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<p>E3 ubiquitin ligases are vital enzymes that define the ubiquitin code in cells. Beyond promoting protein degradation to maintain cellular health, they also mediate non-degradative processes like DNA repair, signaling, and immunity. Despite their therapeutic potential, a comprehensive framework for understanding the relationships among diverse E3 ligases is lacking. Here, we classify the “human E3 ligome”—an extensive set of catalytic human E3s—by integrating multi-layered data, including protein sequences, domain architectures, 3D structures, functions, and expression patterns. Our classification is based on a metric-learning paradigm and uses a weakly supervised hierarchical framework to capture authentic relationships across E3 families and subfamilies. It extends the categorization of E3s into RING, HECT, and RBR classes, including non-canonical mechanisms, successfully explains their functional segregation, distinguishes between multi-subunit complexes and standalone enzymes, and maps E3s to substrates and potential drug interactions. Our analysis provides a global view of E3 biology, opening strategies for drugging E3-substrate networks, including drug repurposing and designing specific E3 handles.</p>

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Multi-scale classification decodes the complexity of the human E3 ligome

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月25日

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  • Arghya Dutta,
  • Alberto Cristiani,
  • Siddhanta V. Nikte,
  • Jonathan Eisert,
  • Yves Matthess,
  • Borna Markusic,
  • Cosmin Tudose,
  • Chiara Becht,
  • Ronay Cetin,
  • Varun Jayeshkumar Shah,
  • Thorsten Mosler,
  • Koraljka Husnjak,
  • Ivan Dikic,
  • Manuel Kaulich,
  • Ramachandra M. Bhaskara

摘要

E3 ubiquitin ligases are vital enzymes that define the ubiquitin code in cells. Beyond promoting protein degradation to maintain cellular health, they also mediate non-degradative processes like DNA repair, signaling, and immunity. Despite their therapeutic potential, a comprehensive framework for understanding the relationships among diverse E3 ligases is lacking. Here, we classify the “human E3 ligome”—an extensive set of catalytic human E3s—by integrating multi-layered data, including protein sequences, domain architectures, 3D structures, functions, and expression patterns. Our classification is based on a metric-learning paradigm and uses a weakly supervised hierarchical framework to capture authentic relationships across E3 families and subfamilies. It extends the categorization of E3s into RING, HECT, and RBR classes, including non-canonical mechanisms, successfully explains their functional segregation, distinguishes between multi-subunit complexes and standalone enzymes, and maps E3s to substrates and potential drug interactions. Our analysis provides a global view of E3 biology, opening strategies for drugging E3-substrate networks, including drug repurposing and designing specific E3 handles.