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Advances in antibody discovery from human BCR repertoires
Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631452/ https://www.ncbi.nlm.nih.gov/pubmed/36338807 http://dx.doi.org/10.3389/fbinf.2022.1044975 |
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author | Xu, Zichang Ismanto, Hendra S. Zhou, Hao Saputri, Dianita S. Sugihara, Fuminori Standley, Daron M. |
author_facet | Xu, Zichang Ismanto, Hendra S. Zhou, Hao Saputri, Dianita S. Sugihara, Fuminori Standley, Daron M. |
author_sort | Xu, Zichang |
collection | PubMed |
description | Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans. |
format | Online Article Text |
id | pubmed-9631452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96314522022-11-04 Advances in antibody discovery from human BCR repertoires Xu, Zichang Ismanto, Hendra S. Zhou, Hao Saputri, Dianita S. Sugihara, Fuminori Standley, Daron M. Front Bioinform Bioinformatics Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans. Frontiers Media S.A. 2022-10-20 /pmc/articles/PMC9631452/ /pubmed/36338807 http://dx.doi.org/10.3389/fbinf.2022.1044975 Text en Copyright © 2022 Xu, Ismanto, Zhou, Saputri, Sugihara and Standley. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Xu, Zichang Ismanto, Hendra S. Zhou, Hao Saputri, Dianita S. Sugihara, Fuminori Standley, Daron M. Advances in antibody discovery from human BCR repertoires |
title | Advances in antibody discovery from human BCR repertoires |
title_full | Advances in antibody discovery from human BCR repertoires |
title_fullStr | Advances in antibody discovery from human BCR repertoires |
title_full_unstemmed | Advances in antibody discovery from human BCR repertoires |
title_short | Advances in antibody discovery from human BCR repertoires |
title_sort | advances in antibody discovery from human bcr repertoires |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631452/ https://www.ncbi.nlm.nih.gov/pubmed/36338807 http://dx.doi.org/10.3389/fbinf.2022.1044975 |
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