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Protein complexes are macromolecules essential to the functioning andwell-being of all living organisms. As the structure of a protein complex, inparticular its region of interaction between multiple protein subunits (i.e.,chains), has a notable influence on the biological function of the complex,computational methods that can quickly and effectively be used to refine andassess the quality of a protein complex's 3D structure can directly be usedwithin a drug discovery pipeline to accelerate the development of newtherapeutics and improve the efficacy of future vaccines. In this work, weintroduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graphneural network (GNN) for multi-task structure refinement and assessment ofprotein complexes. Our experiments on new, diverse protein complex datasets,all of which we make publicly available in this work, demonstrate thestate-of-the-art effectiveness of EGR for atomistic refinement and assessmentof protein complexes and outline directions for future work in the field. Indoing so, we establish a baseline for future studies in macromolecularrefinement and structure analysis.

EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures