Resolving compositional and conformational heterogeneity in cryo-EM with deformable 3D Gaussian representations
This addresses the problem of analyzing protein flexibility and dynamics in cryo-EM for structural biology, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the challenge of analyzing cryo-EM datasets with mixed structural states by introducing GaussianEM, a Gaussian-based framework that resolves compositional and conformational heterogeneity, successfully reconstructing variability and capturing broader conformational diversity in public datasets.
Understanding protein flexibility and its dynamic interactions with other molecules is essential for studying protein function. Although cryogenic electron microscopy(cryo-EM) provides an opportunity to observe macromolecular dynamics directly, computational analysis of datasets mixing continuous and discrete structural states remains a formidable challenge. Here we introduce GaussianEM, a Gaussian-based pseudo-atomic framework that simultaneously resolves compositional and conformational heterogeneity from cryo-EM images. GaussianEM employs a dual-encoder-single-decoder architecture to decompose images into learnable Gaussian components, with variability encoded through modulated parameters. This explicit parameterization yields a continuous, intuitive representation of conformational dynamics that inherently preserves local structural integrity. By modeling displacements in Gaussian space, we capture atomic-scale conformational landscapes, bridging density maps and all-atom models. In comprehensive experiments, GaussianEM successfully reconstructs complex compositional and conformational variability,and resolves previously unobserved details in public datasets. Quantitative evaluations further confirm its ability to capture broader conformational diversity without sacrificing structural fidelity.