Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery

Shayan Shekarforoush, David B. Lindell, Marcus A Brubaker, David J. Fleet

Keywords: Cryo-EM 3D Reconstruction, Gaussian Mixture Models, Part Discovery

TL;DR: We present a part-aware hierarchical GMM-based density model to tackle cryo-EM heterogeneous reconstruction.

Abstract:

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

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 25737