De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy

Abstract

Despite efforts for over 25 years, de novo protein design has not succeeded in achieving the TIM-barrel fold. Here we describe the computational design of four-fold symmetrical (β/α)8 barrels guided by geometrical and chemical principles. Experimental characterization of 33 designs revealed the importance of side chain–backbone hydrogen bonds for defining the strand register between repeat units. The X-ray crystal structure of a designed thermostable 184-residue protein is nearly identical to that of the designed TIM-barrel model. PSI-BLAST searches do not identify sequence similarities to known TIM-barrel proteins, and sensitive profile-profile searches indicate that the design sequence is distant from other naturally occurring TIM-barrel superfamilies, suggesting that Nature has sampled only a subset of the sequence space available to the TIM-barrel fold. The ability to design TIM barrels de novo opens new possibilities for custom-made enzymes.

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: Geometric constraints on the secondary structure arrangement in an ideal four-fold-symmetric TIM barrel.
Figure 2: Sequence determinants of de novo–designed TIM barrel.
Figure 3: Effect of two-fold- versus four-fold-symmetrical barrel interior on stability.
Figure 4: Stability and structure of sTIM-11.
Figure 5: Sequence relationships between natural (β/α)8-barrels and sTIM-11.

Accession codes

Primary accessions

Protein Data Bank

Referenced accessions

Protein Data Bank

References

  1. 1

    Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).

    CAS  Article  Google Scholar 

  2. 2

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

    CAS  Article  Google Scholar 

  3. 3

    Joh, N.H. et al. De novo design of a transmembrane Zn2+-transporting four-helix bundle. Science 346, 1520–1524 (2014).

    CAS  Article  Google Scholar 

  4. 4

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

    CAS  Article  Google Scholar 

  5. 5

    Smadbeck, J. et al. De novo design and experimental characterization of ultrashort self-associating peptides. PLOS Comput. Biol. 10, e1003718 (2014).

    Article  Google Scholar 

  6. 6

    Bellows-Peterson, M.L. et al. De novo peptide design with C3a receptor agonist and antagonist activities: theoretical predictions and experimental validation. J. Med. Chem. 55, 4159–4168 (2012).

    CAS  Article  Google Scholar 

  7. 7

    Khoury, G.A., Smadbeck, J., Kieslich, C.A. & Floudas, C.A. Protein folding and de novo protein design for biotechnological applications. Trends Biotechnol. 32, 99–109 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Correia, B.E. et al. Proof of principle for epitope-focused vaccine design. Nature 507, 201–206 (2014).

    CAS  Article  Google Scholar 

  9. 9

    Sterner, R. & Höcker, B. Catalytic versatility, stability, and evolution of the (βα)8-barrel enzyme fold. Chem. Rev. 105, 4038–4055 (2005).

    CAS  Article  Google Scholar 

  10. 10

    Gerlt, J.A. New wine from old barrels. Nat. Struct. Biol. 7, 171–173 (2000).

    CAS  Article  Google Scholar 

  11. 11

    Höcker, B. Directed evolution of (βα)8-barrel enzymes. Biomol. Eng. 22, 31–38 (2005).

    Article  Google Scholar 

  12. 12

    Kiss, G., Çelebi Ölçüm, N., Moretti, R., Baker, D. & Houk, K.N. Computational enzyme design. Angew. Chem. Int. Ed. Engl. 52, 5700–5725 (2013).

    CAS  Article  Google Scholar 

  13. 13

    Höcker, B., Claren, J. & Sterner, R. Mimicking enzyme evolution by generating new (βα)8-barrels from (βα)4-half-barrels. Proc. Natl. Acad. Sci. USA 101, 16448–16453 (2004).

    Article  Google Scholar 

  14. 14

    Höcker, B., Lochner, A., Seitz, T., Claren, J. & Sterner, R. High-resolution crystal structure of an artificial (βα)8-barrel protein designed from identical half-barrels. Biochemistry 48, 1145–1147 (2009).

    Article  Google Scholar 

  15. 15

    Claren, J., Malisi, C., Höcker, B. & Sterner, R. Establishing wild-type levels of catalytic activity on natural and artificial (βα)8-barrel protein scaffolds. Proc. Natl. Acad. Sci. USA 106, 3704–3709 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Fortenberry, C. et al. Exploring symmetry as an avenue to the computational design of large protein domains. J. Am. Chem. Soc. 133, 18026–18029 (2011).

    CAS  Article  Google Scholar 

  17. 17

    Goraj, K., Renard, A. & Martial, J.A. Synthesis, purification and initial structural characterization of octarellin, a de novo polypeptide modelled on the α/β-barrel Proteins. Protein Eng. 3, 259–266 (1990).

    CAS  Article  Google Scholar 

  18. 18

    Houbrechts, A. et al. Second-generation octarellins: two new de novo (β/α)8 polypeptides designed for investigating the influence of β-residue packing on the α/β-barrel structure stability. Protein Eng. 8, 249–259 (1995).

    CAS  Article  Google Scholar 

  19. 19

    Tanaka, T. et al. Characteristics of a de novo designed protein. Protein Sci. 3, 419–427 (1994).

    CAS  Article  Google Scholar 

  20. 20

    Offredi, F. et al. De novo backbone and sequence design of an idealized α/β-barrel protein: evidence of stable tertiary structure. J. Mol. Biol. 325, 163–174 (2003).

    CAS  Article  Google Scholar 

  21. 21

    Figueroa, M. et al. Octarellin VI: using Rosetta to design a putative artificial (β/α)8 protein. PLoS ONE 8, e71858 (2013).

    CAS  Article  Google Scholar 

  22. 22

    Nagarajan, D., Deka, G. & Rao, M. Design of symmetric TIM barrel proteins from first principles. BMC Biochem. 16, 18 (2015).

    Article  Google Scholar 

  23. 23

    Murzin, A.G., Lesk, A.M. & Chothia, C. Principles determining the structure of β-sheet barrels in proteins I. A theoretical analysis. J. Mol. Biol. 236, 1369–1381 (1994).

    CAS  Article  Google Scholar 

  24. 24

    Ochoa-Leyva, A. et al. Exploring the structure-function loop adaptability of a (β/α)8-barrel enzyme through loop swapping and hinge variability. J. Mol. Biol. 411, 143–157 (2011).

    CAS  Article  Google Scholar 

  25. 25

    Ochoa-Leyva, A. et al. Protein design through systematic catalytic loop exchange in the (β/α)8 fold. J. Mol. Biol. 387, 949–964 (2009).

    CAS  Article  Google Scholar 

  26. 26

    Huang, P.-S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PLoS ONE 6, e24109 (2011).

    CAS  Article  Google Scholar 

  27. 27

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

    CAS  Article  Google Scholar 

  28. 28

    Yang, X., Kathuria, S.V., Vadrevu, R. & Matthews, C.R. βα-hairpin clamps brace βαβ modules and can make substantive contributions to the stability of TIM barrel proteins. PLoS ONE 4, e7179 (2009).

    Article  Google Scholar 

  29. 29

    Höcker, B., Beismann-Driemeyer, S., Hettwer, S., Lustig, A. & Sterner, R. Dissection of a (βα)8-barrel enzyme into two folded halves. Nat. Struct. Biol. 8, 32–36 (2001).

    Article  Google Scholar 

  30. 30

    Romero-Romero, S., Costas, M., Rodríguez-Romero, A. & Fernández-Velasco, D.A. Reversibility and two state behaviour in the thermal unfolding of oligomeric TIM barrel proteins. Phys. Chem. Chem. Phys. 17, 20699–20714 (2015).

    CAS  Article  Google Scholar 

  31. 31

    Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).

    CAS  Article  Google Scholar 

  32. 32

    Minami, S., Sawada, K. & Chikenji, G. MICAN: a protein structure alignment algorithm that can handle multiple-chains, inverse alignments, C(α) only models, alternative alignments, and non-sequential alignments. BMC Bioinformatics 14, 24 (2013).

    CAS  Article  Google Scholar 

  33. 33

    Altschul, S.F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  Article  Google Scholar 

  34. 34

    Söding, J. Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005).

    Article  Google Scholar 

  35. 35

    Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175 (2012).

    CAS  Article  Google Scholar 

  36. 36

    Farías-Rico, J.A., Schmidt, S. & Höcker, B. Evolutionary relationship of two ancient protein superfolds. Nat. Chem. Biol. 10, 710–715 (2014).

    Article  Google Scholar 

  37. 37

    Alva, V., Remmert, M., Biegert, A., Lupas, A.N. & Söding, J. A galaxy of folds. Protein Sci. 19, 124–130 (2010).

    CAS  PubMed  Google Scholar 

  38. 38

    Rämisch, S., Weininger, U., Martinsson, J., Akke, M. & Andre, I. Computational design of a leucine-rich repeat protein with a predefined geometry. Proc. Natl. Acad. Sci. USA 111, 17875–17880 (2014).

    Article  Google Scholar 

  39. 39

    Giger, L. et al. Evolution of a designed retro-aldolase leads to complete active site remodeling. Nat. Chem. Biol. 9, 494–498 (2013).

    CAS  Article  Google Scholar 

  40. 40

    Kabsch, W. & Sander, C. Dictionary of protein secondary structure—pattern-recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637 (1983).

    CAS  Article  Google Scholar 

  41. 41

    Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr 66, 125–132 (2010).

  42. 42

    Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics.Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).

  43. 43

    DiMaio, F. et al. Improved low-resolution crystallographic refinement with Phenix and Rosetta. Nat. Methods 10, 1102–1104 (2013).

    CAS  Article  Google Scholar 

  44. 44

    Song, Y. et al. High-resolution comparative modeling with RosettaCM. Structure 21, 1735–1742 (2013).

    CAS  Article  Google Scholar 

  45. 45

    Afonine, P.V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Frickey, T. & Lupas, A. CLANS: a Java application for visualizing protein families based on pairwise similarity. Bioinformatics 20, 3702–3704 (2004).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank R. Krishnamurty and C. Tinberg for comments on the manuscript, as well as S. Ovchinnikov, A.C. Stiel and S. Schmidt for technical advice. D.A.F.V. thanks the Dirección General de Asuntos del Personal Académico UNAM for a sabbatical stay fellowship, and K.F. acknowledges a fellowship from the International Max Planck Research School (IMPRS) “From Molecules to Organisms” Tübingen. F.P. was supported by Human Frontier Science Program Long-term fellowship LT000070/2009-l. This work was facilitated through the use of advanced computational storage and networking infrastructure provided by the Hyak supercomputer system at the University of Washington. This research was also done using resources provided by the Open Science Grid (OSG), which is supported by the US National Science Foundation and the US Department of Energy's Office of Science. We would like to particularly thank M. Rynge and J. McGee of the OSG Engagement Team at the Renaissance Computing Institute (RENCI) and M. Livny and the HTCondor Team of the University of Wisconsin–Madison for their technical and logistical guidance in our use of OSG resources. This work was supported by grants from the Defense Threat Reduction Agency (DTRA) and the Howard Hughes Medical Institute to D.B., Deutsche Forschungsgemeinschaft grant HO4022/1-2 and Max Planck funds to B.H., and grants CONACYT 99857 and PAPIIT-UNAM IN219913 to D.A.F.V.

Author information

Affiliations

Authors

Contributions

P.-S.H., K.F., D.A.F.V., B.H. and D.B. designed the research. P.-S.H. wrote program code and designed structures with help from B.H. and D.A.F.V. P.-S.H., F.P. and D.A.F.V. built the clones. K.F. and B.H. solved the crystal structure of sTIM-11 and collected thermodynamic data for sTIMs. F.P. characterized the first designs. P.-S.H., K.F., B.H. and D.B. collected and analyzed sequence and structure comparison data. K.F. and B.H. generated the cluster map. P.-S.H., K.F., B.H. and D.B. wrote the manuscript with help from all the authors. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Po-Ssu Huang or Birte Höcker or David Baker.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–9 and Supplementary Tables 1–5. (PDF 6188 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, PS., Feldmeier, K., Parmeggiani, F. et al. De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat Chem Biol 12, 29–34 (2016). https://doi.org/10.1038/nchembio.1966

Download citation

Further reading

Search

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