-01250-5.
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spelling pubmed-87440442022-01-10 Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity Mathew, Shilu Fakhroo, Aisha D. Smatti, Maria Al Thani, Asmaa A. Yassine, Hadi M. Immunogenetics Original Article Cross-reactivity between different human coronaviruses (HCoVs) might contribute to COVID-19 outcomes. Here, we aimed to predict conserved peptides among different HCoVs that could elicit cross-reacting B cell and T cell responses. Three hundred fifty-one full-genome sequences of HCoVs, including SARS-CoV-2 (51), SARS-CoV-1 (50), MERS-CoV (50), and common cold species OC43 (50), NL63 (50), 229E (50), and HKU1 (50) were downloaded aligned using Geneious Prime 20.20. Identification of epitopes in the conserved regions of HCoVs was carried out using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. Further, we identified sequences that bind multiple common MHC and modeled the three-dimensional structures of the protein regions. The search yielded 73 linear and 35 discontinuous epitopes. A total of 16 B-cell and 19 T-cell epitopes were predicted through a comprehensive bioinformatic screening of conserved regions derived from HCoVs. The 16 potentially cross-reactive B-cell epitopes included 12 human proteins and four viral proteins among the linear epitopes. Likewise, we identified 19 potentially cross-reactive T-cell epitopes covering viral proteins. Interestingly, two conserved regions: LSFVSLAICFVIEQF (NSP2) and VVHSVNSLVSSMEVQSL (spike), contained several matches that were described epitopes for SARS-CoV. Most of the predicted B cells were buried within the SARS-CoV-2 protein regions’ functional domains, whereas T-cell stretched close to the functional domains. Additionally, most SARS-CoV-2 predicted peptides (80%) bound to different HLA types associated with autoimmune diseases. We identified a set of potential B cell and T cell epitopes derived from the HCoVs that could contribute to different diseases manifestation, including autoimmune disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00251-021-01250-5. Springer Berlin Heidelberg 2022-01-10 2022 /pmc/articles/PMC8744044/ /pubmed/35006282 http://dx.doi.org/10.1007/s00251-021-01250-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Mathew, Shilu
Fakhroo, Aisha D.
Smatti, Maria
Al Thani, Asmaa A.
Yassine, Hadi M.
Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title_full Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title_fullStr Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title_full_unstemmed Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title_short Immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
title_sort immunoinformatics prediction of potential immunodominant epitopes from human coronaviruses and association with autoimmunity
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744044/
https://www.ncbi.nlm.nih.gov/pubmed/35006282
http://dx.doi.org/10.1007/s00251-021-01250-5
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