ArticleNature communications2021
Improved protein structure refinement guided by deep learning based accuracy estimation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
- A Step-by-Step Protocol for Efficient Global Accuracy Estimation of Protein Complex Structural Models with MViewEMA.Bio-protocol · 2026Article
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- SAKE-PP: A Spatial-Attention Equivariant Network for Accurate Ranking of Protein-Protein Interaction Models.JACS Au · 2026Article
- Time-resolved x-ray solution scattering from detergent solubilized visual rhodopsin.Biophysical journal · 2026Article
- Phosphorylation site topology governs the functional dynamics of arrestin recruitment to GPCRs.Research square · 2026Article
- Efficient global accuracy estimation for protein complex structural models using multi-view representation learning.Cell reports methods · 2026Article
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- Highlights of Model Quality Assessment in CASP16.Proteins · 2026Article
- SpheronizaTor: Spherical Voxelization for Interpretable Protein Microenvironment Modeling.Computational and structural biotechnology journal · 2026Article
- A Comprehensive Review on Homology Modelling in Drug Development: Methodologies, Tools, and Evaluation.Mini reviews in medicinal chemistry · 2026Review
- Modeling protein-small molecule conformational ensembles with PLACER.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Tuning insulin receptor signaling using de novo-designed agonists.Molecular cell · 2025Article
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- Modeling protein-small molecule conformational ensembles with PLACER.bioRxiv : the preprint server for biology · 2025Article
- Gephyrin filaments represent the molecular basis of inhibitory postsynaptic densities.Nature communications · 2025Article
- Enhanced Methodology for Peptide Tertiary Structure Prediction Using GRSA and Bio-Inspired Algorithm.International journal of molecular sciences · 2025Article
- De Novo Design of Integrin α5β1 Modulating Proteins to Enhance Biomaterial Properties.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- DeepUMQA-X: Comprehensive and insightful estimation of model accuracy for protein single-chain and complex.Nucleic acids research · 2025Article
- Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.Briefings in bioinformatics · 2025Review
60 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.
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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.