ReviewNature protocols2023
Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN.
Review in Nature protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
48 citing papers in PubMed, 72 citations in OpenAlex.
- De novo design of miniproteins targeting GPCRs.Nature · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- Article
- 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
- Structural plasticity of the membrane-bound protein degradation assembly supports bacterial adaptation to stress.Cell reports · 2026Article
- Capturing ribosomal structures in cellular extracts with cryoPRISM: A purification-free cryoEM approach reveals novel structural states.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Resolving structural heterogeneity in situ through cryogenic electron tomography.Current opinion in structural biology · 2026Review
- Measuring multisubunit mechanics of geometrically programmed colloidal assemblies via cryo-EM multi-body refinement.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cryo-EM captures early intermediate steps in dynein activation by LIS1.Nature communications · 2025Article
- Multiple steps of dynein activation by Lis1 visualized by cryo-EM.Nature structural & molecular biology · 2025Article
- Exploring the Intrinsic Structural Plasticity and Conformational Dynamics of Human Beta Coronavirus Spike Glycoproteins.Journal of chemical information and modeling · 2025Article
- Structural Plasticity of the Membrane-Bound Protein Degradation Assembly Supports Bacterial Adaptation to Stress.bioRxiv : the preprint server for biology · 2025Article
- CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets.Nature methods · 2025Article
- Article
- An asymmetric nautilus-like HflK/C assembly controls FtsH proteolysis of membrane proteins.The EMBO journal · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.Structure (London, England : 1993) · 2025Article
- Conformational landscapes of a class I ribonucleotide reductase complex during turnover reveal intrinsic dynamics and asymmetry.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single-particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find is effective in modeling both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for the analysis of an existing assembling 50S ribosome dataset, including preparation of inputs, network training and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single-particle cryo-EM datasets and with moderate experience navigating Python and Jupyter notebooks. It requires 3-4 days to complete. CryoDRGN is open source software that is freely available.
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Registered trials
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 OpenQuestion graph.