Bond-centric modular design of protein assemblies
Open accessPublished version of a preprint
Abstract
Directional interactions that generate regular coordination geometries are a powerful means of guiding molecular and colloidal self-assembly, but implementing such high-level interactions with proteins remains challenging due to their complex shapes and intricate interface properties. Here we describe a modular approach to protein nanomaterial design inspired by the rich chemical diversity that can be generated from the small number of atomic valencies. We design protein building blocks using deep learning-based generative tools, incorporating regular coordination geometries and tailorable bonding interactions that enable the assembly of diverse closed and open architectures guided by simple geometric principles. Experimental characterization confirms the successful formation of more than 20 multicomponent polyhedral protein cages, two-dimensional arrays and three-dimensional protein lattices, with a high (10%–50%) success rate and electron microscopy data closely matching the corresponding design models. Due to modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable networks for designer nanomaterials.
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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
Protein complex prediction with AlphaFold-Multimer
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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., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369 · see citing works
Self-citationDe novo design of protein structure and function with RFdiffusion
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., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Self-citationRobust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107 · see citing works
Self-citationRobust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,070 · see citing works
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Illuminating protein space with a programmable generative model
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Flagged first, then by citations
- Authors
- Wang, S., Favor, A., Kibler, R. D., Lubner, J. M., Borst, A. J., Coudray, N., Redler, R. L., Chiang, H. T., Sheffler, W., Hsia, Y., Bethel, N. P., Li, Z., Ekiert, D. C., Bhabha, G., Pozzo, L. D., & Baker, D.
- Affiliations
- University of Washington (US)
- Johns Hopkins University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Wang, Shunzhi
- Baker, David
- ORCID
- Venue
- Nature Materials
- Published
- 2025-07-31
- Volume
- 24
- Issue
- 10
- Pages
- 1644-1652
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- PMC
- PMC12484077
- OpenAlex
- W4412780842
- Cited by
- 22
- References
- 8
- Citations by year
- 2026: 16 · 2025: 6
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Materials (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Burroughs Wellcome Fund (1199630)
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (R01AG063845)
- Washington Research Foundation
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM128777, U24 GM129539)
- U.S. Department of Energy (DE-SC0019288)
- U.S. Department of Health & Human Services | National Institutes of Health (U24 GM129539)
- Howard Hughes Medical Institute
- Topics
- Physical Sciences › Materials Science › Biomaterials › Supramolecular Self-Assembly in Materials
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein self-assembly, protein nanomaterials, modular protein design, coordination geometry, protein microarrays, directional interactions
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