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<p>A systematic understanding of cellular metabolism is essential for engineering yeast and uncovering the principles of metabolic robustness and evolution, yet much of its metabolic space remains unexplored. Although yeast genome-scale metabolic models have been reconstructed and curated for over two decades, more than 90% of the yeast metabolome remains uncovered. Here, to address this gap, we have developed an integrated workflow that combines retrobiosynthesis, deep learning-based enzyme annotation and enzyme–substrate prediction to systematically explore yeast underground metabolism. Using the framework, we reconstruct a yeast metabolic twin model, Yeast-MetaTwin, comprising 16,244 metabolites, 1,976 metabolic genes and 59,865 reactions. The model reveals systematic differences in <i>K</i><sub>m</sub> distributions between the known and underground networks and identifies key hub metabolites linking the underground network. Moreover, Yeast-MetaTwin predicts by-product formation in yeast cell factories, and we experimentally validate two genes converting geraniol to geranial during geraniol biosynthesis.</p><p></p>

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Systematically exploring yeast metabolism through retrobiosynthesis and deep learning

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Ke Wu,
  • Haohao Liu,
  • Yao Zhou,
  • Manda Sun,
  • Runze Mao,
  • Yindi Jiang,
  • Eduard J. Kerkhoven,
  • Yu Chen,
  • Jens Nielsen,
  • Hongting Tang,
  • Feiran Li

摘要

A systematic understanding of cellular metabolism is essential for engineering yeast and uncovering the principles of metabolic robustness and evolution, yet much of its metabolic space remains unexplored. Although yeast genome-scale metabolic models have been reconstructed and curated for over two decades, more than 90% of the yeast metabolome remains uncovered. Here, to address this gap, we have developed an integrated workflow that combines retrobiosynthesis, deep learning-based enzyme annotation and enzyme–substrate prediction to systematically explore yeast underground metabolism. Using the framework, we reconstruct a yeast metabolic twin model, Yeast-MetaTwin, comprising 16,244 metabolites, 1,976 metabolic genes and 59,865 reactions. The model reveals systematic differences in Km distributions between the known and underground networks and identifies key hub metabolites linking the underground network. Moreover, Yeast-MetaTwin predicts by-product formation in yeast cell factories, and we experimentally validate two genes converting geraniol to geranial during geraniol biosynthesis.