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<p>Liquiritigenin is a medicinal flavonoid whose production is constrained by inefficient plant extraction and complex chemical synthesis. To overcome this, we developed a modular cell-free multi-enzyme system for its efficient biosynthesis from tyrosine, integrating spatial enzyme assembly with machine learning-guided optimization. Using a combined cell-free metabolic engineering (CFME) and cell-free protein synthesis-driven metabolic engineering (CFPS-ME) approach, we screened and optimized five key pathway enzymes to establish a one-pot reaction. The optimal enzyme combination (phenylalanine ammonia-lyase from <i>Zea mays</i>, 4-coumarate-coenzyme A ligase 4 from <i>Arabidopsis thaliana</i>, chalcone synthase from <i>Glycine max</i>, chalcone reductase from <i>Medicago sativa</i>, chalcone flavonone isomerase from <i>Zea mays</i>) was identified through systematic screening and ratio optimization. After Plackett–Burman and steepest-ascent experiments, three rounds of iterative machine learning fine-tuned key parameters, including enzyme ratios and cofactor concentrations, yielding 155.32 ± 14.39&#xa0;mg/L. Spatial enzyme assembly was further enhanced via covalent peptide tags and scaffold proteins (γPFD-SpyCatcher) under CFME. Combining CFPS-ME with scaffold-assisted co-immobilization significantly boosted production, reaching a final titer of 439.42 ± 19.53&#xa0;mg/L. This study demonstrates that machine learning-driven optimization and spatial assembly of multienzyme complexes is a powerful approach for cell-free biosynthesis.</p>

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Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Fei Liu,
  • Si-Bo Zhao,
  • Yan-Hua Liu,
  • Jun-Feng Li,
  • Nuo-Qiao Lin,
  • Meihereayi Mutailifu,
  • Pei Xu,
  • Jian-Zhong Liu

摘要

Liquiritigenin is a medicinal flavonoid whose production is constrained by inefficient plant extraction and complex chemical synthesis. To overcome this, we developed a modular cell-free multi-enzyme system for its efficient biosynthesis from tyrosine, integrating spatial enzyme assembly with machine learning-guided optimization. Using a combined cell-free metabolic engineering (CFME) and cell-free protein synthesis-driven metabolic engineering (CFPS-ME) approach, we screened and optimized five key pathway enzymes to establish a one-pot reaction. The optimal enzyme combination (phenylalanine ammonia-lyase from Zea mays, 4-coumarate-coenzyme A ligase 4 from Arabidopsis thaliana, chalcone synthase from Glycine max, chalcone reductase from Medicago sativa, chalcone flavonone isomerase from Zea mays) was identified through systematic screening and ratio optimization. After Plackett–Burman and steepest-ascent experiments, three rounds of iterative machine learning fine-tuned key parameters, including enzyme ratios and cofactor concentrations, yielding 155.32 ± 14.39 mg/L. Spatial enzyme assembly was further enhanced via covalent peptide tags and scaffold proteins (γPFD-SpyCatcher) under CFME. Combining CFPS-ME with scaffold-assisted co-immobilization significantly boosted production, reaching a final titer of 439.42 ± 19.53 mg/L. This study demonstrates that machine learning-driven optimization and spatial assembly of multienzyme complexes is a powerful approach for cell-free biosynthesis.