The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Open accessAbstract
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 3 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 10 4 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 10 8 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 10 6 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.
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“Mason et al. [65] presented a method to train a convolutional neural network on the results of a directed evolution experiment to generate improved libraries.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [65]
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Metadata
- Authors
- Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., Correia, B. E., & Reddy, S. T.
- Affiliations
- ETH Zurich (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Reddy, Sai T.
- ORCID
- Venue
- Nature Biomedical Engineering
- Published
- 2021-04-15
- Volume
- 5
- Issue
- 6
- Pages
- 600-612
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W3156522942
- Cited by
- 308
- References
- 59
- Citations by year
- 2026: 55 · 2025: 66 · 2024: 62 · 2023: 60 · 2022: 48 · 2021: 17
- FWCI
- 24.67
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- NCCR Molecular Systems Engineering
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- therapeutic antibodies, antibody engineering, trastuzumab, HER2, deep learning, antibody optimization
- MeSH terms
- Trastuzumab, Deep Learning, Amino Acid Sequence, Animals, Antibody Affinity, Antibody Specificity, Antigens, Humans, Hybridomas, Protein Binding, Protein Engineering, Mutagenesis, Site-Directed, Erb-b2 Receptor Tyrosine Kinases, Sequence Analysis, Protein, Recombinational DNA Repair, CRISPR-Cas Systems
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