Computational design of epitope-specific functional antibodies
Open accessAbstract
The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts. We use this approach to design a functional antibody to counter the proinflammatory effect of the cytokine interleukin-17A (IL-17A). X-ray crystallography confirms that the designed antibody binds the targeted epitope and the interaction is mediated by the designed contacts. Cell-based assays confirm that the antibody is functional. Importantly, this approach does not rely on a high-quality 3D model of the designed complex or even a solved structure of the target. As demonstrated here, this approach can be used to design biologically active antibodies, removing some of the main hurdles in antibody design and in drug discovery.
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Nimrod et al. proposed a strategy of re-epitoping using similarity to epitopes of known antibody-antigen complexes for antibody design.
“Nimrod et al. [92] proposed a strategy of re-epitoping [i.e., using similarity to epitopes of known antibodyantigen complexes for their antibody design strategy.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [92]
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“A further approach to data mining existing structures to improve antibody affinity is ‘re-epitoping’, pioneered by Ofran and collaborators [184].”
BackgroundComputational and artificial intelligence-based methods for antibody development
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- Authors
- Nimrod, G., Fischman, S., Austin, M., Herman, A., Keyes, F., Leiderman, O., Hargreaves, D., Strajbl, M., Breed, J., Klompus, S., Minton, K., Spooner, J., Buchanan, A., Vaughan, T. J., & Ofran, Y.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Bar-Ilan University (IL)
- Corresponding author
- Ofran, Yanay
- ORCID
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 25
- Issue
- 8
- Pages
- 2121-2131.e5
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W2901312940
- Cited by
- 77
- References
- 51
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 9 · 2023: 8 · 2022: 18 · 2021: 11 · 2020: 7 · 2019: 7
- FWCI
- 2.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- GIF (1327)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- 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
- Keywords
- antibody design, epitope-specific antibodies, protein design, IL-17A, IL-17A inhibition, residue-residue contact prediction, machine learning, structural model, inflammatory cytokines, antibody-antigen binding
- MeSH terms
- Algorithms, Amino Acid Sequence, Antibodies, Antibody Specificity, Epitopes, Humans, Immunoglobulin Fab Fragments, Models, Molecular, Computational Biology
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