Hallucinating protein assemblies
Open accessPreprint — published as 10.1126/science.add1964 · Paper page
Abstract
Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 Å), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summary: Deep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
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Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
- Authors
- Wicky, B. I. M., Milles, L. F., Courbet, A., Ragotte, R. J., Dauparas, J., Kinfu, E., Tipps, S., Kibler, R. D., Baek, M., DiMaio, F., Li, X., Carter, L., Kang, A., Nguyen, H., Bera, A. K., & Baker, D.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, D.
- ORCID
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2022-06-09
- Volume
- 378
- Issue
- 6615
- Pages
- 56-61
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC9724707
- OpenAlex
- W4296031103
- Cited by
- 240
- References
- 1
- Citations by year
- 2026: 46 · 2025: 72 · 2024: 62 · 2023: 52 · 2022: 8
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Science Foundation (P30 GM124165, P30 CA015704, CA015704, DE-AC02–06CH11357, P41-GM103311, 1629214)
- Howard Hughes Medical Institute
- U.S. Department of Energy (DE‐ AC02‐06CH11357, Contract No. DE-AC02-06CH11357., AC02‐06CH11357, P30 GM124165, 06CH11357, No. DE-AC02-06CH11357, DE-AC02-)
- Alfred P. Sloan Foundation (g-2021-16899)
- University of Washington (DE-AC02-06CH11357, P30 CA015704)
- Open Philanthropy Project
- National Institutes of Health (S10 OD032290, DE-AC02–06CH11357, GM124165, CA015704)
- Office of Science (Contract No. DE-AC02-06CH11357, AC02‐06CH11357, DE-AC02, No. DE-AC02-06CH11357., 06CH11357, DE-AC02-06CH11357)
- University of California, San Francisco (NIH P41-GM103311, P41-GM103311)
- National Cancer Institute (P30CA015704)
- National Institute of General Medical Sciences (GM103311, P41GM103311, P41GM103533, DE-AC02-06CH11357, NIH P41-GM103311, 103533, GM124165, P30GM124165)
- Division of Chemistry (1629214, CHE 1629214)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, No. DE-AC02-06CH11357, 06CH11357, Contract No. DE-AC02-06CH11357., P30 GM124165)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure design, homo-oligomers, nanorobots, biomaterials
- MeSH terms
- Deep Learning, Biocompatible Materials, Models, Molecular, Protein Conformation, Protein Engineering, Cryoelectron Microscopy, Protein Subunits
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