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.

  • Article
  • Published:

Computational design of self-assembling cyclic protein homo-oligomers

Nature Chemistry volume 9, pages 353–360 (2017)Cite this article

Subjects

Abstract

Self-assembling cyclic protein homo-oligomers play important roles in biology, and the ability to generate custom homo-oligomeric structures could enable new approaches to probe biological function. Here we report a general approach to design cyclic homo-oligomers that employs a new residue-pair-transform method to assess the designability of a protein–protein interface. This method is sufficiently rapid to enable the systematic enumeration of cyclically docked arrangements of a monomer followed by sequence design of the newly formed interfaces. We use this method to design interfaces onto idealized repeat proteins that direct their assembly into complexes that possess cyclic symmetry. Of 96 designs that were characterized experimentally, 21 were found to form stable monodisperse homo-oligomers in solution, and 15 (four homodimers, six homotrimers, six homotetramers and one homopentamer) had solution small-angle X-ray scattering data consistent with the design models. X-ray crystal structures were obtained for five of the designs and each is very close to their corresponding computational model.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Computational design protocol.
Figure 2: Assessment of the solution conformation of selected cyclic oligomers.
Figure 3: Comparison between the experimentally determined crystal structures and corresponding design models.
Figure 4: Robustness of designs to subunit extension by repeat addition.

Similar content being viewed by others

Accession codes

Primary accessions

Protein Data Bank

References

  1. Ali, M. H. & Imperiali, B. Protein oligomerization: how and why. Bioorg. Med. Chem. 13, 5013–5020 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Nishi, H., Hashimoto, K., Madej, T. & Panchenko, A. R. Evolutionary, physicochemical, and functional mechanisms of protein homooligomerization. Prog. Mol. Biol. Transl. 117, 3–24 (2013).

    Article  CAS  Google Scholar 

  4. Fletcher, J. M. et al. A basis set of de novo coiled-coil peptide oligomers for rational protein design and synthetic biology. ACS Synth. Biol. 1, 240–250 (2012).

    Article  CAS  Google Scholar 

  5. Smock, R. G., Yadid, I., Dym, O., Clarke, J. & Tawfik, D. S. De novo evolutionary emergence of a symmetrical protein is shaped by folding constraints. Cell 164, 476–486 (2016).

    Article  CAS  Google Scholar 

  6. Voet, A. R. D. et al. Computational design of a self-assembling symmetrical β-propeller protein. Proc. Natl Acad. Sci. USA 111, 15102–15107 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. 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).

    Article  CAS  Google Scholar 

  9. Mou, Y., Huang, P. S., Hsu, F. C., Huang, S. J. & Mayo, S. L. Computational design and experimental verification of a symmetric protein homodimer. Proc. Natl Acad. Sci. USA 112, 10714–10719 (2015).

    Article  CAS  Google Scholar 

  10. Huang, P. S. et al. De novo design of an ideal TIM-barrel scaffold. Protein Sci. 24, 186–186 (2015).

    Google Scholar 

  11. Lin, Y. -R. et al. Control over overall shape and size in de novo designed proteins. Proc. Natl Acad. Sci. USA 112, E5478–E5485 (2015).

    Article  CAS  Google Scholar 

  12. Koga, N. et al. Principles for designing ideal protein structures. Nature 491, 222–227 (2012).

    Article  CAS  Google Scholar 

  13. Parmeggiani, F. et al. A general computational approach for repeat protein design. J. Mol. Biol. 427, 563–575 (2015).

    Article  CAS  Google Scholar 

  14. Huang, P. -S. et al. High thermodynamic stability of parametrically designed helical bundles. Science 346, 481–485 (2014).

    Article  CAS  Google Scholar 

  15. Brunette, T. J. et al. Exploring the repeat protein universe through computational protein design. Nature 528 580–584 (2015).

    Article  CAS  Google Scholar 

  16. Main, E. R. G., Xiong, Y., Cocco, M. J., D'Andrea, L. & Regan, L. Design of stable alpha-helical arrays from an idealized TPR motif. Structure 11, 497–508 (2003).

    Article  CAS  Google Scholar 

  17. Pechmann, S., Levy, E. D., Tartaglia, G. G. & Vendruscolo, M. Physicochemical principles that regulate the competition between functional and dysfunctional association of proteins. Proc. Natl Acad. Sci. USA 106, 10159–10164 (2009).

    Article  CAS  Google Scholar 

  18. Levy, E. D. & Teichmann, S. Structural, evolutionary, and assembly principles of protein oligomerization. Prog. Mol. Biol. Transl. 117, 25–51 (2013).

    Article  CAS  Google Scholar 

  19. Bahadur, R. P., Chakrabarti, P., Rodier, F. & Janin, J. Dissecting subunit interfaces in homodimeric proteins. Proteins 53, 708–719 (2003).

    Article  CAS  Google Scholar 

  20. Bahadur, R. P., Chakrabarti, P., Rodier, F. & Janin, J. A dissection of specific and non-specific protein–protein interfaces. J. Mol. Biol. 336, 943–955 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Leaver-Fay, A. et al. in Computer Methods, Part C Vol. 487 (eds Johnson, M. L. & Brand, L.) 545–574 (Elsevier, 2011).

    Book  Google Scholar 

  23. Chen, R. & Weng, Z. P. Docking unbound proteins using shape complementarity, desolvation, and electrostatics. Proteins 47, 281–294 (2002).

    Article  CAS  Google Scholar 

  24. Pierce, B., Tong, W. W. & Weng, Z. P. M-ZDOCK: a grid-based approach for C n symmetric multimer docking. Bioinformatics 21, 1472–1478 (2005).

    Article  CAS  Google Scholar 

  25. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R. & Wolfson, H. J. Patchdock and symmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 33, W363–W367 (2005).

    Article  CAS  Google Scholar 

  26. Gray, J. J. & Baker, D. Protein–protein docking predictions with RosettaDock. Biophys. J. 86, 306A–306A (2004).

    Google Scholar 

  27. Gray, J. J. et al. Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331, 281–299 (2003).

    Article  CAS  Google Scholar 

  28. Wei, H. et al. Lysozyme-stabilized gold fluorescent cluster: synthesis and application as Hg2+ sensor. Analyst 135, 1406–1410 (2010).

    Article  CAS  Google Scholar 

  29. Lu, M., Dousis, A. D. & Ma, J. OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. J. Mol. Biol. 376, 288–301 (2008).

    Article  CAS  Google Scholar 

  30. Zheng, W. H., Schafer, N. P., Davtyan, A., Papoian, G. A. & Wolynes, P. G. Predictive energy landscapes for protein–protein association. Proc. Natl Acad. Sci. USA 109, 19244–19249 (2012).

    Article  CAS  Google Scholar 

  31. DeBartolo, J., Dutta, S., Reich, L. & Keating, A. E. Predictive Bcl-2 family binding models rooted in experiment or structure. J. Mol. Biol. 422, 124–144 (2012).

    Article  CAS  Google Scholar 

  32. Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS One 6, e20161 (2011).

    Article  CAS  Google Scholar 

  33. Schneidman-Duhovny, D., Hammel, M. & Sali, A. FoXS: a web server for rapid computation and fitting of SAXS profiles. Nucleic Acids Res. 38, W540–W544 (2010).

    Article  CAS  Google Scholar 

  34. Boyken, S. E. et al. De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science 352, 680–687 (2016).

    Article  CAS  Google Scholar 

  35. Park, K. et al. Control of repeat-protein curvature by computational protein design. Nat. Struct. Mol. Biol. 22, 167–174 (2015).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Howard Hughes Medical Institute (HHMI), Air Force Office for Scientific Research (AFOSR FA950-12-10112), the National Science Foundation (NSF MCB-1445201 and CHE-1332907), the Bill and Melinda Gates Foundation (OPP1120319) and the Defense Threat Reduction Agency (HDTRA1-11-C-0026 AM06). We thank R. Koga and L. Carter for assistance with SEC–MALS. We thank M. Collazo and M. Sawaya supported by Department of Energy (DOE Grant DE-FC02-02ER63421. We thank M. Capel, K. Rajashankar, N. Sukumar, J. Schuermann, I. Kourinov and F. Murphy at Northeastern Collaborative Access Team supported by grants from the National Center for Research Resources (5P41RR015301-10) and the National Institute of General Medical Sciences (NIGMS P41GM103403-10) from the National Institutes of Health (NIH). Use of the APS is supported by the DOE under Contract DE-AC02-06CH11357. X-ray crystallography and SAXS data were collected at the Advanced Light Source (ALS, Lawrence Berkeley National Laboratory, Berkeley, California Department of Energy, contract no. DE-AC02-05CH11231); SAXS data were collected through the SIBYLS mail-in SAXS program under the aforementioned contract no. and is funded by DOE BER IDAT, NIH MINOS (RO1GM105404) and the ALS, and we thank K. Burnett and G. Hura. The Berkeley Center for Structural Biology is supported in part by the NIH, NIGMS and the HHMI. The ALS is supported by the Director, Office of Science, Office of Basic Energy Sciences of the US DOE under contract no. DE-AC02-05CH11231.

Author information

Author notes

  1. Dan E. McNamara

    Present address: Present address: Departments of Structural Biology and of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA,

  2. Jorge A. Fallas, George Ueda and William Sheffler: These authors contributed equally to this work

Authors and Affiliations

  1. Department of Biochemistry, University of Washington, Seattle, 98195, Washington, USA

    Jorge A. Fallas, George Ueda, William Sheffler, Fabio Parmeggiani, T. J. Brunette & David Baker

  2. Institute for Protein Design, University of Washington, Seattle, 98195, Washington, USA

    Jorge A. Fallas, George Ueda, William Sheffler, Vanessa Nguyen, Fabio Parmeggiani, T. J. Brunette & David Baker

  3. Department of Chemistry and Biochemistry, University of California Los Angles, Los Angeles, 90095, California, USA

    Dan E. McNamara, Duilio Cascio & Todd R. Yeates

  4. Berkeley Center for Structural Biology, Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, Berkeley, 94720, California, USA

    Banumathi Sankaran, Jose Henrique Pereira & Peter Zwart

  5. Joint BioEnergy Institute, Emeryville, 94608, California, USA

    Jose Henrique Pereira

  6. Howard Hughes Medical Institute, University of Washington, Seattle, 98195, Washington, USA

    David Baker

Authors
  1. Jorge A. Fallas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Sheffler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vanessa Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dan E. McNamara
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Banumathi Sankaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jose Henrique Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fabio Parmeggiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. T. J. Brunette
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Duilio Cascio
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Todd R. Yeates
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Peter Zwart
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. David Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.A.F., G.U., W.S. and D.B. designed the research. W.S. developed the RPX method and wrote the program code. J.A.F., G.U. and V.N. carried out design calculations, and purified and biophysically characterized the designed proteins. F.P. and T.J.B. designed and characterized the monomeric repeat proteins used as scaffolds. D.E.M., D.C., T.R.Y., J.H.P., G.U. and J.A.F crystallized the designed proteins. D.E.M, D.C., B.S. and P.Z. collected and analysed crystallographic data. J.A.F., D.E.M., D.C., B.S. and P.Z. solved the structures. All the 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.

Supplementary information

Supplementary information

Supplementary information (PDF 6942 kb)

Symmetry Definition File

symmetry name C2 (TXT 0 kb)

Supplementary information

symmetry name C3 (TXT 0 kb)

Symmetry Definition File

symmetry name C4 (TXT 0 kb)

Symmetry Definition File

symmetry name C5 (TXT 0 kb)

Symmetry Definition File

symmetry name C6 (TXT 0 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fallas, J., Ueda, G., Sheffler, W. et al. Computational design of self-assembling cyclic protein homo-oligomers. Nature Chem 9, 353–360 (2017). https://doi.org/10.1038/nchem.2673

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchem.2673

This article is cited by

Search

Advanced 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