High-Resolution Mapping of the Human E3-Substrate Interactome using Ubicon Uncovers Network Architecture and Cancer Vulnerabilities

Abstract

Mapping the intricate network of E3 ubiquitin ligase-substrate interactions (ESIs), essential for cellular regulation and implicated in numerous diseases including cancer, remains a central challenge limiting mechanistic understanding. Existing experimental and computational methods suffer from limitations in throughput, accuracy, and contextual relevance. Here, we introduce Ubicon, a deep learning framework designed to overcome these hurdles for accurate, proteome-wide human ESI prediction. Ubicon achieves high fidelity by synergistically integrating multimodal features: sequence embeddings from a protein language model specifically adapted for ESI prediction via parameter-efficient fine-tuning (PEFT), predicted 3D structures, and subcellular localization context. Validated extensively, Ubicon achieves state-of-the-art performance (AUROC = 0.9305, AUPRC = 0.6812), significantly surpassing previous approaches. Applying Ubicon, we constructed a high-resolution map of the human E3-substrate interactome, revealing its systems-level architecture characterized by hub proteins and functional modules linked to distinct biological processes. Integrating predictions with cancer genomics further uncovered disease-specific ESI network rewiring, involving oncogenic ligases like AURKA and CDC20, linked to poor prognosis. Ubicon provides a powerful platform to dissect ubiquitin signaling, uncover disease mechanisms, and inform targeted protein degrader development.