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Computationally-driven identification of antibody epitopes
Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody e...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739537/ https://www.ncbi.nlm.nih.gov/pubmed/29199956 http://dx.doi.org/10.7554/eLife.29023 |
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author | Hua, Casey K Gacerez, Albert T Sentman, Charles L Ackerman, Margaret E Choi, Yoonjoo Bailey-Kellogg, Chris |
author_facet | Hua, Casey K Gacerez, Albert T Sentman, Charles L Ackerman, Margaret E Choi, Yoonjoo Bailey-Kellogg, Chris |
author_sort | Hua, Casey K |
collection | PubMed |
description | Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries. |
format | Online Article Text |
id | pubmed-5739537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-57395372017-12-22 Computationally-driven identification of antibody epitopes Hua, Casey K Gacerez, Albert T Sentman, Charles L Ackerman, Margaret E Choi, Yoonjoo Bailey-Kellogg, Chris eLife Structural Biology and Molecular Biophysics Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries. eLife Sciences Publications, Ltd 2017-12-04 /pmc/articles/PMC5739537/ /pubmed/29199956 http://dx.doi.org/10.7554/eLife.29023 Text en © 2017, Hua et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Structural Biology and Molecular Biophysics Hua, Casey K Gacerez, Albert T Sentman, Charles L Ackerman, Margaret E Choi, Yoonjoo Bailey-Kellogg, Chris Computationally-driven identification of antibody epitopes |
title | Computationally-driven identification of antibody epitopes |
title_full | Computationally-driven identification of antibody epitopes |
title_fullStr | Computationally-driven identification of antibody epitopes |
title_full_unstemmed | Computationally-driven identification of antibody epitopes |
title_short | Computationally-driven identification of antibody epitopes |
title_sort | computationally-driven identification of antibody epitopes |
topic | Structural Biology and Molecular Biophysics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739537/ https://www.ncbi.nlm.nih.gov/pubmed/29199956 http://dx.doi.org/10.7554/eLife.29023 |
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