English

PROflow: An iterative refinement model for PROTAC-induced structure prediction

Biomolecules 2024-05-14 v1 Artificial Intelligence Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2405.06654,
  title  = {PROflow: An iterative refinement model for PROTAC-induced structure prediction},
  author = {Bo Qiang and Wenxian Shi and Yuxuan Song and Menghua Wu},
  journal= {arXiv preprint arXiv:2405.06654},
  year   = {2024}
}

Comments

Published at the GEM workshop, ICLR 2024

R2 v1 2026-06-28T16:23:32.155Z