ArticleStructure (London, England : 1993)2023
Residue-level error detection in cryoelectron microscopy models.
Article in Structure (London, England : 1993), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Structures of ALG3/9/12 reveal the assembly logic of the N-glycan oligomannose core.Nature chemical biology · 2026Article
- AI-based quality assessment methods for protein structure models from cryo-EM.Current research in structural biology · 2025Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- Using deep-learning predictions reveals a large number of register errors in PDB depositions.IUCrJ · 2024Article
- The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins.Nature chemical biology · 2024Review
- UCSF ChimeraX: Tools for structure building and analysis.Protein science : a publication of the Protein Society · 2023Article
- AQcalc: A web server that identifies weak molecular interactions in protein structures.Protein science : a publication of the Protein Society · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. As modelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC's ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.
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Registered trials
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