Integrating molecular models into CryoEM heterogeneity analysis using scalable high-resolution deep Gaussian mixture models

2022/11/18 by Muyuan Chen, Chen, Muyuan, Bogdan Toader +3 · 3 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Biomolecules (q-bio.BM) #Electron and X-Ray Spectroscopy Techniques #FOS: Biological sciences #Machine Learning in Materials Science #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.2211.10518

openalex publication_date 2022/11/18 · openalex created_date 2022/11/29 · openalex updated_date 2026/07/28

Abstract

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can resolve complex protein conformational changes at near atomic resolution and present the results in a more interpretable form.

Cited by

• CryoSplat: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction
• Efficient high-resolution refinement in cryo-EM with stochastic gradient descent
• Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery

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