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

TL;DR

This paper introduces a graph neural network method that automates protein model building in cryo-EM maps, significantly reducing manual effort and outperforming existing techniques at high resolutions.

Contribution

The paper presents a novel GNN-based approach that integrates cryo-EM data, amino acid sequences, and protein geometry to automate and improve model building.

Findings

Outperforms state-of-the-art methods on test cases.

Approximates manual building for cryo-EM maps below 3.5 Å resolution.

Effective in automating protein structure modeling from cryo-EM data.

Abstract

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}.