ArticleNature methods2024
DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Article
- Computational Approaches for Protein-DNA/RNA Complex Modeling for Cryo-EM Maps.Current protocols · 2026Article
- Cryo-EM structure of soluble VPS13C suggests its regulation by a conformational switch and by calmodulin.Molecular cell · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- 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
- 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
- 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
- Fine-tuning AlphaFold with limited cryo-EM observations.Communications chemistry · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Structural insights into GM4951 as a lipid droplet GTPase regulating hepatic lipid metabolism.Nature communications · 2025Article
- Structural Insights and Functional Dynamics of β-Lactoglobulin Fibrils.Nano letters · 2025Article
- Insights into the regulation of VPS13 family bridge-like lipid transfer proteins from the structure of VPS13C.bioRxiv : the preprint server for biology · 2025Article
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- An Empirical Biasing Force Constant to Minimize Overfitting in Cryo-EM Flexible Fitting Refinement.Journal of chemical information and modeling · 2025Article
- Cryo-EM and cryo-ET reveal the molecular architecture and host interactions of mycobacteriophage Douge.Cell reports · 2025Article
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Abstract
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.
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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.