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Repurposing conformational changes in ANL superfamily enzymes to rapidly generate biosensors for organic and amino acids

Author

Listed:
  • Jin Wang

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Haihe Laboratory of Synthetic Biology
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Ning Xue

    (Chinese Academy of Sciences
    Haihe Laboratory of Synthetic Biology
    Tianjin University of Science & Technology)

  • Wenjia Pan

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Ran Tu

    (Chinese Academy of Sciences
    College of Environmental and Resources, Chongqing Technology and Business University)

  • Shixin Li

    (Chinese Academy of Sciences
    Tianjin University of Science & Technology)

  • Yue Zhang

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Yufeng Mao

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Ye Liu

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Haijiao Cheng

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Yanmei Guo

    (Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Wei Yuan

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

  • Xiaomeng Ni

    (Chinese Academy of Sciences)

  • Meng Wang

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing)

Abstract

Biosensors are powerful tools for detecting, real-time imaging, and quantifying molecules, but rapidly constructing diverse genetically encoded biosensors remains challenging. Here, we report a method to rapidly convert enzymes into genetically encoded circularly permuted fluorescent protein-based indicators to detect organic acids (GECFINDER). ANL superfamily enzymes undergo hinge-mediated ligand-coupling domain movement during catalysis. We introduce a circularly permuted fluorescent protein into enzymes hinges, converting ligand-induced conformational changes into significant fluorescence signal changes. We obtain 11 GECFINDERs for detecting phenylalanine, glutamic acid and other acids. GECFINDER-Phe3 and GECFINDER-Glu can efficiently and accurately quantify target molecules in biological samples in vitro. This method simplifies amino acid quantification without requiring complex equipment, potentially serving as point-of-care testing tools for clinical applications in low-resource environments. We also develop a GECFINDER-enabled droplet-based microfluidic high-throughput screening method for obtaining high-yield industrial strains. Our method provides a foundation for using enzymes as untapped blueprint resources for biosensor design, creation, and application.

Suggested Citation

  • Jin Wang & Ning Xue & Wenjia Pan & Ran Tu & Shixin Li & Yue Zhang & Yufeng Mao & Ye Liu & Haijiao Cheng & Yanmei Guo & Wei Yuan & Xiaomeng Ni & Meng Wang, 2023. "Repurposing conformational changes in ANL superfamily enzymes to rapidly generate biosensors for organic and amino acids," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42431-y
    DOI: 10.1038/s41467-023-42431-y
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    References listed on IDEAS

    as
    1. Dana C. Nadler & Stacy-Anne Morgan & Avi Flamholz & Kaitlyn E. Kortright & David F. Savage, 2016. "Rapid construction of metabolite biosensors using domain-insertion profiling," Nature Communications, Nature, vol. 7(1), pages 1-11, November.
    2. Janice M. Reimer & Martin N. Aloise & Paul M. Harrison & T. Martin Schmeing, 2016. "Synthetic cycle of the initiation module of a formylating nonribosomal peptide synthetase," Nature, Nature, vol. 529(7585), pages 239-242, January.
    3. Yu Wang & Haijiao Cheng & Yang Liu & Ye Liu & Xiao Wen & Kun Zhang & Xiaomeng Ni & Ning Gao & Liwen Fan & Zhihui Zhang & Jiao Liu & Jiuzhou Chen & Lixian Wang & Yanmei Guo & Ping Zheng & Meng Wang & J, 2021. "In-situ generation of large numbers of genetic combinations for metabolic reprogramming via CRISPR-guided base editing," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
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