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:
- 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.
- 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.
- 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.
- 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.
- 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.
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