ReviewIUCrJ2026
Making sense of invisible densities in single-particle cryo-EM.
Review in IUCrJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In the era of single-particle cryogenic electron microscopy (cryo-EM) and AI-driven protein structure prediction, obtaining high-resolution protein structures, either experimentally or computationally, has become increasingly routine. Yet studying and understanding protein dynamics remains challenging. In single-particle cryo-EM, protein dynamics are most obviously manifested as poor local resolution or disappearing densities in specific regions of a reconstruction. No method is yet available to computationally generate conformational ensembles that fully deconvolute these experimental observations. When dynamics are key to understanding protein function, it is clear to us that introducing new experimental approaches is necessary to close this gap and make sense of invisible densities in single-particle cryo-EM.
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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.