The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Efficient evolution of human antibodies from general protein language models
Open accessAbstract
Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses. The same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity, suggesting that these results generalize to many settings.
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Cites
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“Because we used general language models 19,20 , trained on non-redundant sequence datasets that are meant to represent variation across all natural proteins 23 , these models can only learn more general evolutionary rules than could a model trained specifically on antibody sequences 24–27 or a…”
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
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Cited by
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Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., et al. (2024) · Science · doi:10.1126/science.ado9336 · Cited by 580
Self-citationProGen2: Exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.002 · Cited by 480
Bilingual language model for protein sequence and structure
Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae150 · Cited by 311
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178
Self-citationThe Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies
Swanson, K., Wu, W., Bulaong, N. L., Pak, J. E., & Zou, J. (2025) · Nature · doi:10.1038/s41586-025-09442-9 · Cited by 169
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Metadata
- Authors
- Hie, B. L., Shanker, V. R., Xu, D., Bruun, T. U. J., Weidenbacher, P. A., Tang, S., Wu, W., Pak, J. E., & Kim, P. S.
- Venue
- Nature Biotechnology
- Published
- 2023-04-24
- Volume
- 42
- Issue
- 2
- Pages
- 275-283
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W4366823175
- Cited by
- 449
- References
- 33
- FWCI
- 65.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
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