ArticleNature methods2021
Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers.
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122 citing papers in PubMed.
- Electrostatic Gaussian Transistor for Real-Time Probabilistic Inference.Advanced materials (Deerfield Beach, Fla.) · 2026Article
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- Comparing multislice projections of MD simulations with cryo-EM exposes structural prediction errors.Biophysical journal · 2026Article
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Cryo-EM analysis of cooperative conformational changes in the SARS-CoV-2 spike protein trimer.IUCrJ · 2026Article
- Digital twin of biomacromolecular thermodynamics in cryo-EM data.Communications chemistry · 2026Article
- Structural dynamics of kappa opioid receptor interactions with β-arrestin 1.Nature communications · 2026Article
- Article
- Methods to Study the Molecular Mechanism and Drive the Design of Protein Degraders.Chemical reviews · 2026Review
- Heterogeneous reconstruction algorithms for cryoEM achieve limited particle classification accuracy on real benchmark datasets.bioRxiv : the preprint server for biology · 2026Article
- Engineered Cas9 complexes establish an experimentally grounded benchmark for heterogeneous cryoEM reconstruction methods.bioRxiv : the preprint server for biology · 2026Article
- Statistical crystallography reveals an allosteric network in SARS-CoV-2 MCommunications biology · 2026Article
- Cryogenic electron tomography reveals herpesvirus capsid assembly intermediates inside the cell nucleus.Nature communications · 2026Article
- Mechanism of Gating and Isoform-Specific Inhibition in Renal CLC Chloride Channels.bioRxiv : the preprint server for biology · 2026Article
- Comparing Multislice Projections of MD Simulations with CryoEM Exposes Structural Prediction Errors.bioRxiv : the preprint server for biology · 2026Article
- Point cloud deformation modeling for particle selection following cryo-EM 2D classification.BMC bioinformatics · 2026Article
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Article
- Fine-tuning AlphaFold with limited cryo-EM observations.Communications chemistry · 2026Article
- Structure of HIV-1 Env glycoprotein on virions reveals an alternative fusion subunit organization and native membrane coupling.bioRxiv : the preprint server for biology · 2026Article
62 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a three-dimensional Gaussian mixture model mapped onto two-dimensional particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data and three biological systems to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.
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