Computational design of closely related proteins that adopt two well-defined but structurally divergent folds
Open accessAbstract
The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320–327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol. 13, 1280–1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149–21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations—one short (∼66 Å height) and the other long (∼100 Å height)—reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.
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There are examples of highly similar protein sequences having radically different structures and functions and of sequences that can form multiple folds.
“Although it is generally believed that proteins with similar sequences form similar structures, there are also interesting examples of highly similar protein sequences having radically different structures and functions 60,61 and of sequences that can form multiple folds 62.”
From Learning the protein language: Evolution, structure, and function (2021) · cites this paper as [61]
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The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., & Baker, D. (2016) · Nature · doi:10.1038/nature19946 · Cited by 1,478 · see citing works
Self-citationA De Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch
Lizatović, R., Aurelius, O., Stenström, O., Drakenberg, T., Akke, M., Logan, D. T., & André, I. (2016) · Structure · doi:10.1016/j.str.2016.03.027 · Cited by 48 · see citing works
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Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483 · see citing works
Deep learning techniques have significantly impacted protein structure prediction and protein design
Pearce, R., & Zhang, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.007 · Cited by 127 · see citing works
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141 · see citing works
Self-citationDesign and discovery of metamorphic proteins
Dishman, A. F., & Volkman, B. F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102380 · Cited by 36 · see citing works
Notari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4 · see citing works
- Authors
- Wei, K. Y., Moschidi, D., Bick, M. J., Nerli, S., McShan, A. C., Carter, L. P., Huang, P.-S., Fletcher, D. A., Sgourakis, N. G., Boyken, S. E., & Baker, D.
- Affiliations
- University of Washington (US)
- University of California, Santa Cruz (US)
- Stanford University (US)
- Lawrence Berkeley National Laboratory (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Boyken, Scott E.
- Baker, David
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-03-18
- Volume
- 117
- Issue
- 13
- Pages
- 7208-7215
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7132107
- OpenAlex
- W3012394013
- Cited by
- 57
- References
- 2
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 4 · 2022: 10 · 2021: 23 · 2020: 4
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- HHS | National Institutes of Health (S10OD018455)
- Chan Zuckerberg (Biohub)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
- Keywords
- de novo protein design, structural plasticity, conformational switching, protein folding, viral fusion protein, protein conformational change
- MeSH terms
- Amino Acid Sequence, Amino Acids, Molecular Conformation, Protein Conformation, Proteins, Protein Engineering, Protein Folding, Computational Biology
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