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Fragment-based computational design of antibodies targeting structured epitopes
De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody sca...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651861/ https://www.ncbi.nlm.nih.gov/pubmed/36367941 http://dx.doi.org/10.1126/sciadv.abp9540 |
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author | Aguilar Rangel, Mauricio Bedwell, Alice Costanzi, Elisa Taylor, Ross J. Russo, Rosaria Bernardes, Gonçalo J. L. Ricagno, Stefano Frydman, Judith Vendruscolo, Michele Sormanni, Pietro |
author_facet | Aguilar Rangel, Mauricio Bedwell, Alice Costanzi, Elisa Taylor, Ross J. Russo, Rosaria Bernardes, Gonçalo J. L. Ricagno, Stefano Frydman, Judith Vendruscolo, Michele Sormanni, Pietro |
author_sort | Aguilar Rangel, Mauricio |
collection | PubMed |
description | De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes. |
format | Online Article Text |
id | pubmed-9651861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96518612022-11-23 Fragment-based computational design of antibodies targeting structured epitopes Aguilar Rangel, Mauricio Bedwell, Alice Costanzi, Elisa Taylor, Ross J. Russo, Rosaria Bernardes, Gonçalo J. L. Ricagno, Stefano Frydman, Judith Vendruscolo, Michele Sormanni, Pietro Sci Adv Physical and Materials Sciences De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes. American Association for the Advancement of Science 2022-11-11 /pmc/articles/PMC9651861/ /pubmed/36367941 http://dx.doi.org/10.1126/sciadv.abp9540 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (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 | Physical and Materials Sciences Aguilar Rangel, Mauricio Bedwell, Alice Costanzi, Elisa Taylor, Ross J. Russo, Rosaria Bernardes, Gonçalo J. L. Ricagno, Stefano Frydman, Judith Vendruscolo, Michele Sormanni, Pietro Fragment-based computational design of antibodies targeting structured epitopes |
title | Fragment-based computational design of antibodies targeting structured epitopes |
title_full | Fragment-based computational design of antibodies targeting structured epitopes |
title_fullStr | Fragment-based computational design of antibodies targeting structured epitopes |
title_full_unstemmed | Fragment-based computational design of antibodies targeting structured epitopes |
title_short | Fragment-based computational design of antibodies targeting structured epitopes |
title_sort | fragment-based computational design of antibodies targeting structured epitopes |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651861/ https://www.ncbi.nlm.nih.gov/pubmed/36367941 http://dx.doi.org/10.1126/sciadv.abp9540 |
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