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

Jin Wang, Ning Xue, Wenjia Pan, Ran Tu, Shixin Li, Yue Zhang, Yufeng Mao, Ye Liu, Haijiao Cheng, Yanmei Guo, Wei Yuan, Xiaomeng Ni and Meng Wang ()
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Jin Wang: University of Chinese Academy of Sciences
Ning Xue: Chinese Academy of Sciences
Wenjia Pan: Chinese Academy of Sciences
Ran Tu: Chinese Academy of Sciences
Shixin Li: Chinese Academy of Sciences
Yue Zhang: Chinese Academy of Sciences
Yufeng Mao: Chinese Academy of Sciences
Ye Liu: Chinese Academy of Sciences
Haijiao Cheng: Chinese Academy of Sciences
Yanmei Guo: Chinese Academy of Sciences
Wei Yuan: University of Chinese Academy of Sciences
Xiaomeng Ni: Chinese Academy of Sciences
Meng Wang: University of Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42431-y

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