Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Open accessPreprint — published as 10.1038/s41586-023-06415-8 · Paper page
Abstract
There has been considerable recent progress in designing new proteins using deep learning methods 1–9 . 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 models 10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely 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 (RF diffusion ), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RF diffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.
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Self-citation
- Authors
- Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., Lauko, A., De Bortoli, V., Mathieu, E., Barzilay, R., Jaakkola, T. S., DiMaio, F., Baek, M., & Baker, D.
- Affiliations
- University of Washington (US)
- Centre National de la Recherche Scientifique (FR)
- Massachusetts Institute of Technology (US)
- Seoul National University (KR)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- ORCID
- Watson, Joseph L.
- Juergens, David
- Bennett, Nathaniel R.
- Trippe, Brian L.
- Yim, Jason
- Eisenach, Helen E.
- Ahern, Woody
- Borst, Andrew J.
- Ragotte, Robert J.
- Milles, Lukas F.
- Wicky, Basile I. M.
- Hanikel, Nikita
- Pellock, Samuel J.
- Courbet, Alexis
- Sheffler, William
- Wang, Jue
- Venkatesh, Preetham
- Sappington, Isaac
- Torres, Susana Vázquez
- Lauko, Anna
- De Bortoli, Valentin
- Barzilay, Regina
- Jaakkola, Tommi S.
- DiMaio, Frank
- Baek, Minkyung
- Baker, David
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2022-12-10
- Volume
- 620
- Issue
- 7976
- Pages
- 1089-1100
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC10468394
- OpenAlex
- W4383957026
- Cited by
- 2,369
- References
- 57
- Citations by year
- 2026: 943 · 2025: 795 · 2024: 473 · 2023: 153 · 2022: 4
- FWCI
- 210.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- National Science Foundation (HDTRA1-19-1-0003, 1918839)
- Howard Hughes Medical Institute
- U.S. Department of Energy (DESC0018940)
- Bill and Melinda Gates Foundation (INV-010680)
- Washington Research Foundation
- Amgen
- Sanofi
- Washington State University
- Open Philanthropy Project
- Defense Advanced Research Projects Agency
- Defense Threat Reduction Agency (HDTRA1-19-1-0003, HDTRA1)
- Office of Science (DE-SC0018940 MOD03, DESC0018940)
- Engineering and Physical Sciences Research Council (EP/T005386/1)
- National Institute on Aging (U19AG065156)
- National Institute of General Medical Sciences (T32GM007250, T32GM008268)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein design, de novo protein design, diffusion models, RoseTTAFold, protein binder design, metal-binding proteins, protein backbone, influenza haemagglutinin, therapeutic proteins
- MeSH terms
- Deep Learning, Protein Binding, Proteins, Hemagglutinin Glycoproteins, Influenza Virus, Catalytic Domain, Cryoelectron Microscopy
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