GraphConv: Geometric Deep Learning for Multiple Conformation Generation from Electron Density Images

Abstract

In the field of cryo-electron microscopy (cryo-EM) structural analysis, the precise prediction of molecular conformations within datasets is essential. Despite strides made in deep learning methodologies, existing solutions often yield volumes of suboptimal quality. Addressing this critical limitation, our research introduces GraphConv, an innovative encoder model designed to embed particle images into a latent space, thereby substituting the conventional encoder utilized by CryoDRGN. This novel approach employs a Graph Neural Network (GNN) architecture featuring multiple GraphConv and Convolutional layers, aimed at capturing richer information from particle images and precisely reconstructing corresponding 3D volumes. Rigorous testing across two authentic datasets and three simulated datasets underscores the efficacy of our model, suggesting marked enhancements in reconstruction quality. Specifically, our findings reveal enhancements in resolution by up to 20% compared to CryoDRGN. By harnessing the power of GNNs, our methodology shows promise for significant advancements in the fidelity and accuracy of output volumes, thereby contributing to the ongoing refinement of cryo-EM structural analysis methodologies.