ArticleFrontiers in molecular biosciences2024
Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality.
Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predicting water at the protein interface in cryo-EM structures from MD-excess chemical potential.Biophysical journal · 2026Article
- Interactive segmentation of membrane and membrane-mimic densities in cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2026Article
- 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
- In silico docking yields small molecule negative allosteric modulators targeting the core of Frizzled 7.Nature communications · 2025Article
- Artificial intelligence in structural biology: Preface.Structural dynamics (Melville, N.Y.) · 2025Article
- ANTIDOTE: A Metadata-Driven Neural Network for Improving CryoEM 3-D Particle Sorting.bioRxiv : the preprint server for biology · 2025Article
- De Novo Hydration of Cryo-EM Reconstructions through Molecular Dynamics Simulations of the Excess Chemical Potential.bioRxiv : the preprint server for biology · 2025Article
- Applications of visualization technology in the structural sciences.Structural dynamics (Melville, N.Y.) · 2025Article
- Preparation of oxygen-sensitive proteins for high-resolution cryoEM structure determination using blot-free vitrification.Nature communications · 2025Article
- Advances in cryo-electron microscopy (cryoEM) for structure-based drug discovery.Expert opinion on drug discovery · 2025Review
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Authors and funding
3 authors.
Funding
Abstract
The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.
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