SE(3)-equivariant ternary complex prediction towards target protein degradation

Fanglei Xue, Meihan Zhang, Shuqi Li, Xinyu Gao, James A. Wohlschlegel, Wenbing Huang (), Yi Yang () and Weixian Deng ()
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Fanglei Xue: University of Technology Sydney
Meihan Zhang: Nankai University
Shuqi Li: Renmin University of China
Xinyu Gao: University of Chinese Academy of Sciences
James A. Wohlschlegel: University of California, Los Angeles
Wenbing Huang: Renmin University of China
Yi Yang: Zhejiang University
Weixian Deng: University of California, Los Angeles

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Targeted protein degradation (TPD) has rapidly emerged as a powerful modality for drugging previously “undruggable” proteins. TPD employs small molecules like PROTACs and molecular glue degraders (MGD) to induce target protein degradation via the formation of a ternary complex with an E3 ligase. However, the rational design of these degraders is severely hindered by the difficulty of obtaining these ternary structures. Here we introduce DeepTernary, a novel end-to-end deep learning approach using an SE(3)-equivariant encoder and a query-based decoder to accurately and rapidly predict these critical structures. Trained on carefully curated TernaryDB, DeepTernary achieves state-of-the-art performance on PROTAC benchmarks without prior exposure to known PROTACs and shows notable prediction capability on the more challenging MGD benchmark with a blind docking protocol. Remarkably, the buried surface areas calculated from predicted structures correlate with experimental degradation potency metrics. Overall, DeepTernary offers a powerful tool for the development of targeted protein degraders.

Date: 2025
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DOI: 10.1038/s41467-025-61272-5

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