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SE(3)-equivariant ternary complex prediction towards target protein degradation
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SE(3)-equivariant ternary complex prediction towards target protein degradation
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Journal Article
SE(3)-equivariant ternary complex prediction towards target protein degradation
2025
Overview
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.
This work introduces DeepTernary, a deep learning method for rapid and accurate prediction of PROTAC and molecular glue-induced ternary complex structures, achieving state-of-the-art results by learning from a curated dataset, TernaryDB.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
