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A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies
Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808390/ https://www.ncbi.nlm.nih.gov/pubmed/33427589 http://dx.doi.org/10.1080/19420862.2020.1869406 |
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author | Richardson, Eve Galson, Jacob D. Kellam, Paul Kelly, Dominic F. Smith, Sarah E. Palser, Anne Watson, Simon Deane, Charlotte M. |
author_facet | Richardson, Eve Galson, Jacob D. Kellam, Paul Kelly, Dominic F. Smith, Sarah E. Palser, Anne Watson, Simon Deane, Charlotte M. |
author_sort | Richardson, Eve |
collection | PubMed |
description | Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis. Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid |
format | Online Article Text |
id | pubmed-7808390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-78083902021-01-29 A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies Richardson, Eve Galson, Jacob D. Kellam, Paul Kelly, Dominic F. Smith, Sarah E. Palser, Anne Watson, Simon Deane, Charlotte M. MAbs Report Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis. Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid Taylor & Francis 2021-01-11 /pmc/articles/PMC7808390/ /pubmed/33427589 http://dx.doi.org/10.1080/19420862.2020.1869406 Text en © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Report Richardson, Eve Galson, Jacob D. Kellam, Paul Kelly, Dominic F. Smith, Sarah E. Palser, Anne Watson, Simon Deane, Charlotte M. A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title | A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title_full | A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title_fullStr | A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title_full_unstemmed | A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title_short | A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
title_sort | computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies |
topic | Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808390/ https://www.ncbi.nlm.nih.gov/pubmed/33427589 http://dx.doi.org/10.1080/19420862.2020.1869406 |
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