Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

By Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause. 2021

174 citations recorded by the source corpus.

Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no conformational change within the proteins happens during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position relative to the second protein. We mathematically guarantee a basic principle: the predicted complex is always identical regardless of the initial locations and orientations of the two structures. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment, achieved through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements and often outperform existing docking software despite not relying on heavy candidate sampling, structure refinement, or templates.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “design Jin et al. [2021], Ingraham et al. [2019]. Among which, AlphaFold 2 achieved outstanding performance in protein structure prediction Jumper et al.”
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “Similar to (Ganea et al., 2021a), an important geometric induc-E tive bias is to predict the same binding complex no matter how the initial molecules are positioned and oriented in space.”
P HYSICS - INFORMED DEEP NEURAL NETWORK FOR RIGID - BODY PROTEIN DOCKING (2022, background): “Additionally, a pairwise-independent SE (3) -equivariant graph matching network, termed EquiDock, has been proposed for end-to-end rigid protein docking and shows promising results for deep learning applied to this problem Ganea et al. (2021).”
Riemannian Score-Based Generative Modeling (2022, background): “Applications of orthogonal constraints span various fields, such as protein docking with ligands binding pose prediction (Ganea et al., 2022), robotics and Computer vision with rigid body transformation estimation (Barfoot et al.”