EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
This work addresses the need for computational methods to accelerate drug discovery and vaccine development by improving protein complex structure analysis, though it appears incremental as it builds on existing GNN approaches.
The authors tackled the problem of refining and assessing 3D protein complex structures by introducing EGR, an E(3)-equivariant graph neural network, which demonstrated state-of-the-art effectiveness on new, diverse datasets.
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.