Predicting antibody binders and generating synthetic antibodies using deep learning
Open accessAbstract
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning, a subfield of machine learning, is rapidly gaining prominence in the biomedical research. Recent advances in microfluidics technologies and next-generation sequencing have not only revolutionized therapeutic antibody discovery, but also contributed to a vast amount of antibody repertoire sequencing data, providing opportunities for deep learning-based applications. Previously, we used microfluidics, yeast display, and deep sequencing to generate a panel of binder and non-binder antibody sequences to the cancer immunotherapy targets PD-1 and CTLA-4. Here we encoded the antibody light and heavy chain complementarity-determining regions (CDR3s) into antibody images, then built and trained convolutional neural network models to classify binders and non-binders. To improve model interpretability, we performed in silico mutagenesis to identify CDR3 residues that were important for binder classification. We further built generative deep learning models using generative adversarial network models to produce synthetic antibodies against PD-1 and CTLA-4. Our models generated variable length CDR3 sequences that resemble real sequences. Overall, our study demonstrates that deep learning methods can be leveraged to mine and learn patterns in antibody sequences, offering insights into antibody engineering, optimization, and discovery.
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A number of other related approaches use ML models to learn sequence models from directed evolution experiments.
“A number of other related approaches that use ML models to learn sequence models from directed evolution experiments have been developed [66][67][68].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [66]
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- Authors
- Lim, Y. W., Adler, A. S., & Johnson, D. S.
- Affiliations
- GigaGen (United States) (US)
- Corresponding author
- Johnson, David S.
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2022-04-28
- Volume
- 14
- Issue
- 1
- Pages
- 2069075-2069075
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC9067455
- OpenAlex
- W4225008297
- Cited by
- 49
- References
- 42
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 13 · 2023: 12 · 2022: 3
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- mAbs (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- funding
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- CDR3 sequences, convolutional neural networks, generative adversarial networks, PD-1, CTLA-4, cancer immunotherapy targets, antibody engineering, in silico mutagenesis, yeast surface display
- MeSH terms
- Deep Learning, Antibodies, Complementarity Determining Regions, CTLA-4 Antigen, Programmed Cell Death 1 Receptor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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