Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Accurate design of co-assembling multi-component protein nanomaterials

Abstract

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Overview of the computational design method.
Figure 2: Experimental characterization of co-assembly.
Figure 3: Designed interfaces of two-component protein nanomaterials.
Figure 4: Electron micrographs of designed two-component protein nanomaterials.
Figure 5: Crystal structures of designed two-component protein nanomaterials.

Accession codes

Primary accessions

Protein Data Bank

Data deposits

The crystal structures and structure factors for the designed materials have been deposited in the RCSB Protein Data Bank (http://www.rcsb.org/) under the accession codes 4NWN (T32-28), 4NWO (T33-15), 4NWP (T33-21, R32 crystal form), 4NWQ (T33-21, F4132 crystal form) and 4NWR (T33-28).

References

  1. 1

    Howorka, S. Rationally engineering natural protein assemblies in nanobiotechnology. Curr. Opin. Biotechnol. 22, 485–491 (2011)

    CAS  Article  Google Scholar 

  2. 2

    Douglas, T. & Young, M. Viruses: making friends with old foes. Science 312, 873–875 (2006)

    ADS  CAS  Article  Google Scholar 

  3. 3

    Lai, Y. T., King, N. P. & Yeates, T. O. Principles for designing ordered protein assemblies. Trends Cell Biol. 22, 653–661 (2012)

    CAS  Article  Google Scholar 

  4. 4

    King, N. P. & Lai, Y. T. Practical approaches to designing novel protein assemblies. Curr. Opin. Struct. Biol. 23, 632–638 (2013)

    CAS  Article  Google Scholar 

  5. 5

    Sinclair, J. C. Constructing arrays of proteins. Curr. Opin. Chem. Biol. 17, 946–951 (2013)

    CAS  Article  Google Scholar 

  6. 6

    Salgado, E. N., Radford, R. J. & Tezcan, F. A. Metal-directed protein self-assembly. Acc. Chem. Res. 43, 661–672 (2010)

    CAS  Article  Google Scholar 

  7. 7

    King, N. P. et al. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336, 1171–1174 (2012)

    ADS  CAS  Article  Google Scholar 

  8. 8

    Brodin, J. D. et al. Metal-directed, chemically tunable assembly of one-, two- and three-dimensional crystalline protein arrays. Nature Chem. 4, 375–382 (2012)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Lanci, C. J. et al. Computational design of a protein crystal. Proc. Natl Acad. Sci. USA 109, 7304–7309 (2012)

    ADS  CAS  Article  Google Scholar 

  10. 10

    Stranges, P. B., Machius, M., Miley, M. J., Tripathy, A. & Kuhlman, B. Computational design of a symmetric homodimer using beta-strand assembly. Proc. Natl Acad. Sci. USA 108, 20562–20567 (2011)

    ADS  CAS  Article  Google Scholar 

  11. 11

    Sinclair, J. C., Davies, K. M., Venien-Bryan, C. & Noble, M. E. Generation of protein lattices by fusing proteins with matching rotational symmetry. Nature Nanotechnol. 6, 558–562 (2011)

    ADS  CAS  Article  Google Scholar 

  12. 12

    Lai, Y. T., Cascio, D. & Yeates, T. O. Structure of a 16-nm cage designed by using protein oligomers. Science 336, 1129 (2012)

    ADS  CAS  Article  Google Scholar 

  13. 13

    Der, B. S. et al. Metal-mediated affinity and orientation specificity in a computationally designed protein homodimer. J. Am. Chem. Soc. 134, 375–385 (2012)

    CAS  Article  Google Scholar 

  14. 14

    Fletcher, J. M. et al. Self-assembling cages from coiled-coil peptide modules. Science 340, 595–599 (2013)

    ADS  CAS  Article  Google Scholar 

  15. 15

    Boyle, A. L. et al. Squaring the circle in peptide assembly: from fibers to discrete nanostructures by de novo design. J. Am. Chem. Soc. 134, 15457–15467 (2012)

    CAS  Article  Google Scholar 

  16. 16

    Grigoryan, G. et al. Computational design of virus-like protein assemblies on carbon nanotube surfaces. Science 332, 1071–1076 (2011)

    ADS  CAS  Article  Google Scholar 

  17. 17

    Seeman, N. C. Nanomaterials based on DNA. Annu. Rev. Biochem. 79, 65–87 (2010)

    CAS  Article  Google Scholar 

  18. 18

    Rothemund, P. W. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006)

    ADS  CAS  Article  Google Scholar 

  19. 19

    Ke, Y., Ong, L. L., Shih, W. M. & Yin, P. Three-dimensional structures self-assembled from DNA bricks. Science 338, 1177–1183 (2012)

    ADS  CAS  Article  Google Scholar 

  20. 20

    Han, D. et al. DNA gridiron nanostructures based on four-arm junctions. Science 339, 1412–1415 (2013)

    ADS  CAS  Article  Google Scholar 

  21. 21

    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)

    ADS  CAS  Article  Google Scholar 

  22. 22

    Usui, K. et al. Nanoscale elongating control of the self-assembled protein filament with the cysteine-introduced building blocks. Protein Sci. 18, 960–969 (2009)

    CAS  Article  Google Scholar 

  23. 23

    Goodsell, D. S. & Olson, A. J. Structural symmetry and protein function. Annu. Rev. Biophys. Biomol. Struct. 29, 105–153 (2000)

    CAS  Article  Google Scholar 

  24. 24

    Janin, J., Bahadur, R. P. & Chakrabarti, P. Protein-protein interaction and quaternary structure. Q. Rev. Biophys. 41, 133–180 (2008)

    CAS  Article  Google Scholar 

  25. 25

    Huang, P. S., Love, J. J. & Mayo, S. L. A de novo designed protein protein interface. Protein Sci. 16, 2770–2774 (2007)

    CAS  Article  Google Scholar 

  26. 26

    Jha, R. K. et al. Computational design of a PAK1 binding protein. J. Mol. Biol. 400, 257–270 (2010)

    CAS  Article  Google Scholar 

  27. 27

    Karanicolas, J. et al. A de novo protein binding pair by computational design and directed evolution. Mol. Cell 42, 250–260 (2011)

    CAS  Article  Google Scholar 

  28. 28

    Fleishman, S. J. et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816–821 (2011)

    ADS  CAS  Article  Google Scholar 

  29. 29

    Khare, S. D. & Fleishman, S. J. Emerging themes in the computational design of novel enzymes and protein-protein interfaces. FEBS Lett. 587, 1147–1154 (2013)

    CAS  Article  Google Scholar 

  30. 30

    Kuhlman, B. & Baker, D. Native protein sequences are close to optimal for their structures. Proc. Natl Acad. Sci. USA 97, 10383–10388 (2000)

    ADS  CAS  Article  Google Scholar 

  31. 31

    Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011)

    CAS  Article  Google Scholar 

  32. 32

    DiMaio, F., Leaver-Fay, A., Bradley, P., Baker, D. & Andre, I. Modeling symmetric macromolecular structures in Rosetta3. PLoS ONE 6, e20450 (2011)

    ADS  CAS  Article  Google Scholar 

  33. 33

    Lawrence, M. C. & Colman, P. M. Shape complementarity at protein/protein interfaces. J. Mol. Biol. 234, 946–950 (1993)

    CAS  Article  Google Scholar 

  34. 34

    Arnold, F. H. & Volkov, A. A. Directed evolution of biocatalysts. Curr. Opin. Chem. Biol. 3, 54–59 (1999)

    CAS  Article  Google Scholar 

  35. 35

    Jäckel, C., Kast, P. & Hilvert, D. Protein design by directed evolution. Annu. Rev. Biophys. 37, 153–173 (2008)

    Article  Google Scholar 

  36. 36

    Wörsdörfer, B., Pianowski, Z. & Hilvert, D. Efficient in vitro encapsulation of protein cargo by an engineered protein container. J. Am. Chem. Soc. 134, 909–911 (2012)

    Article  Google Scholar 

  37. 37

    Wörsdörfer, B., Woycechowsky, K. J. & Hilvert, D. Directed evolution of a protein container. Science 331, 589–592 (2011)

    ADS  Article  Google Scholar 

  38. 38

    Bradley, P. & Baker, D. Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation. Proteins 65, 922–929 (2006)

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank D. Shi and B. Nannenga (JFRC) for help with electron microscopy, F. DiMaio and R. Moretti for assistance with software development, P. Greisen for scripts used to compare side-chain conformations, J. Gallaher for technical assistance, M. Collazo for help with preliminary crystallization screening, D. Cascio and M. Sawaya for help with crystallographic experiments, and M. Capel, J. Schuermann and I. Kourinov at NE-CAT beamline 24-ID-C for help with data collection. This work was supported by the Howard Hughes Medical Institute (T.G. and D.B.) and the JFRC visitor program (S.G.), the National Science Foundation under CHE-1332907 (D.B. and T.O.Y.), grants from the International AIDS Vaccine Initiative, DTRA (N00024-10-D-6318/0024), AFOSR (FA950-12-10112) and DOE (DE-SC0005155) to D.B., an NIH Biotechnology Training Program award to D.E.M. (T32GM067555) and an NSF graduate research fellowship to J.B.B. (DGE-0718124). T.O.Y. and D.E.M. also acknowledge support from the BER programme of the DOE Office of Science. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies.

Author information

Affiliations

Authors

Contributions

N.P.K., J.B.B., W.S. and D.B. designed the research. N.P.K., J.B.B. and W.S. wrote program code and performed the docking and design calculations. N.P.K. and J.B.B. biophysically characterized the designed materials and prepared samples for structural analysis. S.G. characterized the designed materials by electron microscopy; S.G. and T.G. analysed electron microscopy data. D.E.M. crystallized the designed protein materials; D.E.M. and T.O.Y. analysed crystallographic data. N.P.K., J.B.B. and D.B. analysed data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to David Baker.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Comparison of one-component and multi-component symmetric fold trees.

Within the Rosetta macromolecular modelling suite, the connections between residues in a protein structure are represented as a directed, acyclic, graph referred to as a ‘fold tree’32,38. When modelling multiple subunits in symmetric systems, the rigid body orientations of the subunits can be controlled by modifying the appropriate connections in the fold tree. In this work, we have extended Rosetta to allow multiple, independently managed connections from the fold tree to the subunits in the asymmetric unit (ASU) of the modelled structure. To demonstrate the new behaviour enabled by this change, three different symmetric fold tree representations of a D32 architecture are shown. In this architecture, which is used because of its relative simplicity, two trimeric building blocks (wheat) are aligned along the three-fold rotational axes of D3 point-group symmetry and three dimeric building blocks (light blue) are aligned along the two-fold rotational axes. a, The dimer-centric one-component symmetry case. Rigid-body degree of freedom (RB DOF, black lines) JD3 connecting the dimer subunit to the trimer subunit in the ASU is downstream of RB DOFs JD1 and JD2 controlling the dimer subunit; in this case the positions of the trimeric subunits depend on the positions of the dimeric subunits. b, The trimer-centric one-component symmetry case. RB DOF JT3 connecting the trimer subunit to the dimer subunit in the ASU is downstream of RB DOFs JT1 and JT2 controlling the trimer subunit; in this case the positions of the dimeric subunits depend on the positions of the trimeric subunits. c, The multi-component symmetry case. With multi-component symmetric modelling, the RB DOFs controlling the trimer subunit (JT1 and JT2) and the dimer subunit (JD1 and JD2) in the ASU are independent. In this case the positions of the dimeric subunits do not depend on the positions of the trimeric subunits and vice versa, allowing the internal DOFs for each building block (JT2 and JD2) to be maintained while moving the building blocks independently (JT1 and JD1). See Supplementary Methods for additional discussion.

Extended Data Figure 2 Models of the 57 designs selected for experimental characterization.

Smoothed surface representations of each of the 30 T32 and 27 T33 designs are shown. The trimeric component of each T32 design is shown in grey and the dimeric component in orange. The two different trimeric components of each T33 design are shown in blue and green. The tetrahedral two-fold and three-fold symmetry axes (black lines) are shown passing through the centre of each component. Each design is named according to its symmetric architecture (T32 or T33) followed by a unique identification number. The pairs of scaffold proteins from which the designs are derived are also indicated.

Extended Data Figure 3 Native PAGE analysis of cleared cell lysates.

Each gel contains cleared lysates pertaining to a, T32-28, b, T33-09, c, T33-15, d, T33-21, or e, T33-28. Lane 1 is from cells expressing the wild-type scaffold for component A and lane 2 the wild-type scaffold for component B. Lanes 3 and 4 are from cells expressing the individual design components and lanes 5 and 6 the co-expressed components. Lanes 7 and 8 are from samples mixed as crude equal volume or crude adjusted volume (cr.e.v. or cr.a.v.) lysates, while lanes 9 and 10 are from samples mixed as cleared lysates (cl.e.v. or cl.a.v.). Lane 5 is from cells expressing the C-terminally A1-tagged constructs; all other lanes are from cells expressing the C-terminally His-tagged constructs. An arrow is positioned next to each gel indicating the migration of 24-subunit assemblies and the gel regions containing unassembled building blocks are bracketed. Each gel was stained with GelCode Blue. Portions of the gels in a and c are also shown in Fig. 2b.

Extended Data Figure 4 Structural metrics for the computational design models.

Selected metrics related to the designed interfaces are plotted for the 57 designs that were experimentally characterized, including a, the predicted binding energy measured in Rosetta energy units (REU), b, the surface area buried by each instance of the designed interface, c, the binding energy density (calculated as the predicted binding energy divided by the buried surface area), d, the number of buried unsatisfied polar groups at the designed interface, e, the shape complementarity of the designed interface, and f, the total number of mutations in each designed pair of proteins. Each circle represents a single design; the five successful materials are plotted as filled circles and labelled. In each plot, the designs are arranged on the x axis in order of increasing value of the metric analysed.

Extended Data Figure 5 Electron micrographs of in vitro-assembled T33-15.

Negative stain micrographs of independently purified T33-15A (a) and T33-15B (b), as well as unpurified, in vitro-assembled T33-15 (c) are shown to scale (scale bar at right, 25 nm).

Extended Data Table 1 Root mean square deviations (r.m.s.d.) between crystal structures and design models

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Tables 1-7 and Supplementary References. (PDF 1036 kb)

Design Models

Zipped folder containing design models. (ZIP 4714 kb)

Docking

Zipped folder containing example files for docking protocol. (ZIP 64 kb)

Design

Zipped folder containing example files for design protocol. (ZIP 77 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

King, N., Bale, J., Sheffler, W. et al. Accurate design of co-assembling multi-component protein nanomaterials. Nature 510, 103–108 (2014). https://doi.org/10.1038/nature13404

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing