Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid
  Interface Prediction
Ziyang Yu Wenbing Huang Yang Liu
The study of rigid protein-protein docking plays an essential role in a
variety of tasks such as drug design and protein engineering. Recently, several
learning-based methods have been proposed for the task, exhibiting much faster
docking speed than those computational methods. In this paper, we propose a
novel learning-based method called ElliDock, which predicts an elliptic
paraboloid to represent the protein-protein docking interface. To be specific,
our model estimates elliptic paraboloid interfaces for the two input proteins
respectively, and obtains the roto-translation transformation for docking by
making two interfaces coincide. By its design, ElliDock is independently
equivariant with respect to arbitrary rotations/translations of the proteins,
which is an indispensable property to ensure the generalization of the docking
process. Experimental evaluations show that ElliDock achieves the fastest
inference time among all compared methods and is strongly competitive with
current state-of-the-art learning-based models such as DiffDock-PP and Multimer
particularly for antibody-antigen docking.