ArticleQRB discovery2022
Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps.
Article in QRB discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.International journal of molecular sciences · 2026Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Decoys Reveal Multiple Basins of Attraction for Cryo-Electron-Microscopy Flexible Fitting.Computational structural bioinformatics : international workshop, CSBW 2024, Boston, MA, USA, November 16, 2024, proceedings. Computational Structural Bioinformatics Workshop (2024 : Boston, Mass.) · 2025Article
- DeepSSETracer 2.0: Improved Deep Learning Model Performance for Protein Secondary Structure Segmentation from Cryo-EM Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.Bioinformatics advances · 2025Article
- AlphaFold2 Model Refinement Using Structure Decoys.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Refinement of AlphaFold2 Models against Experimental Cryo-EM Density Maps at 4-6Å Resolution.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing structure prediction method at CASP14) on cryo-EM refinement using the Phenix refinement suite for AlphaFold2 models. To study the robustness of model refinement at a lower resolution of interest, we introduced hybrid maps (i.e. experimental cryo-EM maps) filtered to lower resolutions by real-space convolution. The AlphaFold2 models were refined to attain good accuracies above 0.8 TM scores for 9 of the 13 cryo-EM maps. TM scores improved for AlphaFold2 models refined against all 13 cryo-EM maps of better than 4.5 Å resolution, 8 hybrid maps of 6 Å resolution, and 3 hybrid maps of 8 Å resolution. The results show that it is possible (at least with the Phenix protocol) to extend the refinement success below 4.5 Å resolution. We even found isolated cases in which resolution lowering was slightly beneficial for refinement, suggesting that high-resolution cryo-EM maps might sometimes trap AlphaFold2 models in local optima.
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Registered trials
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