Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Open accessPublished version of a preprint
Abstract
Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured. In a head-to-head comparison with a directed evolution approach, we show that the best scFv generated from our method represents a 28.7-fold improvement in binding over the best scFv from the directed evolution. Additionally, 99% of designed scFvs in our most successful library are improvements over the initial candidate scFv. By comparing a library’s predicted success to actual measurements, we demonstrate our method’s ability to explore tradeoffs between library success and diversity. Results of our work highlight the significant impact machine learning models can have on scFv development. We expect our method to be broadly applicable and provide value to other protein engineering tasks.
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Cites
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Flagged first, then by citations
- Authors
- Li, L., Gupta, E., Spaeth, J., Shing, L., Jaimes, R., Engelhart, E., Lopez, R., Caceres, R. S., Bepler, T., & Walsh, M. E.
- Affiliations
- MIT Lincoln Laboratory (US)
- New York Structural Biology Center (US)
- Johns Hopkins University (US)
- Corresponding author
- Li, Lin
- Venue
- Nature Communications
- Published
- 2023-06-12
- Volume
- 14
- Issue
- 1
- Pages
- 3454-3454
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10258481
- OpenAlex
- W4380321714
- Cited by
- 96
- References
- 11
- Citations by year
- 2026: 20 · 2025: 37 · 2024: 30 · 2023: 9
- FWCI
- 15.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- antibody design, scFv, scFv library, Bayesian optimization, language models, library diversity, binding affinity, directed evolution, protein engineering, therapeutic antibodies, machine learning optimization
- MeSH terms
- Machine Learning, Bayes Theorem, Language, Gene Library, Single-Chain Antibodies
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