ArticleProceedings of the National Academy of Sciences of the United States of America2013
Cross-validation in cryo-EM-based structural modeling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Convergent use of aminopeptidase N receptor by hedgehog merbecoviruses.Nature microbiology · 2026Article
- IHMValidation: Assessment of Integrative Structure Models Deposited to the Protein Data Bank.Journal of molecular biology · 2026Article
- AI-based quality assessment methods for protein structure models from cryo-EM.Current research in structural biology · 2025Review
- Article
- Correction of preferred orientation-induced distortion in cryo-electron microscopy maps.Science advances · 2024Article
- Structures of human γδ T cell receptor-CD3 complex.Nature · 2024Article
- Cryo-EM of Aβ fibrils from mouse models find tg-APPNature neuroscience · 2023Article
- Overview and applications of map and model validation tools in the CCP-EM software suite.Faraday discussions · 2022Review
- Atomic model validation using the CCP-EM software suite.Acta crystallographica. Section D, Structural biology · 2022Article
- Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality.Structure (London, England : 1993) · 2021Article
- Predicting RNA Scaffolds with a Hybrid Method of Vfold3D and VfoldLA.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Cryo-EM structure of islet amyloid polypeptide fibrils reveals similarities with amyloid-β fibrils.Nature structural & molecular biology · 2020Article
- Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.Journal of chemical information and modeling · 2020Review
- Validation tests for cryo-EM maps using an independent particle set.Journal of structural biology: X · 2020Article
- Salt Dependence of A-Form RNA Duplexes: Structures and Implications.The journal of physical chemistry. B · 2019Article
- CryoEM-based hybrid modeling approaches for structure determination.Current opinion in microbiology · 2018Review
- Variability of Protein Structure Models from Electron Microscopy.Structure (London, England : 1993) · 2017Article
- Archaeal flagellin combines a bacterial type IV pilin domain with an Ig-like domain.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Refinement of atomic models in high resolution EM reconstructions using Flex-EM and local assessment.Methods (San Diego, Calif.) · 2016Article
- Multifrequency Force Microscopy of Helical Protein Assembly on a Virus.Scientific reports · 2016Article
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2 authors.
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Abstract
Single-particle cryo-EM is a powerful approach to determine the structure of large macromolecules and assemblies thereof in many cases at subnanometer resolution. It has become popular to refine or flexibly fit atomic models into density maps derived from cryo-EM experiments. These density maps are typically significantly lower in resolution than electron density maps obtained from X-ray diffraction experiments, such that the number of parameters that need to be determined is much larger than the number of experimental observables. Overfitting and misinterpretation of the density, thus, become a serious problem. For diffraction data, a cross-validation approach was introduced almost 20 y ago; however, no such approach has been described yet for structure refinement against cryo-EM density maps, although the overfitting problem is, because of the lower resolution, significantly larger. We present a cross-validation approach for real-space refinement against cryo-EM density maps in analogy to cross-validation typically used in crystallography. Our approach is able to detect overfitting and allows for optimizing the choice of restraints used in the refinement. The approach is shown on three protein structures with simulated data and experimental data of the rotavirus double-layer particle. Because cross-validation requires splitting the dataset into at least two independent sets, we further present an approach to quantify correlations between the structure factor sets. This analysis is also helpful for other cross-validation applications, such as refinements against diffraction data or 3D reconstructions of cryo-EM density maps.
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