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Modular and tunable biological feedback control using a de novo protein switch

Nature volume 572, pages 265–269 (2019)Cite this article

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A Publisher Correction to this article was published on 25 February 2020

This article has been updated

Abstract

De novo-designed proteins1,2,3 hold great promise as building blocks for synthetic circuits, and can complement the use of engineered variants of natural proteins4,5,6,7. One such designer protein—degronLOCKR, which is based on ‘latching orthogonal cage–key proteins’ (LOCKR) technology8—is a switch that degrades a protein of interest in vivo upon induction by a genetically encoded small peptide. Here we leverage the plug-and-play nature of degronLOCKR to implement feedback control of endogenous signalling pathways and synthetic gene circuits. We first generate synthetic negative and positive feedback in the yeast mating pathway by fusing degronLOCKR to endogenous signalling molecules, illustrating the ease with which this strategy can be used to rewire complex endogenous pathways. We next evaluate feedback control mediated by degronLOCKR on a synthetic gene circuit9, to quantify the feedback capabilities and operational range of the feedback control circuit. The designed nature of degronLOCKR proteins enables simple and rational modifications to tune feedback behaviour in both the synthetic circuit and the mating pathway. The ability to engineer feedback control into living cells represents an important milestone in achieving the full potential of synthetic biology10,11,12. More broadly, this work demonstrates the large and untapped potential of de novo design of proteins for generating tools that implement complex synthetic functionalities in cells for biotechnological and therapeutic applications.

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Fig. 1: DegronLOCKR is a modular tool for controlling biological pathways.
Fig. 2: DegronLOCKR implementation of synthetic feedback on the mating pathway.
Fig. 3: Quantifying properties of degronLOCKR feedback via control of a synthetic circuit.
Fig. 4: DegronLOCKR synthetic feedback is tunable.

Data availability

All data that support the findings of this study are available within the Letter and its Supplementary Information. Original data that supports the findings are available from the corresponding author upon reasonable request. Plasmids that encode the synthetic controller and process are available from Addgene (plasmids 127164 and 127165).

Code availability

Code that supports the findings of this study is available at https://github.com/andrewng1023/NatureFeedbackdegronLOCKR.

Change history

  • 25 February 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 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  ADS  CAS  Google Scholar 

  2. Huang, P.-S., Boyken, S. E. & Baker, D. The coming of age of de novo protein design. Nature 537, 320–327 (2016).

    Article  ADS  CAS  Google Scholar 

  3. Chen, Z. et al. Programmable design of orthogonal protein heterodimers. Nature 565, 106–111 (2019).

    Article  ADS  CAS  Google Scholar 

  4. Andrews, L. B., Nielsen, A. A. K. & Voigt, C. A. Cellular checkpoint control using programmable sequential logic. Science 361, eaap8987 (2018).

    Article  Google Scholar 

  5. Toda, S., Blauch, L. R., Tang, S. K. Y., Morsut, L. & Lim, W. A. Programming self-organizing multicellular structures with synthetic cell–cell signaling. Science 361, 156–162 (2018)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gao, X. J., Chong, L. S., Kim, M. S. & Elowitz, M. B. Programmable protein circuits in living cells. Science 361, 1252–1258 (2018).

    Article  ADS  CAS  Google Scholar 

  7. Bashor, C. J. et al. Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies. Science 364, 593–597 (2019).

    Article  ADS  CAS  Google Scholar 

  8. Langan, R. et al. De novo design of bioactive protein switches. Nature https://doi.org/10.1038/s41586-019-1432-8 (2019).

    Article  ADS  CAS  Google Scholar 

  9. Aranda-Díaz, A., Mace, K., Zuleta, I., Harrigan, P. & El-Samad, H. Robust synthetic circuits for two-dimensional control of gene expression in yeast. ACS Synth. Biol. 6, 545–554 (2017).

    Article  Google Scholar 

  10. Briat, C., Gupta, A. & Khammash, M. Antithetic integral feedback ensures robust perfect adaptation in noisy biomolecular networks. Cell Syst. 2, 15–26 (2016).

    Article  CAS  Google Scholar 

  11. Del Vecchio, D., Dy, A. J. & Qian, Y. Control theory meets synthetic biology. J. R. Soc. Interface 13, 20160380 (2016).

    Article  Google Scholar 

  12. Aoki, S. K. et al. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570, 533–537 (2019).

    Article  CAS  Google Scholar 

  13. Hoyt, M. A., Zhang, M. & Coffino, P. Ubiquitin-independent mechanisms of mouse ornithine decarboxylase degradation are conserved between mammalian and fungal cells. J. Biol. Chem. 278, 12135–12143 (2003).

    Article  CAS  Google Scholar 

  14. Bardwell, L. A walk-through of the yeast mating pheromone response pathway. Peptides 26, 339–350 (2005).

    Article  Google Scholar 

  15. McCullagh, E., Seshan, A., El-Samad, H. & Madhani, H. D. Coordinate control of gene expression noise and interchromosomal interactions in a MAP kinase pathway. Nat. Cell Biol. 12, 954–962 (2010).

    Article  CAS  Google Scholar 

  16. Chen, R. E., Patterson, J. C., Goupil, L. S. & Thorner, J. Dynamic localization of Fus3 mitogen-activated protein kinase is necessary to evoke appropriate responses and avoid cytotoxic effects. Mol. Cell. Biol. 30, 4293–4307 (2010).

    Article  CAS  Google Scholar 

  17. Harrigan, P., Madhani, H. D. & El-Samad, H. Real-time genetic compensation defines the dynamic demands of feedback control. Cell 175, 877–886 (2018).

    Article  CAS  Google Scholar 

  18. Lee, M. E., DeLoache, W. C., Cervantes, B. & Dueber, J. E. A highly characterized yeast toolkit for modular, multipart assembly. ACS Synth. Biol. 4, 975–986 (2015).

    Article  CAS  Google Scholar 

  19. Renicke, C., Schuster, D., Usherenko, S., Essen, L.-O. & Taxis, C. A LOV2 domain-based optogenetic tool to control protein degradation and cellular function. Chem. Biol. 20, 619–626 (2013).

    Article  CAS  Google Scholar 

  20. Ma, W., Trusina, A., El-Samad, H., Lim, W. A. & Tang, C. Defining network topologies that can achieve biochemical adaptation. Cell 138, 760–773 (2009).

    Article  CAS  Google Scholar 

  21. Bashor, C. J., Helman, N. C., Yan, S. & Lim, W. A. Using engineered scaffold interactions to reshape MAP kinase pathway signaling dynamics. Science 319, 1539–1543 (2008).

    Article  ADS  CAS  Google Scholar 

  22. Wei, P. et al. Bacterial virulence proteins as tools to rewire kinase pathways in yeast and immune cells. Nature 488, 384–388 (2012).

    Article  ADS  CAS  Google Scholar 

  23. Lim, W. A. & June, C. H. The principles of engineering immune cells to treat cancer. Cell 168, 724–740 (2017).

    Article  CAS  Google Scholar 

  24. Zhang, F., Carothers, J. M. & Keasling, J. D. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. 30, 354–359 (2012).

    Article  CAS  Google Scholar 

  25. Moore, G. E. Progress in digital integrated electronics. In International Electron Devices Meeting, IEEE 11–13 (1975).

Download references

Acknowledgements

We thank W. Lim, O. Weiner, and M. Lajoie for helpful discussions and P. Harrigan for assistance with hardware. This work was supported by the Defense Advanced Research Projects Agency, Contract No. HR0011-16-2-0045 to H.E.-S. The content and information does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. H.E.-S. is a Chan-Zuckerberg investigator. A.H.N. was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. R.A.L. was supported by Bruce and Jeannie Nordstrom, thanks to the Patty and Jimmy Barrier Gift for the Institute for Protein Design Directors Fund. S.E.B. is supported by a Career Award at the Scientific Interface from Burroughs Wellcome Fund.

Author information

Authors and Affiliations

  1. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Andrew H. Ng, Taylor H. Nguyen, Mariana Gómez-Schiavon, Galen Dods & Hana El-Samad

  2. Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

    Andrew H. Ng, Jennifer A. Samson, Lucas M. Waldburger & John E. Dueber

  3. The UC Berkeley–UCSF Graduate Program in Bioengineering, UC Berkeley, Berkeley, CA, USA

    Andrew H. Ng

  4. Cell Design Initiative, University of California, San Francisco, CA, USA

    Andrew H. Ng & Hana El-Samad

  5. Department of Biochemistry, University of Washington, Seattle, WA, USA

    Robert A. Langan, Scott E. Boyken & David Baker

  6. Institute for Protein Design, University of Washington, Seattle, WA, USA

    Robert A. Langan, Scott E. Boyken & David Baker

  7. Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, WA, USA

    Robert A. Langan

  8. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    David Baker

  9. Chan–Zuckerberg Biohub, San Francisco, CA, USA

    Hana El-Samad

Authors
  1. Andrew H. Ng
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  4. Galen Dods
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Contributions

A.H.N. and H.E.-S. conceived the study and designed all experiments. T.H.N. performed dynamic experiments. A.H.N. performed all other experiments. M.G.-S., A.H.N. and H.E.-S. conceived the computational model. M.G.-S. performed all numerical simulations. G.D. contributed to plasmid and strain construction. R.A.L., S.E.B. and D.B. designed degronLOCKR and gave feedback throughout. J.A.S., L.M.W. and J.E.D. contributed to the construction of mating-pathway strains. A.H.N. and H.E.-S. wrote the manuscript and all authors edited and approved the manuscript.

Corresponding author

Correspondence to Hana El-Samad.

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Competing interests

R.A.L., A.H.N., S.E.B., D.B. and H.E.-S. have filed a provisional patent application describing the design, composition and function of degronLOCKR constructs. R.A.L., A.H.N., S.E.B., D.B. and H.E.-S. have filed a provisional patent application describing the degronLOCKR-based molecular feedback circuits. D.B. and S.E.B. hold equity in Lyell Immunopharma.

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Extended data figures and tables

Extended Data Fig. 1 Panel of mating-pathway regulators tested with degronLOCKR.

DegronSwitch was fused to the C terminus of the endogenous copy of each regulator. The key with or without a SV40 NLS was expressed using a progesterone (Pg)-inducible system8. Ste20, Ste11 and Ptp3 were degraded using a cytoplasmic key (key–CFP), and Msg5, Ste12, Dig1 and Dig2 were degraded using a nuclear key (key–CFP–NLS). Fus3 was degraded using either a cytoplasmic or a nuclear key. Cells were induced with 1 nM (low) or 100 nM (high) α-factor and 50 nM or 0 nM progesterone, and grown for 4 h before YFP fluorescence was measured using a flow cytometer. Data represent mean ± s.d. of three biological replicates.

Extended Data Fig. 2 Dynamic measurements of the mating pathway with synthetic feedback.

DegronSwitch was fused to the C terminus of the endogenous copy of Ste12 (left) and Fus3 (right). Measurements of pAGA1-YFP-cODC dynamics for synthetic negative (left) and positive (right) feedback. Synthetic feedback and no-feedback (pREV1-key-CFP-NLS) strains were induced with 25, 12.5, 6.25 or 3.13 nM α-factor at 0 h, and flow cytometry measurements (points) were performed every 10 min. Points represent the mean ± s.d. of three biological replicates. Lines represent a moving average taken over three data points.

Extended Data Fig. 3 Model analysis of synthetic transcriptional circuit with degronLOCKR feedback to probe steady-state solutions in response to positive or negative disturbances.

a, b, Steady-state values of model variables as progesterone (a) or Z3PM degradation rate (γZ) (b) change (see ‘Model description’ in Supplementary Information). Continuous lines correspond to the feedback system (FB), and the dashed lines show an example in which the feedback has been removed (no FB; that is, fK = μK* instead of equation (12) in Supplementary Information). The grey box delimits the area in which the feedback is considered to be ‘active’. This is defined by the relative change in total GEM (Δ(G + C)/(G + C)) over the relative change of the disturbance (ΔP/P for a or ΔγZ/γZ for b) being higher than 0.15. In the absence of feedback, Δ(G + C) is equal to zero for any disturbance, unless the disturbance directly affects the synthesis or degradation rate of the key or GEM. The parameters used, and definitions of variables and species, are provided in the Supplementary Information.

Extended Data Fig. 4 Experimental behaviour of synthetic transcriptional circuit with degronLOCKR feedback as a function of progesterone for a fixed dose of oestradiol.

a, Comparison of behaviour of the steady-state circuit (ten hours after stimulation) with and without feedback (pRNR2-key-CFP-NLS) as a function of progesterone at all concentrations of oestradiol (E2). YFP fluorescence is the output of the circuit and RFP fluorescence is a proxy for Z3PM concentration. Points represent mean ± s.d. of three biological replicates. b, Example of gating strategy used to generate flow cytometry data. The cells that were used are in Q2.

Extended Data Fig. 5 Experimental behaviour of synthetic transcriptional circuit with degronLOCKR feedback as a function of oestradiol for a fixed dose of progesterone.

Comparison of the behaviour of the steady-state circuit (ten hours after stimulation) with and without feedback (pRNR2-key-CFP-NLS) as a function of oestradiol at all concentrations of progesterone. YFP fluorescence is the output of the circuit and RFP fluorescence is a proxy for Z3PM concentration. Points represent mean ± s.d. of three biological replicates.

Extended Data Fig. 6 Behaviour of synthetic transcriptional circuit with degronLOCKR feedback cannot be matched by constitutively expressing different amounts of the key.

Comparison of the behaviour of the steady-state circuit (ten hours after stimulation) with feedback, and various levels of expression of the key without feedback (REV1, RNR2, RET2 and RPL18B promoters), as a function of progesterone at a fixed concentration of 7.5 nM oestradiol. YFP fluorescence is the output of the circuit and RFP fluorescence is a proxy for Z3PM concentration. The yellow line in the top panel is the YFP output for the feedback circuit, and the red line in the bottom panel is the RFP output for the feedback circuit. Points represent mean ± s.d. of three biological replicates. Data points are connected by lines to guide the eye.

Extended Data Fig. 7 Model analysis of synthetic transcriptional circuit with degronLOCKR feedback to probe the effect of changing η+ or μK on steady state solution & feedback activity.

a, b, Steady values as progesterone changes according to the model (see ‘Model description’ in Supplementary Information). Continuous lines correspond to model simulation of the feedback system, and the dashed line shows an example in which the feedback has been removed (fK = μK* instead of equation (12) in Supplementary Information). The effect of decreasing (×0.01) or increasing (×100) η+ (a) or μK (b) are shown in orange or purple, respectively. The shadow boxes delimit the area in which the feedback is considered to be active ((Δ(G + C)/(G + C))/(ΔP/P) > 0.15) for each case. Grey, original parameter set; orange, decreasing η+ (a) or μK (b); purple, increasing η+ (a) or μK (b). c, d, Range of feedback activity for progesterone disturbances, and how this changes as η+ (c) or μK (d) varies. See colour bar on the right for precision metric. The colours of the vertical lines in c and d correspond to the cases shown in a and b, respectively.

Extended Data Fig. 8 Changing the promoter strength or length of the key in experiments modulates the steady-state properties for the synthetic transcriptional circuit with degronLOCKR feedback.

Comparison of the behaviour of the steady-state circuit (ten hours after stimulation) for various levels of feedback as a function of progesterone, at a fixed concentration of 7.5 nM oestradiol. Left, tuning via changing the strength of the feedback promoter. x, number of Z3 binding sites. Right, tuning via changing the length of the key. m, number of residues removed from the C- terminus of the key. YFP fluorescence (top panels)is the output of the circuit, RFP fluorescence (bottom panels) is a proxy for Z3PM concentration. Points represent mean ± s.d. of three biological replicates.

Extended Data Fig. 9 Changing the length of the key in experiments tunes feedback strength and changes the dynamic behaviour of the output of the synthetic transcriptional circuit with degronLOCKR feedback.

Dynamic measurements of pZ3-Venus-cODC using automated flow cytometry for the synthetic-feedback strain with various combinations of feedback promoter strength and key length and no-feedback strain (pREV1-key-CFP-NLS), after induction with 3.13 nM progesterone and 7.5 nM oestradiol at 0 h. The lines represent a moving average taken over three data points and the points represent the mean ± s.d. of three biological replicates.

Extended Data Fig. 10 Combinatorial tuning of synthetic negative feedback in the mating pathway.

Top, dynamic measurements of pAGA1-YFP-cODC output for various feedback and no-feedback (REV1, RNR2, RET2 and RPL18B promoter) strains with Ste12 fused to degronSwitch, after stimulation with 25 nM α-factor. The lines represent a moving average taken over three flow cytometry data points and the points represent the mean ± s.d. of three biological replicates. Bottom, α-factor dose–response of feedback strains versus no-feedback (REV1, RNR2, RET2 and RPL18B promoter) strains. YFP fluorescence was measured using flow cytometry four hours after induction with α-factor. Points represent mean ± s.d. of three biological replicates. Lines are a Hill function fit to the data.

Supplementary information

Supplementary Information

Sections 1 and 2 containing the Model Description and Qualitative Properties, and a table showing the simulation parameter values in the Main and Extended Data Figures.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7 containing information about strains, plasmids, oligos, hill function fits, information about the automated flow cytometry device, and p-values.

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Ng, A.H., Nguyen, T.H., Gómez-Schiavon, M. et al. Modular and tunable biological feedback control using a de novo protein switch. Nature 572, 265–269 (2019). https://doi.org/10.1038/s41586-019-1425-7

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