Rapid and automated design of two-component protein nanomaterials using ProteinMPNN
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Abstract
The design of protein–protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein–protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning–based methods to unlock the widespread application of designed protein–protein interfaces and self-assembling protein nanomaterials in biotechnology.
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- Authors
- de Haas, R. J., Brunette, N., Goodson, A., Dauparas, J., Yi, S. Y., Yang, E. C., Dowling, Q., Nguyen, H., Kang, A., Bera, A. K., Sankaran, B., de Vries, R., Baker, D., & King, N. P.
- Affiliations
- Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen 6078 WE, The Netherlands
- University of Washington (US)
- Lawrence Berkeley National Laboratory (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- King, Neil P.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2024-03-19
- Volume
- 121
- Issue
- 13
- Pages
- e2314646121-e2314646121
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC10990136
- OpenAlex
- W4392946437
- Cited by
- 42
- References
- 14
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 11
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by-nc-nd)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- PubMed Central (submittedVersion)
- Funders
- Bill and Melinda Gates Foundation (INV-010680)
- HHS | NIH | National Institute of Allergy and Infectious Diseases (U54AI170856, 1P01AI167966)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein sequence design, ProteinMPNN, deep learning, protein nanomaterials, self-assembly, in vitro assembly, crystal structure
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