ArticleNature methods2024
Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 48 citations in OpenAlex.
- Convergence of cryo-electron microscopy and artificial intelligence in integrative structural biology: A critical review of advances, synergies, and implications for molecular biophysics and drug discovery.Journal of microscopy · 2026Review
- From Static Structures to Molecular Dynamics: Emerging Directions in X-Ray and Electron Materials Characterization.Materials (Basel, Switzerland) · 2026Review
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- FlyTomo: a streamlined software for on-the-fly cryo-ET data processing and diagnosis.Nature communications · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 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
- Emerging roles of lipids in the flavivirus life cycle.Trends in microbiology · 2026Review
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Resolving structural heterogeneity in situ through cryogenic electron tomography.Current opinion in structural biology · 2026Review
- Biological Breakthroughs and Drug Discovery Revolution via Cryo-Electron Microscopy of Membrane Proteins.Membranes · 2025Review
- Strategies for studying discrete heterogeneity in situ using cryo-electron tomography.Current opinion in structural biology · 2025Review
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- High-throughput cryo-electron tomography enables multiscale visualization of the inner life of microbes.Current opinion in structural biology · 2025Review
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets.Nature methods · 2025Article
- Advancing time-resolved structural biology: latest strategies in cryo-EM and X-ray crystallography.Nature methods · 2025Review
- A new age in structural S-layer biology: Experimental and in silico milestones.The Journal of biological chemistry · 2025Review
- In-cell chromatin structure by Cryo-FIB and Cryo-ET.Current opinion in structural biology · 2025Review
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2 authors at 1 institution in 1 country.
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Abstract
Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.