ArticleNature communications2024
Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
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Who cites it
3 citing papers in PubMed.
- Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta.bioRxiv : the preprint server for biology · 2026Article
- CryoAlign2: efficient global and local Cryo-EM map retrieval based on parallel-accelerated local spatial structural features.Bioinformatics (Oxford, England) · 2025Article
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.
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Registered trials
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