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De novo design of modular peptide-binding proteins by superhelical matching

Author

Listed:
  • Kejia Wu

    (University of Washington
    University of Washington
    University of Washington)

  • Hua Bai

    (University of Washington
    University of Washington
    University of Washington)

  • Ya-Ting Chang

    (New York University School of Medicine)

  • Rachel Redler

    (New York University School of Medicine)

  • Kerrie E. McNally

    (MRC Laboratory of Molecular Biology)

  • William Sheffler

    (University of Washington
    University of Washington)

  • T. J. Brunette

    (University of Washington
    University of Washington)

  • Derrick R. Hicks

    (University of Washington
    University of Washington)

  • Tomos E. Morgan

    (MRC Laboratory of Molecular Biology)

  • Tim J. Stevens

    (MRC Laboratory of Molecular Biology)

  • Adam Broerman

    (University of Washington
    University of Washington
    University of Washington)

  • Inna Goreshnik

    (University of Washington
    University of Washington)

  • Michelle DeWitt

    (University of Washington
    University of Washington)

  • Cameron M. Chow

    (University of Washington
    University of Washington)

  • Yihang Shen

    (Carnegie Mellon University)

  • Lance Stewart

    (University of Washington
    University of Washington)

  • Emmanuel Derivery

    (MRC Laboratory of Molecular Biology)

  • Daniel Adriano Silva

    (University of Washington
    University of Washington
    The Hong Kong University of Science and Technology
    Monod Bio)

  • Gira Bhabha

    (New York University School of Medicine)

  • Damian C. Ekiert

    (New York University School of Medicine
    New York University School of Medicine)

  • David Baker

    (University of Washington
    University of Washington
    University of Washington)

Abstract

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1–3. Here, inspired by natural and re-engineered protein–peptide systems4–11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

Suggested Citation

  • Kejia Wu & Hua Bai & Ya-Ting Chang & Rachel Redler & Kerrie E. McNally & William Sheffler & T. J. Brunette & Derrick R. Hicks & Tomos E. Morgan & Tim J. Stevens & Adam Broerman & Inna Goreshnik & Mich, 2023. "De novo design of modular peptide-binding proteins by superhelical matching," Nature, Nature, vol. 616(7957), pages 581-589, April.
  • Handle: RePEc:nat:nature:v:616:y:2023:i:7957:d:10.1038_s41586-023-05909-9
    DOI: 10.1038/s41586-023-05909-9
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    Cited by:

    1. Marcus N. Leiwe & Satoshi Fujimoto & Toshikazu Baba & Daichi Moriyasu & Biswanath Saha & Richi Sakaguchi & Shigenori Inagaki & Takeshi Imai, 2024. "Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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