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

Author

Listed:
  • Andrew H. Ng

    (University of California, San Francisco
    University of California, Berkeley
    The UC Berkeley–UCSF Graduate Program in Bioengineering, UC Berkeley
    Cell Design Initiative, University of California)

  • Taylor H. Nguyen

    (University of California, San Francisco)

  • Mariana Gómez-Schiavon

    (University of California, San Francisco)

  • Galen Dods

    (University of California, San Francisco)

  • Robert A. Langan

    (University of Washington
    University of Washington
    University of Washington)

  • Scott E. Boyken

    (University of Washington
    University of Washington)

  • Jennifer A. Samson

    (University of California, Berkeley)

  • Lucas M. Waldburger

    (University of California, Berkeley)

  • John E. Dueber

    (University of California, Berkeley)

  • David Baker

    (University of Washington
    University of Washington
    University of Washington)

  • Hana El-Samad

    (University of California, San Francisco
    Cell Design Initiative, University of California
    Chan–Zuckerberg Biohub)

Abstract

De novo-designed proteins1–3 hold great promise as building blocks for synthetic circuits, and can complement the use of engineered variants of natural proteins4–7. 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.

Suggested Citation

  • Andrew H. Ng & Taylor H. Nguyen & Mariana Gómez-Schiavon & Galen Dods & Robert A. Langan & Scott E. Boyken & Jennifer A. Samson & Lucas M. Waldburger & John E. Dueber & David Baker & Hana El-Samad, 2019. "Modular and tunable biological feedback control using a de novo protein switch," Nature, Nature, vol. 572(7768), pages 265-269, August.
  • Handle: RePEc:nat:nature:v:572:y:2019:i:7768:d:10.1038_s41586-019-1425-7
    DOI: 10.1038/s41586-019-1425-7
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    Cited by:

    1. Maurice Filo & Sant Kumar & Mustafa Khammash, 2022. "A hierarchy of biomolecular proportional-integral-derivative feedback controllers for robust perfect adaptation and dynamic performance," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Stanislav Anastassov & Maurice Filo & Ching-Hsiang Chang & Mustafa Khammash, 2023. "A cybergenetic framework for engineering intein-mediated integral feedback control systems," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Luna Rizik & Loai Danial & Mouna Habib & Ron Weiss & Ramez Daniel, 2022. "Synthetic neuromorphic computing in living cells," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    5. Yuanli Gao & Lei Wang & Baojun Wang, 2023. "Customizing cellular signal processing by synthetic multi-level regulatory circuits," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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