PROflow: An iterative refinement model for PROTAC-induced structure prediction
This work addresses a key bottleneck in PROTAC drug design for targeting undruggable proteins, offering a practical tool for researchers, though it is incremental in improving upon existing docking methods.
The paper tackled the challenge of predicting PROTAC-induced structures for rational drug design by developing PROflow, an iterative refinement model that outperformed state-of-the-art methods in docking metrics and runtime, enabling large-scale screening and showing significant correlations with degradation activities.
Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), existing PROTAC docking methods have been forced to simplify the problem into a distance-constrained protein-protein docking task. To address the data issue, we develop a novel pseudo-data generation scheme that requires only binary protein-protein complexes. This new dataset enables PROflow, an iterative refinement model for PROTAC-induced structure prediction that models the full PROTAC flexibility during constrained protein-protein docking. PROflow outperforms the state-of-the-art across docking metrics and runtime. Its inference speed enables the large-scale screening of PROTAC designs, and computed properties of predicted structures achieve statistically significant correlations with published degradation activities.