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.

本文介绍了一种新的E(3)-equivariant图神经网络(EGR)，旨在用于多任务结构细化和评估蛋白质复合物的效果最优，证实其在蛋白质复合物原子精细化和评估方面的效果最好，并针对未来研究进行了简要展示和总结，为大分子细化和结构分析领域建立了一个基准。

EGR: 三维蛋白质复合物结构的等变图形细化和评估