ArticleNature methods2025
CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Article
- FlyTomo: a streamlined software for on-the-fly cryo-ET data processing and diagnosis.Nature communications · 2026Article
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- CryoROLE: describing large inter-domain rotation in single particle cryo-EM.bioRxiv : the preprint server for biology · 2026Article
- prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.Structure (London, England : 1993) · 2026Article
- Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis.Acta crystallographica. Section D, Structural biology · 2026Article
- A comprehensive foundation model for cryo-EM image processing.Nature methods · 2026Article
- Artificial intelligence foundation model automates cryo-EM structure determination.Nature methods · 2026Article
- Bayesian Perspective for Orientation Determination in Cryo-EM with Application to Structural Heterogeneity Analysis.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-electron tomography can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, three-dimensional reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-electron microscopy and cryo-electron tomography as a high-throughput tool for structural biology and discovery.
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