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

TL;DR

This paper introduces EGR, a novel E(3)-equivariant graph neural network designed for the refinement and assessment of 3D protein complex structures, demonstrating state-of-the-art performance on diverse datasets.

Contribution

The paper presents EGR, the first E(3)-equivariant GNN for simultaneous refinement and assessment of protein complex structures, establishing a new benchmark in the field.

Findings

EGR achieves state-of-the-art results in atomistic refinement.

EGR effectively assesses protein complex structures.

New diverse datasets are publicly released.

Abstract

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.