CoChat
Sign Up
Cited by 17Protein Structure and DynamicsSupramolecular Self-Assembly in MaterialsBiochemical and Structural Characterization

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Computational design of bifaceted protein nanomaterials

Open access
Rankovic, Sanela, Carr, Kenneth D., Decarreau, Justin, Skotheim, Rebecca, Kibler, Ryan D., Ols, Sebastian, Lee, Sangmin, Chun, Jung-Ho, Tooley, Marti R., Dauparas, Justas, Eisenach, Helen E., Glögl, Matthias, Weidle, Connor, Borst, Andrew J., Baker, David, King, Neil P.·2025·Nature Materials
DOI 10.1038/s41563-025-02295-7PMC PMC12407187

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Advances in computational methods have led to considerable progress in the design of protein nanomaterials. However, nearly all nanoparticles designed so far exhibit strict point group symmetry, which limits structural diversity and precludes anisotropic functionalization. Here we describe a computational strategy for designing multicomponent bifaceted protein nanomaterials with two distinctly addressable sides. The method centres on docking pseudosymmetric hetero-oligomeric building blocks in architectures with dihedral symmetry and designing an asymmetric protein–protein interface between them. We obtain an initial 30-subunit assembly with pseudo- D 5 symmetry and generate variants in which we alter the size and morphology of the bifaceted nanoparticles by designing extensions to one of the subunits. Functionalization of the two nanoparticle faces with protein minibinders enables the specific colocalization of two populations of polystyrene microparticles coated with the target protein receptors. The ability to accurately design anisotropic protein nanoparticles could be broadly useful in applications requiring the colocalization of distinct target moieties.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41563-025-02295-7

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 65

  1. Goodsell, D. S. & Olson, A. J. Structural symmetry and protein function. Annu. Rev. Biophys. Biomol. Struct. 29, 105–153 (2000). · DOI · Paper page
  2. Alberts, B. The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92, 291–294 (1998). · DOI · Paper page
  3. Lai, Y.-T., King, N. P. & Yeates, T. O. Principles for designing ordered protein assemblies. Trends Cell Biol. 22, 653–661 (2012). · DOI
  4. Khmelinskaia, A., Wargacki, A. & King, N. P. Structure-based design of novel polyhedral protein nanomaterials. Curr. Opin. Microbiol. 61, 51–57 (2021). · DOI
  5. Ueda, G. et al. Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens. eLife 9, e57659 (2020). · DOI
  6. Brouwer, P. J. M. et al. Enhancing and shaping the immunogenicity of native-like HIV-1 envelope trimers with a two-component protein nanoparticle. Nat. Commun. 10, 4272 (2019). · DOI
  7. Bruun, T. U. J., Andersson, A.-M. C., Draper, S. J. & Howarth, M. Engineering a rugged nanoscaffold to enhance plug-and-display vaccination. ACS Nano 12, 8855–8866 (2018). · DOI
  8. Boyoglu-Barnum, S. et al. Quadrivalent influenza nanoparticle vaccines induce broad protection. Nature 592, 623–628 (2021). · DOI
  9. Cohen, A. A. et al. Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371, 735–741 (2021). · DOI
  10. Walls, A. C. et al. Elicitation of potent neutralizing antibody responses by designed protein nanoparticle vaccines for SARS-CoV-2. Cell 183, 1367–1382.e17 (2020). · DOI · Paper page
  11. Marcandalli, J. et al. Induction of potent neutralizing antibody responses by a designed protein nanoparticle vaccine for respiratory syncytial virus. Cell 176, 1420–1431.e17 (2019). · DOI
  12. Liu, Y., Huynh, D. T. & Yeates, T. O. A 3.8 Å resolution cryo-EM structure of a small protein bound to an imaging scaffold. Nat. Commun. 10, 1864 (2019). · DOI
  13. Liu, Y., Gonen, S., Gonen, T. & Yeates, T. O. Near-atomic cryo-EM imaging of a small protein displayed on a designed scaffolding system. Proc. Natl Acad. Sci. USA 115, 3362–3367 (2018). · DOI
  14. Castells-Graells, R. et al. Cryo-EM structure determination of small therapeutic protein targets at 3 Å-resolution using a rigid imaging scaffold. Proc. Natl Acad. Sci. USA 120, e2305494120 (2023). · DOI · Paper page
  15. McConnell, S. A. et al. Designed protein cages as scaffolds for building multienzyme materials. ACS Synth. Biol. 9, 381–391 (2020). · DOI
  16. Divine, R. et al. Designed proteins assemble antibodies into modular nanocages. Science 372, eabd9994 (2021). · DOI
  17. Lutz, I. D. et al. Top-down design of protein architectures with reinforcement learning. Science 380, 266–273 (2023). · DOI
  18. Mohan, K. et al. Topological control of cytokine receptor signaling induces differential effects in hematopoiesis. Science 364, eaav7532 (2019). · DOI
  19. King, N. P. et al. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336, 1171–1174 (2012). · DOI
  20. King, N. P. et al. Accurate design of co-assembling multi-component protein nanomaterials. Nature 510, 103–108 (2014). · DOI
  21. Hsia, Y. et al. Correction: corrigendum: design of a hyperstable 60-subunit protein icosahedron. Nature 540, 150 (2016). · DOI
  22. Bale, J. B. et al. Accurate design of megadalton-scale two-component icosahedral protein complexes. Science 353, 389–394 (2016). · DOI
  23. Wukovitz, S. W. & Yeates, T. O. Why protein crystals favour some space-groups over others. Nat. Struct. Biol. 2, 1062–1067 (1995). · DOI
  24. Kibler, R. D. et al. Design of pseudosymmetric protein hetero-oligomers. Nat. Commun. 15, 10684 (2024). · DOI
  25. Lee, S. et al. Four-component protein nanocages designed by programmed symmetry breaking. Nature https://doi.org/10.1038/s41586-024-07814-1 (2024). · DOI
  26. Dowling, Q. M. et al. Hierarchical design of pseudosymmetric protein nanocages. Nature https://doi.org/10.1038/s41586-024-08360-6 (2024). · DOI
  27. Zhang, X., Fu, Q., Duan, H., Song, J. & Yang, H. Janus nanoparticles: from fabrication to (bio)applications. ACS Nano 15, 6147–6191 (2021). · DOI
  28. Huehls, A. M., Coupet, T. A. & Sentman, C. L. Bispecific T-cell engagers for cancer immunotherapy. Immunol. Cell Biol. 93, 290–296 (2015). · DOI
  29. Li, X. et al. Preparation and application of Janus nanoparticles: recent development and prospects. Coord. Chem. Rev. 454, 214318 (2022). · DOI
  30. Zeng, Y. C. et al. Fine tuning of CpG spatial distribution with DNA origami for improved cancer vaccination. Nat. Nanotechnol. 19, 1055–1065 (2024). · DOI
  31. Jin, H., Cui, J. & Zhan, W. Enzymatic Janus liposome micromotors. Langmuir 39, 4198–4206 (2023). · DOI
  32. Lee, J., Sands, I., Zhang, W., Zhou, L. & Chen, Y. DNA-inspired nanomaterials for enhanced endosomal escape. Proc. Natl Acad. Sci. USA 118, e2104511118 (2021). · DOI
  33. Hsia, Y. et al. Design of a hyperstable 60-subunit protein icosahedron. Nature 535, 136–139 (2016). · DOI
  34. Wang, J. Y. J. et al. Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains. Proc. Natl Acad. Sci. USA 120, e2214556120 (2023). · DOI
  35. Lee, S. et al. Four-component protein nanocages designed by programmed symmetry breaking. Nature 638, 546–552 (2025). · DOI
  36. Grueninger, D. et al. Designed protein-protein association. Science 319, 206–209 (2008). · DOI
  37. Carr, K. D. et al. Protein identification using cryo-EM and artificial intelligence guides improved sample purification. J. Struct. Biol. X 11, 100120 (2025).
  38. Young, G. et al. Quantitative mass imaging of single biological macromolecules. Science 360, 423–427 (2018). · DOI
  39. Velas, L. et al. Three-dimensional single molecule localization microscopy reveals the topography of the immunological synapse at isotropic precision below 15 nm. Nano Lett. 21, 9247–9255 (2021). · DOI
  40. Kirichenko, E. Y., Skatchkov, S. N. & Ermakov, A. M. Structure and functions of gap junctions and their constituent connexins in the mammalian CNS. Biochem. Moscow Suppl. Ser. A 15, 107–119 (2021). · DOI
  41. Karatekin, E. et al. A 20-nm step toward the cell membrane preceding exocytosis may correspond to docking of tethered granules. Biophys. J. 94, 2891–2905 (2008). · DOI
  42. Gloegl, M. et al. Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists. Science 386, 1154–1161 (2024). · DOI
  43. Dowling, Q. M. et al. Hierarchical design of pseudosymmetric protein nanocages. Nature 638, 553–561 (2025). · DOI
  44. Sosa, S. et al. Asymmetric bifunctional protein nanoparticles through redesign of self-assembly. Nanoscale Adv. 1, 1833–1846 (2019). · DOI
  45. Yang, E. C. et al. Computational design of non-porous pH-responsive antibody nanoparticles. Nat. Struct. Mol. Biol. 31, 1404–1412 (2024). · DOI
  46. Sheffler, W. et al. Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock. PLoS Comput. Biol. 19, e1010680 (2023). · DOI
  47. Padilla, J. E., Colovos, C. & Yeates, T. O. Nanohedra: using symmetry to design self assembling protein cages, layers, crystals, and filaments. Proc. Natl Acad. Sci. USA 98, 2217–2221 (2001). · DOI
  48. de Haas, R. J. et al. Rapid and automated design of two-component protein nanomaterials using ProteinMPNN. Proc. Natl Acad. Sci. USA 121, e2314646121 (2024). · DOI · Paper page
  49. Meador, K. et al. A suite of designed protein cages using machine learning and protein fragment-based protocols. Structure 32, 751–765.e11 (2024). · DOI · Paper page
  50. Huddy, T. F. et al. Blueprinting extendable nanomaterials with standardized protein blocks. Nature 627, 898–904 (2024). · DOI
  51. Lai, Y.-T. et al. Structure of a designed protein cage that self-assembles into a highly porous cube. Nat. Chem. 6, 1065–1071 (2014). · DOI
  52. Hoffnagle, A. M. & Tezcan, F. A. Atomically accurate design of metalloproteins with predefined coordination geometries. J. Am. Chem. Soc. 145, 14208–14214 (2023). · DOI
  53. Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378, 56–61 (2022). · DOI · Paper page
  54. Ellis, D. et al. Antigen spacing on protein nanoparticles influences antibody responses to vaccination. Cell Rep. 42, 113552 (2023). · DOI
  55. Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021). · DOI
  56. Meng, E. C. et al. UCSF ChimeraX: tools for structure building and analysis. Protein Sci. 32, e4792 (2023). · DOI
  57. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018). · DOI · Paper page
  58. Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021). · DOI
  59. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Cryst. D75, 861–877 (2019).
  60. Davis, I. W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007). · DOI
  61. Kidmose, R. T. et al. Namdinator—automatic molecular dynamics flexible fitting of structural models into cryo-EM and crystallography experimental maps. IUCrJ 6, 526–531 (2019). · DOI
  62. Croll, T. I. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Cryst. D74, 519–530 (2018).
  63. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Cryst. D66, 486–501 (2010).
  64. wwPDB Consortium Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2019). · DOI · Paper page
  65. Ellis, D. et al. Structure-based design of stabilized recombinant influenza neuraminidase tetramers. Nat. Commun. 13, 1825 (2022). · DOI

Metadata

Authors
Rankovic, S., Carr, K. D., Decarreau, J., Skotheim, R., Kibler, R. D., Ols, S., Lee, S., Chun, J.-H., Tooley, M. R., Dauparas, J., Eisenach, H. E., Glögl, M., Weidle, C., Borst, A. J., Baker, D., & King, N. P.
Affiliations
  • University of Washington (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
King, Neil P.
ORCID
Venue
Nature Materials
Publisher
Springer Science and Business Media LLC
Published
2025-07-31
Volume
24
Issue
10
Pages
1635-1643
ISSN
  • 1476-1122 (ISSN-L)
  • 1476-4660
DOI
10.1038/s41563-025-02295-7
PMC
PMC12407187
OpenAlex
W4412780873
Cited by
17
References
8
Citations by year
2026: 13 · 2025: 4
FWCI
5.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Materials (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • Bill and Melinda Gates Foundation (INV-043758)
  • U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U54AI170856, 1P01AI167966, U19AI181881)
  • ARPA-H (P023), the Audacious Project at the Institute for Protein Design, and the Open Philanthropy Project Improving Protein Design Fund.
  • Swedish Research Council
  • Shurl and Kay Curci Foundation
  • Howard Hughes Medical Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Materials Science › Biomaterials › Supramolecular Self-Assembly in Materials
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
Keywords
protein nanomaterials, anisotropic nanoparticles, dihedral symmetry, nanoparticle morphology control

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review