Geometric Deep Learning for Multiple Conformation Generation from Electron Density Images

In cryo-electron microscopy (cryo-EM) structural analysis, accurately predicting molecular conformations from image datasets is crucial. Despite advancements in deep learning, current solutions often produce suboptimal volume reconstructions that require additional processing for research use. To address this limitation, we introduce GraphConv, an innovative encoder model that embeds particle images into a latent space, replacing the conventional encoder used by CryoDRGN. Our approach employs a Graph Neural Network (GNN) architecture with multiple GraphConv and convolutional layers to capture richer information from particle images and accurately reconstruct 3D volumes. Rigorous testing on two authentic datasets and three simulated datasets demonstrates our model's efficacy, with improvements in reconstruction quality and resolution enhancements of up to 20 % compared to CryoDRGN. By leveraging the power of GNNs, our methodology significantly advances the fidelity and accuracy of cryo-EM volume reconstructions, contributing to the refinement of structural analysis techniques.