Fig. 1: DeepTernary is a deep learning model for predicting the structure of the ternary complex induced by PROTACs and MG(D)s.

a The MOA of PROTACs and MGDs. The protein of interest (POI) and the E3 ligase are recruited to proximity by PROTACs or MGDs to form a ternary complex, and then the Ubiquitin-Proteasome System (UPS) is employed to transfer the ubiquitin and degrade the POI. b To mitigate the scarcity of known PROTACs and MG(D)s structures, a large-scale ternary complex dataset (named TernaryDB) was collected by searching and cleaning complexes from the Protein Data Bank (PDB) archive. The collected samples were then grouped into clusters by similarity. Any complex that is similar to known PROTAC and MG(D) induced complexes was excluded from the training set. DeepTernary was trained on this filtered database by predicting the original complex structure using disassembled monomers. c DeepTernary is an SE(3)-equivalent graph neural network equipped with attention blocks to facilitate efficient information exchange. It begins by representing two proteins and a small molecule as three graphs, encoding node coordinates, diverse amino acid or atom characteristics as node features, edge types, and distances as edge features. The three graphs are fed into an encoder consisting of a series of SE(3)-equivariant blocks, enabling both intra- and inter-graph learning to capture interactions effectively. The encoder will predict the conformation of the small molecule and output the refined node features/coordinates of the two proteins. Subsequently, a decoder comprising several attention-based blocks employs these refined features/coordinates to generate two pairs of pocket points and a predicted aligned error (PAE). The pocket points are then used to align both the small molecule and protein2 to protein1. * For PROTAC, the pocket points are derived from unbound structures, don’t need to be predicted. ** For MG(D), the ligand and protein2 are simultaneously aligned to protein1. Image created with BioRender. Xue, F. (2025) https://BioRender.com/o91e3ly, with permission.