Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery

Cryo-EM is a transformational paradigm in molecular biology where computa-1 tional methods are used to infer 3D molecular structure at atomic resolution from2 extremely noisy 2D electron microscope images. At the forefront of research is3 how to model the structure when the imaged particles exhibit non-rigid conforma-4 tional flexibility and compositional variation where parts are sometimes missing.5 We introduce a novel 3D reconstruction framework with a hierarchical Gaussian6 mixture model, inspired in part by Gaussian Splatting for 4D scene reconstruction.7 In particular, the structure of the model is grounded in an initial process that infers8 a part-based segmentation of the particle, providing essential inductive bias in9 order to handle both conformational and compositional variability. The framework,10 called CryoSPIRE, is shown to reveal biologically meaningful structures on com-11 plex experimental datasets, and establishes a new state-of-the-art on CryoBench, a12 benchmark for cryo-EM heterogeneity methods.