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<p>E3 ubiquitin ligases recognize substrates through specific interfaces. Accurate delineation of these interfaces is essential, as mutations disrupting them impair protein ubiquitination and drive cancer progression. However, available E3–substrate interface data are sparse and systematic prediction methods remain lacking. Here, we propose MetaESI, a deep learning framework that simultaneously predicts E3–substrate interactions and leverages its interpretable architecture to infer binding interfaces de novo. With a two-stage meta-learning strategy, MetaESI generalizes across diverse E3s and achieves state-of-the-art performance in both interaction and interface prediction. We applied MetaESI at the proteome scale to generate MetaESI-Atlas, which comprises 68,056 annotated interactions across eight species. Integrating multi-omics data, we identified mutations at MetaESI-predicted interfaces that disrupt E3–substrate binding, and experimentally validated representative examples including JunB Q244E and SPOP F102C as oncogenic drivers. By combining interpretable AI with mechanistic insight, MetaESI establishes a methodological paradigm for interpretable model design and a foundational resource for precision oncology and targeted protein degradation.</p>

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Interpretable deep learning framework for mapping E3–substrate binding interfaces

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Dianke Li,
  • Yuting Zhang,
  • Yuan Liu,
  • Zihao Zhang,
  • Yingjie Qu,
  • Jiajun Li,
  • Linyang Jiang,
  • Lihong Diao,
  • Ziding Zhang,
  • Lingqiang Zhang,
  • Chun-Ping Cui,
  • Dong Li

摘要

E3 ubiquitin ligases recognize substrates through specific interfaces. Accurate delineation of these interfaces is essential, as mutations disrupting them impair protein ubiquitination and drive cancer progression. However, available E3–substrate interface data are sparse and systematic prediction methods remain lacking. Here, we propose MetaESI, a deep learning framework that simultaneously predicts E3–substrate interactions and leverages its interpretable architecture to infer binding interfaces de novo. With a two-stage meta-learning strategy, MetaESI generalizes across diverse E3s and achieves state-of-the-art performance in both interaction and interface prediction. We applied MetaESI at the proteome scale to generate MetaESI-Atlas, which comprises 68,056 annotated interactions across eight species. Integrating multi-omics data, we identified mutations at MetaESI-predicted interfaces that disrupt E3–substrate binding, and experimentally validated representative examples including JunB Q244E and SPOP F102C as oncogenic drivers. By combining interpretable AI with mechanistic insight, MetaESI establishes a methodological paradigm for interpretable model design and a foundational resource for precision oncology and targeted protein degradation.