ArticleNature methods2022
CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba.Nature communications · 2026Article
- A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.International journal of molecular sciences · 2026Article
- Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta.bioRxiv : the preprint server for biology · 2026Article
- When cryo-EM modeling meets structure prediction.Nature structural & molecular biology · 2026Article
- EMProt improves structure determination from cryo-EM maps.Nature structural & molecular biology · 2026Article
- CryoAtom improves model building for cryo-EM.Nature structural & molecular biology · 2026Article
- Impurities in amyloid studies: The power of automated model building within a cautionary tale for structural biologists.Protein science : a publication of the Protein Society · 2025Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
- AI-based methods for biomolecular structure modeling for Cryo-EM.Current opinion in structural biology · 2025Review
- Navigating the unstructured by evaluating alphafold's efficacy in predicting missing residues and structural disorder in proteins.PloS one · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- All-atom RNA structure determination from cryo-EM maps.Nature biotechnology · 2025Article
- Secondary Structure Detection and Structure Modeling for Cryo-EM.Methods in molecular biology (Clifton, N.J.) · 2025Article
- DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.Nature methods · 2024Article
- Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.Nature communications · 2024Article
- Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences.Nature communications · 2024Article
- Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER (cryo-EM iterative threading assembly refinement), which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (template modeling (TM) score >0.5) for 643 targets that is 64% higher than the best of some other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the main advantage of CR-I-TASSER lies in the deep learning-based Cα position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density map resolutions.
Indexed as
Identifiers
What OpenQuestion holds
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.