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Computational maturation of a single-domain antibody against Aβ42 aggregation

The expansion of structural databases and the increase in computing power are enabling approaches for antibody discovery based on computational design. It has already been shown that it is possible to use this approach to generate antibodies for specific epitopes on challenging targets. Here we desc...

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Autores principales: Lin, Jiacheng, Figazzolo, Chiara, Metrick, Michael A., Sormanni, Pietro, Vendruscolo, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901120/
https://www.ncbi.nlm.nih.gov/pubmed/35475123
http://dx.doi.org/10.1039/d1sc03898b
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author Lin, Jiacheng
Figazzolo, Chiara
Metrick, Michael A.
Sormanni, Pietro
Vendruscolo, Michele
author_facet Lin, Jiacheng
Figazzolo, Chiara
Metrick, Michael A.
Sormanni, Pietro
Vendruscolo, Michele
author_sort Lin, Jiacheng
collection PubMed
description The expansion of structural databases and the increase in computing power are enabling approaches for antibody discovery based on computational design. It has already been shown that it is possible to use this approach to generate antibodies for specific epitopes on challenging targets. Here we describe an application of this procedure for antibody maturation through the computational design of mutational variants of increased potency. We illustrate this procedure in the case of a single-domain antibody targeting an epitope in the N-terminal region of Aβ42, a peptide whose aggregation is closely associated with Alzheimer's disease. We show that this approach enables the generation of an antibody variant with over 200-fold increased potency against the primary nucleation process in Aβ42 aggregation. Our results thus demonstrate that potentiated antibody variants can be obtained by computational maturation.
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spelling pubmed-89011202022-04-25 Computational maturation of a single-domain antibody against Aβ42 aggregation Lin, Jiacheng Figazzolo, Chiara Metrick, Michael A. Sormanni, Pietro Vendruscolo, Michele Chem Sci Chemistry The expansion of structural databases and the increase in computing power are enabling approaches for antibody discovery based on computational design. It has already been shown that it is possible to use this approach to generate antibodies for specific epitopes on challenging targets. Here we describe an application of this procedure for antibody maturation through the computational design of mutational variants of increased potency. We illustrate this procedure in the case of a single-domain antibody targeting an epitope in the N-terminal region of Aβ42, a peptide whose aggregation is closely associated with Alzheimer's disease. We show that this approach enables the generation of an antibody variant with over 200-fold increased potency against the primary nucleation process in Aβ42 aggregation. Our results thus demonstrate that potentiated antibody variants can be obtained by computational maturation. The Royal Society of Chemistry 2021-10-07 /pmc/articles/PMC8901120/ /pubmed/35475123 http://dx.doi.org/10.1039/d1sc03898b Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Lin, Jiacheng
Figazzolo, Chiara
Metrick, Michael A.
Sormanni, Pietro
Vendruscolo, Michele
Computational maturation of a single-domain antibody against Aβ42 aggregation
title Computational maturation of a single-domain antibody against Aβ42 aggregation
title_full Computational maturation of a single-domain antibody against Aβ42 aggregation
title_fullStr Computational maturation of a single-domain antibody against Aβ42 aggregation
title_full_unstemmed Computational maturation of a single-domain antibody against Aβ42 aggregation
title_short Computational maturation of a single-domain antibody against Aβ42 aggregation
title_sort computational maturation of a single-domain antibody against aβ42 aggregation
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8901120/
https://www.ncbi.nlm.nih.gov/pubmed/35475123
http://dx.doi.org/10.1039/d1sc03898b
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