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De novo design of protein interactions with learned surface fingerprints

Author

Listed:
  • Pablo Gainza

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics
    Monte Rosa Therapeutics)

  • Sarah Wehrle

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Alexandra Hall-Beauvais

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Anthony Marchand

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Andreas Scheck

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Zander Harteveld

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Stephen Buckley

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Dongchun Ni

    (École Polytechnique Fédérale de Lausanne
    University of Lausanne)

  • Shuguang Tan

    (Chinese Academy of Sciences)

  • Freyr Sverrisson

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Casper Goverde

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Priscilla Turelli

    (École Polytechnique Fédérale de Lausanne)

  • Charlène Raclot

    (École Polytechnique Fédérale de Lausanne)

  • Alexandra Teslenko

    (École Polytechnique Fédérale de Lausanne)

  • Martin Pacesa

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Stéphane Rosset

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Sandrine Georgeon

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Jane Marsden

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

  • Aaron Petruzzella

    (École Polytechnique Fédérale de Lausanne)

  • Kefang Liu

    (Chinese Academy of Sciences)

  • Zepeng Xu

    (Chinese Academy of Sciences)

  • Yan Chai

    (Chinese Academy of Sciences)

  • Pu Han

    (Chinese Academy of Sciences)

  • George F. Gao

    (Chinese Academy of Sciences)

  • Elisa Oricchio

    (École Polytechnique Fédérale de Lausanne)

  • Beat Fierz

    (École Polytechnique Fédérale de Lausanne)

  • Didier Trono

    (École Polytechnique Fédérale de Lausanne)

  • Henning Stahlberg

    (École Polytechnique Fédérale de Lausanne
    University of Lausanne)

  • Michael Bronstein

    (University of Oxford)

  • Bruno E. Correia

    (École Polytechnique Fédérale de Lausanne
    Swiss Institute of Bioinformatics)

Abstract

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2–9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Suggested Citation

  • Pablo Gainza & Sarah Wehrle & Alexandra Hall-Beauvais & Anthony Marchand & Andreas Scheck & Zander Harteveld & Stephen Buckley & Dongchun Ni & Shuguang Tan & Freyr Sverrisson & Casper Goverde & Prisci, 2023. "De novo design of protein interactions with learned surface fingerprints," Nature, Nature, vol. 617(7959), pages 176-184, May.
  • Handle: RePEc:nat:nature:v:617:y:2023:i:7959:d:10.1038_s41586-023-05993-x
    DOI: 10.1038/s41586-023-05993-x
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    Cited by:

    1. Wonho Zhung & Hyeongwoo Kim & Woo Youn Kim, 2024. "3D molecular generative framework for interaction-guided drug design," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Lucien F. Krapp & Fernando A. Meireles & Luciano A. Abriata & Jean Devillard & Sarah Vacle & Maria J. Marcaida & Matteo Dal Peraro, 2024. "Context-aware geometric deep learning for protein sequence design," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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