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

  • Nat Commun. 2025 Jul 1;16(1):5514. doi: 10.1038/s41467-025-61272-5.
Fanglei Xue  1 ,  Meihan Zhang  2 ,  Shuqi Li  3 ,  Xinyu Gao  4 ,  James A Wohlschlegel  5 ,  Wenbing Huang  6  7 ,  Yi Yang  8 ,  Weixian Deng  9
Affiliations
  • 1. ReLER Lab, AAII, University of Technology Sydney, Sydney, NSW, 2007, Australia.
  • 2. College of Life Sciences, Nankai University, Tianjin, China.
  • 3. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China.
  • 4. University of Chinese Academy of Sciences, Beijing, China.
  • 5. Department of Biological Chemistry at David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • 6. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China. [email protected].
  • 7. Beijing Key Laboratory of Research on Large Models and Intelligent Governance, Beijing, China. [email protected].
  • 8. ReLER Lab, CCAI, Zhejiang University, Hangzhou, China. [email protected].
  • 9. Department of Biological Chemistry at David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. [email protected].
PMID: 40593782 DOI: 10.1038/s41467-025-61272-5
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.