论文标题
EGR：3D蛋白复合物结构的模棱两可的细化和评估
EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
论文作者
论文摘要
蛋白质复合物是对所有生物体的功能和福祉必不可少的大分子。由于蛋白质复合物的结构，尤其是其在多个蛋白质亚基之间的相互作用区域（即链），对复杂的，计算方法的生物学功能具有显着影响，可以快速有效地用于完善和评估蛋白质复合物3D结构的质量，可以在药物发现渠道中直接使用，以加速新的饮食率，并改善了新的效率。在这项工作中，我们介绍了Equivariant Graph Refiner（EGR），这是一种新型的E（3） - 等级图神经网络（GNN），用于多任务结构的细化和蛋白质复合物的评估。我们对新的，多样化的蛋白质复杂数据集进行的实验，我们在这项工作中公开可用，证明了Egr对原子化的精致和评估蛋白质复合物的最新有效性，以及在该领域未来工作的蛋白质复合物以及轮廓方向。在此过程中，我们建立了一个基线，用于将来的大分子细化和结构分析研究。
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.
