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Efficient molecular evolution to generate enantioselective enzymes using a dual-channel microfluidic droplet screening platform

Author

Listed:
  • Fuqiang Ma

    (Shanghai Jiao Tong University)

  • Meng Ting Chung

    (University of Michigan)

  • Yuan Yao

    (Harbin Institute of Technology)

  • Robert Nidetz

    (University of Michigan)

  • Lap Man Lee

    (University of Michigan)

  • Allen P. Liu

    (University of Michigan
    University of Michigan)

  • Yan Feng

    (Shanghai Jiao Tong University)

  • Katsuo Kurabayashi

    (University of Michigan
    University of Michigan)

  • Guang-Yu Yang

    (Shanghai Jiao Tong University)

Abstract

Directed evolution has long been a key strategy to generate enzymes with desired properties like high selectivity, but experimental barriers and analytical costs of screening enormous mutant libraries have limited such efforts. Here, we describe an ultrahigh-throughput dual-channel microfluidic droplet screening system that can be used to screen up to ~107 enzyme variants per day. As an example case, we use the system to engineer the enantioselectivity of an esterase to preferentially produce desired enantiomers of profens, an important class of anti-inflammatory drugs. Using two types of screening working modes over the course of five rounds of directed evolution, we identify (from among 5 million mutants) a variant with 700-fold improved enantioselectivity for the desired (S)-profens. We thus demonstrate that this screening platform can be used to rapidly generate enzymes with desired enzymatic properties like enantiospecificity, chemospecificity, and regiospecificity.

Suggested Citation

  • Fuqiang Ma & Meng Ting Chung & Yuan Yao & Robert Nidetz & Lap Man Lee & Allen P. Liu & Yan Feng & Katsuo Kurabayashi & Guang-Yu Yang, 2018. "Efficient molecular evolution to generate enantioselective enzymes using a dual-channel microfluidic droplet screening platform," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03492-6
    DOI: 10.1038/s41467-018-03492-6
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    Cited by:

    1. Allwin D. McDonald & Peyton M. Higgins & Andrew R. Buller, 2022. "Substrate multiplexed protein engineering facilitates promiscuous biocatalytic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Zhe Dou & Xuanzao Chen & Ledong Zhu & Xiangyu Zheng & Xiaoyu Chen & Jiayu Xue & Satomi Niwayama & Ye Ni & Guochao Xu, 2024. "Enhanced stereodivergent evolution of carboxylesterase for efficient kinetic resolution of near-symmetric esters through machine learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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