ArticleNature communications2024
De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A large-scale cryo-EM RNA motif dataset and benchmark for machine learning-based structure modeling.Computational biology and chemistry · 2026Article
- Computational Approaches for Protein-DNA/RNA Complex Modeling for Cryo-EM Maps.Current protocols · 2026Article
- Benchmarking deep learning methods for Cα atom prediction in cryo-EM density maps.Bioinformatics (Oxford, England) · 2026Article
- CryoAtom improves model building for cryo-EM.Nature structural & molecular biology · 2026Article
- EMProt improves structure determination from cryo-EM maps.Nature structural & molecular biology · 2026Article
- Advances in Crystallography and Cryo-Electron Microscopy for the Study of Membrane Proteins.Advances in experimental medicine and biology · 2026Review
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- Recognizing amino acid sidechains in a medium-resolution cryo-electron density map.Protein science : a publication of the Protein Society · 2025Article
- DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.Nucleic acids research · 2025Article
- A comprehensive survey and benchmark of deep learning-based methods for atomic model building from cryo-electron microscopy density maps.Briefings in bioinformatics · 2025Article
- GOBoost: leveraging long-tail gene ontology terms for accurate protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery.Bioengineering (Basel, Switzerland) · 2025Review
- A Labeled Dataset for AI-based Cryo-EM Map Enhancement.bioRxiv : the preprint server for biology · 2025Article
- CryoTEN: efficiently enhancing cryo-EM density maps using transformers.Bioinformatics (Oxford, England) · 2025Article
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- A labeled dataset for AI-based cryo-EM map enhancement.Computational and structural biotechnology journal · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.Briefings in bioinformatics · 2024Article
- CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers.bioRxiv : the preprint server for biology · 2024Article
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Abstract
Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.
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