ArticleAngewandte Chemie (International ed. in English)2020
Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.
Article in Angewandte Chemie (International ed. in English), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 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
- Variable Resolution Maps (VRM) in CCTBX and Phenix: Accounting For Local Resolution In cryoEM.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey and benchmark of deep learning-based methods for atomic model building from cryo-electron microscopy density maps.Briefings in bioinformatics · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.Nature methods · 2024Article
- NucleoFind: a deep-learning network for interpreting nucleic acid electron density.Nucleic acids research · 2024Article
- Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.Nature communications · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.Scientific data · 2024Article
- DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model.bioRxiv : the preprint server for biology · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.bioRxiv : the preprint server for biology · 2024Article
- An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.Current opinion in structural biology · 2023Review
- Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps.Briefings in bioinformatics · 2023Article
- Errors in structural biology are not the exception.Acta crystallographica. Section D, Structural biology · 2023Article
- Article
- LPTD: a novel linear programming-based topology determination method for cryo-EM maps.Bioinformatics (Oxford, England) · 2022Article
- Using cryo-EM to uncover mechanisms of bacterial transcriptional regulation.Biochemical Society transactions · 2021Review
- EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.Briefings in bioinformatics · 2021Article
- EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization.Biomimetics (Basel, Switzerland) · 2021Article
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4 authors.
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Abstract
In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main-chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.
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