Fragment-based computational design of antibodies targeting structured epitopes
Open accessAbstract
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.
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A strategy of grafting paratope fragments based on epitope similarity has been used to obtain nanomolar binders without parallel screening.
“However, recently, a strategy of grafting paratope fragments based on epitope similarity has been used to obtain nanomolar binders without parallel screening [94].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [94]
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- Authors
- Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P.
- Affiliations
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
- University of Cambridge (GB)
- University of Milan (IT)
- Department of Bioscience, Università degli Studi di Milano, Milano 20133, Italy.
- Stanford University (US)
- Corresponding author
- Vendruscolo, Michele
- Sormanni, Pietro
- ORCID
- Venue
- Science Advances
- Published
- 2022-11-11
- Volume
- 8
- Issue
- 45
- Pages
- eabp9540-eabp9540
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC9651861
- OpenAlex
- W4308915416
- Cited by
- 55
- References
- 50
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 10 · 2023: 16 · 2022: 4 · 2021: 1
- FWCI
- 6.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Novo Nordisk
- Novo Nordisk Fonden (NNF18OC0033950)
- National Institute of General Medical Sciences (R01GM056433)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- de novo antibody design, fragment-based design, nanobodies, spike protein receptor-binding domain, antibody affinity, epitope targeting
- MeSH terms
- COVID-19, SARS-CoV-2, Antibodies, Monoclonal, Antibody Affinity, Epitopes, Antigens, Humans, Models, Molecular, Spike Glycoprotein, Coronavirus
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