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De novo design of protein structure and function with RFdiffusion

Author

Listed:
  • Joseph L. Watson

    (University of Washington
    University of Washington)

  • David Juergens

    (University of Washington
    University of Washington
    University of Washington)

  • Nathaniel R. Bennett

    (University of Washington
    University of Washington
    University of Washington)

  • Brian L. Trippe

    (University of Washington
    Department of Statistics
    Columbia University)

  • Jason Yim

    (University of Washington
    Massachusetts Institute of Technology)

  • Helen E. Eisenach

    (University of Washington
    University of Washington)

  • Woody Ahern

    (University of Washington
    University of Washington
    University of Washington)

  • Andrew J. Borst

    (University of Washington
    University of Washington)

  • Robert J. Ragotte

    (University of Washington
    University of Washington)

  • Lukas F. Milles

    (University of Washington
    University of Washington)

  • Basile I. M. Wicky

    (University of Washington
    University of Washington)

  • Nikita Hanikel

    (University of Washington
    University of Washington)

  • Samuel J. Pellock

    (University of Washington
    University of Washington)

  • Alexis Courbet

    (University of Washington
    University of Washington
    École Normale Supérieure rue d’Ulm)

  • William Sheffler

    (University of Washington
    University of Washington)

  • Jue Wang

    (University of Washington
    University of Washington)

  • Preetham Venkatesh

    (University of Washington
    University of Washington
    University of Washington)

  • Isaac Sappington

    (University of Washington
    University of Washington
    University of Washington)

  • Susana Vázquez Torres

    (University of Washington
    University of Washington
    University of Washington)

  • Anna Lauko

    (University of Washington
    University of Washington
    University of Washington)

  • Valentin Bortoli

    (École Normale Supérieure rue d’Ulm)

  • Emile Mathieu

    (University of Cambridge)

  • Sergey Ovchinnikov

    (Harvard University
    Harvard University)

  • Regina Barzilay

    (Massachusetts Institute of Technology)

  • Tommi S. Jaakkola

    (Massachusetts Institute of Technology)

  • Frank DiMaio

    (University of Washington
    University of Washington)

  • Minkyung Baek

    (Seoul National University)

  • David Baker

    (University of Washington
    University of Washington
    University of Washington)

Abstract

There has been considerable recent progress in designing new proteins using deep-learning methods1–9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Suggested Citation

  • Joseph L. Watson & David Juergens & Nathaniel R. Bennett & Brian L. Trippe & Jason Yim & Helen E. Eisenach & Woody Ahern & Andrew J. Borst & Robert J. Ragotte & Lukas F. Milles & Basile I. M. Wicky & , 2023. "De novo design of protein structure and function with RFdiffusion," Nature, Nature, vol. 620(7976), pages 1089-1100, August.
  • Handle: RePEc:nat:nature:v:620:y:2023:i:7976:d:10.1038_s41586-023-06415-8
    DOI: 10.1038/s41586-023-06415-8
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    Citations

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    Cited by:

    1. Wei Lu & Jixian Zhang & Weifeng Huang & Ziqiao Zhang & Xiangyu Jia & Zhenyu Wang & Leilei Shi & Chengtao Li & Peter G. Wolynes & Shuangjia Zheng, 2024. "DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Patrick Bryant & Atharva Kelkar & Andrea Guljas & Cecilia Clementi & Frank Noé, 2024. "Structure prediction of protein-ligand complexes from sequence information with Umol," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Shunshi Kohyama & Béla P. Frohn & Leon Babl & Petra Schwille, 2024. "Machine learning-aided design and screening of an emergent protein function in synthetic cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Simon d’Oelsnitz & Daniel J. Diaz & Wantae Kim & Daniel J. Acosta & Tyler L. Dangerfield & Mason W. Schechter & Matthew B. Minus & James R. Howard & Hannah Do & James M. Loy & Hal S. Alper & Y. Jessie, 2024. "Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Xiaorui Wang & Xiaodan Yin & Dejun Jiang & Huifeng Zhao & Zhenxing Wu & Odin Zhang & Jike Wang & Yuquan Li & Yafeng Deng & Huanxiang Liu & Pei Luo & Yuqiang Han & Tingjun Hou & Xiaojun Yao & Chang-Yu , 2024. "Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    6. Xin Zhang & Zakir Ibrahim & Muhammad Bilawal Khaskheli & Hamad Raza & Fanrui Zhou & Imran Haider Shamsi, 2024. "Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications," Sustainability, MDPI, vol. 16(17), pages 1-26, September.
    7. 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.
    8. Enrico Orsi & Lennart Schada von Borzyskowski & Stephan Noack & Pablo I. Nikel & Steffen N. Lindner, 2024. "Automated in vivo enzyme engineering accelerates biocatalyst optimization," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Aika Iwama & Ryoji Kise & Hiroaki Akasaka & Fumiya K. Sano & Hidetaka S. Oshima & Asuka Inoue & Wataru Shihoya & Osamu Nureki, 2024. "Structure and dynamics of the pyroglutamylated RF-amide peptide QRFP receptor GPR103," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    10. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Ying Huang & Chenyang Xue & Ruiqian Bu & Cang Wu & Jiachen Li & Jinqiu Zhang & Jinyu Chen & Zhaoying Shi & Yonglong Chen & Yong Wang & Zhongmin Liu, 2024. "Inhibition and transport mechanisms of the ABC transporter hMRP5," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    12. Chase R. Freschlin & Sarah A. Fahlberg & Pete Heinzelman & Philip A. Romero, 2024. "Neural network extrapolation to distant regions of the protein fitness landscape," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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