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Perfecting antibodies with language models

October 17, 2023
in Science
Perfecting antibodies with language models
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A general-purpose protein language model rapidly improves antibody properties.

Designing a therapeutic antibody is a complex puzzle. Each piece — from how well the antibody neutralizes the target to its specificity and stability — must fit together perfectly to create an equilibrated, functional medicine. Balancing these properties requires meticulous fine-tuning that often takes years of hard work. Writing in Nature Biotechnology, Hie et al.1 present a computational strategy to make this optimization process faster and cheaper. Their approach, demonstrated experimentally on seven clinical antibodies, exploits a general-purpose protein language model to propose beneficial mutations. As their data show, it holds enormous potential both for antibody therapeutics and for protein engineering more broadly.

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Fig. 1: Directed evolution with language models.

References

Hie, B. L. et al. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01763-2 (2023).

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Rives, A. et al. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

Meier, J. et al. Adv. Neural Inf. Process. Syst. 34, 29287–29303 (2021).

Olsen, T. H. et al. Bioinform. Adv. 2, vbac046 (2022).

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Fowler, D. M. et al. Nat. Methods 11, 801–807 (2014).

Outeiral, C. & Deane, C. M. Preprint at bioRxiv https://doi.org/10.1101/2022.12.15.519894 (2022).

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Author information

Authors and Affiliations

Department of Statistics, University of Oxford, Oxford, UK

Carlos Outeiral & Charlotte M. Deane

Corresponding author

Correspondence to
Charlotte M. Deane.

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Cite this article

Outeiral, C., Deane, C.M. Perfecting antibodies with language models.
Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01991-6

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Published: 16 October 2023

DOI: https://doi.org/10.1038/s41587-023-01991-6

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