Cryo-EM Structure Reconstruction by Gaussian Splatting: Pushing the Resolution to Extreme

In the field of structural biology, Cryo-EM based high-resolution 3-D structure reconstruction of complex macromolecules is a vital step. Although multiple attempts have been tried within this framework to consider quality-degrading factors such as imaging noise, non-uniform distribution of particle orientations, and sample heterogeneity in order to achieve high resolution, there is still a substantial gap between the best reconstruction resolution achieved by the existing methods and the hard resolution provided by the imaging device. Here, we introduce CryoGS, a novel 3-D reconstruction method for Cryo-EM structures using Gaussian splatting. Through the integration of 3-D Gaussian representations into neural network learning, CryoGS employs a spatial domain approach to optimize learnable 3-D Gaussians and project them into 2-D images using the splatting technique. Compared with the existing methods, CryoGS achieves significant improvements in resolution, isotropy, and computational efficiency. For example, CryoGS achieves a resolution of 2.217Å on EMPIAR-10492 dataset, approaching its theoretical limit of 2.2Å, while the best resolution achieved by the existing methods is 3.805Å. Furthermore, CryoGS exhibits remarkable robustness in reconstructing heterogeneous structures and high-resolution models under extreme conditions such as pose inaccuracy, limited particle data, and high noise. Based on these results, we believe that CryoGS has great potential to be a powerful tool for Cryo-EM applications to ensure enhanced resolution, robustness, and efficiency.