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<p>Constructing high-quality genome-scale metabolic models (GEMs) for non-model organisms remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment, protein language models (PLMSearch), and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. AlphaGEM enhances homologous relationship identification compared to traditional sequence-based methods. Crucially, it employs an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine dark metabolic functions encoded by nonhomologous proteins, thereby expanding species-specific networks. We validated AlphaGEM across prokaryotes (<i>Klebsiella pneumoniae</i>, <i>Bacillus subtilis</i>), eukaryotes (<i>Rhodosporidium toruloides</i>, <i>Pichia pastoris</i>), and complex mammals (<i>Mus musculus</i>, <i>Cricetulus griseus</i>), achieving predictions comparable to manually curated models while outperforming existing tools. Furthermore, we demonstrated its scalability by automatically reconstructing high-fidelity GEMs for 332 distinct yeast species. In summary, AlphaGEM enables precise, rapid GEM construction across diverse domains, providing a solid foundation for universal functional analysis of organisms having genome sequences available.</p>

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AlphaGEM enables precise genome-scale metabolic modelling by integrating protein structure alignment with deep-learning-based dark metabolism mining

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Weishang Han,
  • Luchi Xiao,
  • Haocheng Sun,
  • Guangming Xiang,
  • Qianxi Jia,
  • Haoyu Wang,
  • Boyang Ji,
  • Cheng Zhang,
  • Eduard J. Kerkhoven,
  • Jens Nielsen,
  • Hongzhong Lu

摘要

Constructing high-quality genome-scale metabolic models (GEMs) for non-model organisms remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment, protein language models (PLMSearch), and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. AlphaGEM enhances homologous relationship identification compared to traditional sequence-based methods. Crucially, it employs an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine dark metabolic functions encoded by nonhomologous proteins, thereby expanding species-specific networks. We validated AlphaGEM across prokaryotes (Klebsiella pneumoniae, Bacillus subtilis), eukaryotes (Rhodosporidium toruloides, Pichia pastoris), and complex mammals (Mus musculus, Cricetulus griseus), achieving predictions comparable to manually curated models while outperforming existing tools. Furthermore, we demonstrated its scalability by automatically reconstructing high-fidelity GEMs for 332 distinct yeast species. In summary, AlphaGEM enables precise, rapid GEM construction across diverse domains, providing a solid foundation for universal functional analysis of organisms having genome sequences available.