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Fragment-based computational design of antibodies targeting structured epitopes.

Published version
Peer-reviewed

Type

Article

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Authors

Aguilar Rangel, Mauricio  ORCID logo  https://orcid.org/0000-0002-4768-9031
Bedwell, Alice 
Costanzi, Elisa 
Taylor, Ross J 

Abstract

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.

Description

Keywords

Humans, Epitopes, Antibody Affinity, Antibodies, Monoclonal, Models, Molecular, SARS-CoV-2, COVID-19, Antigens

Journal Title

Sci Adv

Conference Name

Journal ISSN

2375-2548
2375-2548

Volume Title

8

Publisher

American Association for the Advancement of Science (AAAS)