ArticleIUCrJ2020
Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM.
Article in IUCrJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- IHMValidation: Assessment of Integrative Structure Models Deposited to the Protein Data Bank.Journal of molecular biology · 2026Article
- Article
- High-resolution protein modeling through Cryo-EM and AI: current trends and future perspectives - a review.Frontiers in molecular biosciences · 2025Review
- Cryo-EM structure and B-factor refinement with ensemble representation.Nature communications · 2024Article
- Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.Proteins · 2021Article
- Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using
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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.