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

A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps