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.

本研究提出了一种名为 ElliDock 的新型基于学习的方法，用于预测蛋白质 - 蛋白质对接界面，并在推断时间上显著快于其他方法，特别适用于抗体 - 抗原对接。