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Efficient evolution of human antibodies from general protein language models and sequence information alone

View ORCID ProfileBrian L. Hie, Duo Xu, Varun R. Shanker, Theodora U.J. Bruun, Payton A. Weidenbacher, View ORCID ProfileShaogeng Tang, View ORCID ProfilePeter S. Kim
doi: https://doi.org/10.1101/2022.04.10.487811
Brian L. Hie
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
2Stanford ChEM-H, Stanford, CA 94305, USA
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  • For correspondence: brianhie@stanford.edu kimpeter@stanford.edu
Duo Xu
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
2Stanford ChEM-H, Stanford, CA 94305, USA
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Varun R. Shanker
3Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford CA 94305, USA
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Theodora U.J. Bruun
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
2Stanford ChEM-H, Stanford, CA 94305, USA
3Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford CA 94305, USA
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Payton A. Weidenbacher
2Stanford ChEM-H, Stanford, CA 94305, USA
4Department of Chemistry, Stanford University, Stanford, CA 94305, USA
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Shaogeng Tang
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
2Stanford ChEM-H, Stanford, CA 94305, USA
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Peter S. Kim
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
2Stanford ChEM-H, Stanford, CA 94305, USA
5Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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  • For correspondence: brianhie@stanford.edu kimpeter@stanford.edu
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Abstract

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could accelerate artificial evolution. Here, we report that deep learning algorithms known as protein language models can evolve human antibodies with high efficiency, despite providing the models with no information about the target antigen, binding specificity, or protein structure, and also requiring no additional task-specific finetuning or supervision. We performed language-model-guided affinity maturation of seven diverse antibodies, screening 20 or fewer variants of each antibody across only two rounds of evolution. Our evolutionary campaigns improved the binding affinities of four clinically relevant antibodies up to 7-fold and three unmatured antibodies up to 160-fold across diverse viral antigens, with many designs also demonstrating improved thermostability and viral neutralization activity. Notably, our algorithm requires only a single wildtype sequence and computes recommended amino acid changes in less than a second. Moreover, the same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, indicating that these results generalize to many natural settings. Contrary to prevailing notions of evolution as difficult and resource-intensive, our results suggest that when constrained to a narrow manifold of evolutionary plausibility, evolution can become much easier, which we refer to as the “efficient manifold hypothesis.”

Competing Interest Statement

B.L.H and P.S.K. are named as inventors on a provisional patent application applied for by Stanford University and Chan Zuckerberg Biohub related to this study. B.L.H. performs research for Meta Platforms, Inc.

Footnotes

  • https://github.com/brianhie/efficient-evolution

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 11, 2022.
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Efficient evolution of human antibodies from general protein language models and sequence information alone
Brian L. Hie, Duo Xu, Varun R. Shanker, Theodora U.J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Peter S. Kim
bioRxiv 2022.04.10.487811; doi: https://doi.org/10.1101/2022.04.10.487811
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Efficient evolution of human antibodies from general protein language models and sequence information alone
Brian L. Hie, Duo Xu, Varun R. Shanker, Theodora U.J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Peter S. Kim
bioRxiv 2022.04.10.487811; doi: https://doi.org/10.1101/2022.04.10.487811

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