Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges representing the protein chain. Combining information from the voxel-based cryo-EM data, the amino acid sequence data and prior knowledge about protein geometries, the GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. Application to 28 test cases shows that our approach outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 \r{A}.

翻译：冷冻电镜技术能够生成生物大分子（包括蛋白质）静电势的三维图谱。结合成像分子的已知信息，冷冻电镜图谱可支持从头原子建模，但这一过程通常依赖繁琐的人工操作。受机器学习在蛋白质结构预测领域最新进展的启发，我们提出了一种基于图神经网络的冷冻电镜图谱蛋白质自动化模型构建方法。该图神经网络作用于一个以单个氨基酸为节点、蛋白质链为边的图结构上。通过融合基于体素的冷冻电镜数据、氨基酸序列信息以及蛋白质几何结构的先验知识，该网络能够优化蛋白质链的几何构型，并对每个节点对应的氨基酸进行分类。在28个测试案例上的应用表明，我们的方法优于现有技术水平，且在分辨率优于3.5 Å的冷冻电镜图谱上接近人工建模的效果。