ArticleNature structural & molecular biology2026
CryoAtom improves model building for cryo-EM.
Article in Nature structural & molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- A large-scale cryo-EM RNA motif dataset and benchmark for machine learning-based structure modeling.Computational biology and chemistry · 2026Article
- Cryo-EM structures of biopsy-derived TTR fibrils in hereditary transthyretin amyloidosis.Nature communications · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking deep learning methods for Cα atom prediction in cryo-EM density maps.Bioinformatics (Oxford, England) · 2026Article
- Article
- Cryo-EM structures of ALECT2 filaments from human renal biopsies.Nature communications · 2026Article
- Cryo-EM structure of a photosystem I supercomplex from Euglena gracilis containing pentameric LHCE and dimeric LHCII.Nature communications · 2026Article
- Improving the Reliability of Protein Folding Rate Predictions by Applying Guidelines for Validating QSAR/QSPR Models.International journal of molecular sciences · 2026Article
- When cryo-EM modeling meets structure prediction.Nature structural & molecular biology · 2026Article
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Authors and funding
5 authors.
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Abstract
Constructing atomic models from cryogenic electron microscopy (cryo-EM) density maps is essential for interpreting molecular mechanisms. Here we present CryoAtom, an approach for de novo model building for cryo-EM maps, leveraging recent advancements in AlphaFold2 to improve the state-of-the-art method ModelAngelo. To accommodate the cryo-EM map information, CryoAtom replaces the global attention mechanism in AlphaFold2 with local attention, which is further enhanced by a novel three-dimensional rotary position embedding. CryoAtom produces more complete models, reduces the resolution requirement and accelerates the modeling. The application of CryoAtom to three large maps demonstrates its ability to detect previously uncharacterized proteins with unknown functions, improve the modeling of conformational changes and compartmentalize the map to isolate nonprotein components. A particular case includes a 104-protein complex that was modeled within a few hours and a minor conformational change of a single protein domain was detected at the periphery when models from two different maps were compared. CryoAtom stands as an accurate method currently available for model building of proteins in cryo-EM structure determination. The source code and model parameters are available from GitHub ( https://github.com/YangLab-SDU/CryoAtom ).
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