ArticleActa crystallographica. Section D, Structural biology2020
Density modification of cryo-EM maps.
Article in Acta crystallographica. Section D, Structural biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Taccalonolide AJ uncouples tubulin GTP hydrolysis from microtubule depolymerization.bioRxiv : the preprint server for biology · 2026Article
- Structures of the sodium-coupled phosphate importer SLC34A2 reveal a distinct architecture and gating mechanism.Proceedings of the National Academy of Sciences of the United States of America · 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
- Taxol exploits molecular switches within tubulin to stabilize microtubules.bioRxiv : the preprint server for biology · 2026Article
- Transcription activation mechanism of a noncanonical DNA damage response pathway by the WYL-activator, DriD.Science advances · 2026Article
- Structural dynamics and permeability of the TRPV3 pentamer.Nature communications · 2025Article
- A short intrinsically disordered region at KtrB's N-terminus facilitates allosteric regulation of KNature communications · 2025Article
- Principles of ion binding to RNA inferred from the analysis of a 1.55 Å resolution bacterial ribosome structure - Part I: Mg2.Nucleic acids research · 2025Article
- Structural and mechanistic basis of the central energy-converting methyltransferase complex of methanogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Structural basis for autoinhibition by the dephosphorylated regulatory domain of Ycf1.Nature communications · 2024Article
- Structure of recombinant formate dehydrogenase from Methylobacterium extorquens (MeFDH1).Scientific reports · 2024Article
- A unified Watson-Crick geometry drives transcription of six-letter expanded DNA alphabets by E. coli RNA polymerase.Nature communications · 2023Article
- Structure of the peroxisomal Pex1/Pex6 ATPase complex bound to a substrate.Nature communications · 2023Article
- Article
- A pentameric TRPV3 channel with a dilated pore.Nature · 2023Article
- Chromatin remodeling of histone H3 variants underlies epigenetic inheritance of DNA methylation.bioRxiv : the preprint server for biology · 2023Article
- Improvement of cryo-EM maps by simultaneous local and non-local deep learning.Nature communications · 2023Article
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Electron scattering properties of biological macromolecules and their use for cryo-EM map sharpening.Faraday discussions · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here, the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows the incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between the macromolecule and solvent are visible, and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here, model bias is reduced by using ensemble models that allow an estimation of model uncertainty. A test of model bias is presented that suggests that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.