ArticleActa crystallographica. Section D, Structural biology2023
Likelihood-based signal and noise analysis for docking of models into cryo-EM maps.
Article in Acta crystallographica. Section D, Structural biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Structural basis of stepwise G protein activation by the viral chemokine receptor US28.bioRxiv : the preprint server for biology · 2026Article
- Confidence-guided cryo-EM map optimisation with LocScale-2.0.Nature communications · 2026Article
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
- A multi-dentate, cooperative interaction between endo- and exo-ribonucleases within the bacterial RNA degradosome.Nucleic acids research · 2025Article
- Likelihood-based interactive local docking into cryo-EM maps in ChimeraX.Acta crystallographica. Section D, Structural biology · 2024Article
- The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.Acta crystallographica. Section D, Structural biology · 2023Article
- UCSF ChimeraX: Tools for structure building and analysis.Protein science : a publication of the Protein Society · 2023Article
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Authors and funding
4 authors.
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Abstract
Fast, reliable docking of models into cryo-EM maps requires understanding of the errors in the maps and the models. Likelihood-based approaches to errors have proven to be powerful and adaptable in experimental structural biology, finding applications in both crystallography and cryo-EM. Indeed, previous crystallographic work on the errors in structural models is directly applicable to likelihood targets in cryo-EM. Likelihood targets in Fourier space are derived here to characterize, based on the comparison of half-maps, the direction- and resolution-dependent variation in the strength of both signal and noise in the data. Because the signal depends on local features, the signal and noise are analysed in local regions of the cryo-EM reconstruction. The likelihood analysis extends to prediction of the signal that will be achieved in any docking calculation for a model of specified quality and completeness. A related calculation generalizes a previous measure of the information gained by making the cryo-EM reconstruction.
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