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Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-inf...

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Detalles Bibliográficos
Autores principales: Azulay, Asaf, Cohen-Lavi, Liel, Friedman, Lilach M., McGargill, Maureen A., Hertz, Tomer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475849/
https://www.ncbi.nlm.nih.gov/pubmed/37671022
http://dx.doi.org/10.1016/j.crmeth.2023.100566
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author Azulay, Asaf
Cohen-Lavi, Liel
Friedman, Lilach M.
McGargill, Maureen A.
Hertz, Tomer
author_facet Azulay, Asaf
Cohen-Lavi, Liel
Friedman, Lilach M.
McGargill, Maureen A.
Hertz, Tomer
author_sort Azulay, Asaf
collection PubMed
description The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb’s binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch—an epitope prediction method that is based on a mAb’s binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50–60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.
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spelling pubmed-104758492023-09-05 Mapping antibody footprints using binding profiles Azulay, Asaf Cohen-Lavi, Liel Friedman, Lilach M. McGargill, Maureen A. Hertz, Tomer Cell Rep Methods Article The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb’s binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch—an epitope prediction method that is based on a mAb’s binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50–60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies. Elsevier 2023-08-22 /pmc/articles/PMC10475849/ /pubmed/37671022 http://dx.doi.org/10.1016/j.crmeth.2023.100566 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Azulay, Asaf
Cohen-Lavi, Liel
Friedman, Lilach M.
McGargill, Maureen A.
Hertz, Tomer
Mapping antibody footprints using binding profiles
title Mapping antibody footprints using binding profiles
title_full Mapping antibody footprints using binding profiles
title_fullStr Mapping antibody footprints using binding profiles
title_full_unstemmed Mapping antibody footprints using binding profiles
title_short Mapping antibody footprints using binding profiles
title_sort mapping antibody footprints using binding profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475849/
https://www.ncbi.nlm.nih.gov/pubmed/37671022
http://dx.doi.org/10.1016/j.crmeth.2023.100566
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