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Cited by 308Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approachesComputational Drug Discovery Methods

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 access
Mason, Derek M., Friedensohn, Simon, Weber, Cédric R., Jordi, Christian, Wagner, Bastian, Meng, Simon M., Ehling, Roy A., Bonati, Lucia, Dahinden, Jan, Gainza, Pablo, Correia, Bruno E., Reddy, Sai T.·2021·Nature Biomedical Engineering
DOI 10.1038/s41551-021-00699-9
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Abstract

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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  1. Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. Preprint at https://arxiv.org/abs/1703.01365 (2017). · arXiv

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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
Publisher
Springer Science and Business Media LLC
Published
2021-04-15
Volume
5
Issue
6
Pages
600-612
ISSN
2157-846X (ISSN-L)
DOI
10.1038/s41551-021-00699-9
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
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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