ArticleBioinformatics (Oxford, England)2023
Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Confidence-guided cryo-EM map optimisation with LocScale-2.0.Nature communications · 2026Article
- EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba.Nature communications · 2026Article
- CryoTEN: efficiently enhancing cryo-EM density maps using transformers.Bioinformatics (Oxford, England) · 2025Article
- AI-based methods for biomolecular structure modeling for Cryo-EM.Current opinion in structural biology · 2025Review
- Protein Secondary Structure and DNA/RNA Detection for Cryo-EM and Cryo-ET Using Emap2sec and Emap2secMethods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- The small CRL4Nature communications · 2024Article
- DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model.bioRxiv : the preprint server for biology · 2024Article
- Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality.Frontiers in molecular biosciences · 2024Article
- Super-resolution microscopy to study membrane nanodomains and transport mechanisms in the plasma membrane.Frontiers in molecular biosciences · 2024Review
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Authors and funding
3 authors.
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Abstract
motivationThe tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling.
resultsWe report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. AVAILABILITY AND IMPLEMENTATION: https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.
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