  Protein complexes are macromolecules essential to the functioning and
well-being of all living organisms. As the structure of a protein complex, in
particular 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 and
assess the quality of a protein complex's 3D structure can directly be used
within a drug discovery pipeline to accelerate the development of new
therapeutics and improve the efficacy of future vaccines. In this work, we
introduce the Equivariant Graph Refiner (EGR), a novel E(3)-equivariant graph
neural network (GNN) for multi-task structure refinement and assessment of
protein complexes. Our experiments on new, diverse protein complex datasets,
all of which we make publicly available in this work, demonstrate the
state-of-the-art effectiveness of EGR for atomistic refinement and assessment
of protein complexes and outline directions for future work in the field. In
doing so, we establish a baseline for future studies in macromolecular
refinement and structure analysis.
