CoChat
Sign Up
Cited by 12Advanced Electron Microscopy Techniques and ApplicationsEnzyme Structure and FunctionProtein Structure and Dynamics

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

CryoPROS: Correcting misalignment caused by preferred orientation using AI-generated auxiliary particles

Open access
Zhang, Hui, Zheng, Dihan, Wu, Qiurong, Yan, Nieng, Peng, Han, Hu, Qi, Peng, Ying, Yan, Zhaofeng, Shi, Zuoqiang, Bao, Chenglong, Hu, Mingxu·2025·Nature Communications
DOI 10.1038/s41467-025-59797-wPMC PMC12084624

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Abstract The preferred orientation phenomenon is a common issue in cryo-EM, posing a persistent challenge to conventional reconstruction methods. In this study, we introduce CryoPROS, a computational framework designed to correct misalignment caused by preferred orientation through co-refining the raw and auxiliary particles. These auxiliary particles, generated using a self-supervised deep generative model, enhance the alignment accuracy of particles in datasets affected by preferred orientation. CryoPROS achieved near-atomic resolution with the untilted HA-trimer dataset and successfully resolved high-resolution structures from three experimental datasets, including P001-Y, Na\textsubscript{\textsl{X}}, and hormone-sensitive lipase dimer, all affected by preferred orientation issues. Extensive experiments validate the robustness of cryoPROS and its minimal risk of introducing model bias. These findings suggest that in many cases thought to suffer from preferred orientation, addressing misalignment issues can lead to significant improvements in the density map.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-025-59797-w

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 3 references

Research works printed but not linked: 44

  1. Cheng, Y. Single-particle cryo-EM—How did it get here and where will it go. Science 361, 876–880 (2018). · DOI
  2. Lyumkis, D. Challenges and opportunities in cryo-EM single-particle analysis. J. Biol. Chem. 294, 5181–5197 (2019). · DOI
  3. Glaeser, R. M. & Han, B.-G. Opinion: hazards faced by macromolecules when confined to thin aqueous films. Biophys. Rep. 3, 1–7 (2017). · DOI
  4. Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017). · DOI
  5. Glaeser, R. M. How good can cryo-EM become? Nat. Methods 13, 28–32 (2016). · DOI
  6. Boisset, N. et al. Overabundant single-particle electron microscope views induce a three-dimensional reconstruction artifact. Ultramicroscopy 74, 201–207 (1998). · DOI
  7. Barth, M., Bryan, R. K. & Hegerl, R. Approximation of missing-cone data in 3D electron microscopy. Ultramicroscopy 31, 365–378 (1989). · DOI
  8. Drulyte, I. et al. Approaches to altering particle distributions in cryo-electron microscopy sample preparation. Acta Crystallogr. Sect. D. 74, 560–571 (2018). · DOI
  9. Noble, A. J. et al. Routine single particle CryoEM sample and grid characterization by tomography. eLife 7, e34257 (2018). · DOI
  10. Baldwin, P. R. & Lyumkis, D. Tools for visualizing and analyzing Fourier space sampling in Cryo-EM. Prog. Biophys. Mol. Biol. 160, 53–65 (2021). · DOI
  11. Baldwin, P. & Lyumkis, D. Non-uniformity of projection distributions attenuates resolution in Cryo-EM. Prog. Biophys. Mol. Biol. 150, 160–183 (2020). · DOI
  12. Hauer, F. et al. GraDeR: Membrane Protein Complex Preparation for Single-Particle Cryo-EM. Structure 23, 1769–1775 (2015). · DOI
  13. Chen, J. et al. Eliminating effects of particle adsorption to the air/water interface in single-particle cryo-electron microscopy: Bacterial RNA polymerase and CHAPSO. J. Struct. Biol. X. 1, 100005 (2019).
  14. Li, B. et al. Effect of charge on protein preferred orientation at the air–water interface in cryo-electron microscopy. J. Struct. Biol. 213, 107783 (2021). · DOI
  15. Frotscher, E. et al. A fluorinated detergent for membrane-protein applications. Angew. Chem. 54, 5069–5073 (2015). · DOI
  16. Neselu, K. et al. Measuring the effects of ice thickness on resolution in single particle cryo-EM. J. Struct. Biol. X, 100085 (2023).
  17. Noble, A. J. et al. Reducing effects of particle adsorption to the air–water interface in cryo-EM. Nat. Methods 15, 793–795 (2018). · DOI
  18. Bromberg, R. et al. The His-tag as a decoy modulating preferred orientation in cryoEM. Front. Mol. Biosci.9, 912072 (2022). · DOI
  19. Wu, K. et al. Application of monolayer graphene and its derivative in Cryo-EM sample preparation. Int. J. Mol. Sci. 2021. 22, https://doi.org/10.3390/ijms22168940. · DOI
  20. Xu, J. et al. Structural engineering of graphene for high‐resolution cryo‐electron microscopy. SmartMat 2, 202–212 (2021). · DOI
  21. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife. 7, e42166 (2018). · DOI
  22. Aiyer, S. et al. Overcoming resolution attenuation during tilted cryo-EM data collection. Nat. Commun. 15, 389 (2024). · DOI
  23. Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003) · DOI · Paper page
  24. Zheng, D. et al. Learn from unpaired data for image restoration: A variational bayes approach. IEEE Trans. Pattern Anal. Mach. Intell. 45, 5889–5903 (2022).
  25. Zheng, D. et al. SeNM-VAE: Semi-supervised noise modeling with hierarchical variational autoencoder. in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2024. · DOI
  26. Zivanov, J., Nakane, T. & Scheres, S. H. W. A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis. IUCrJ 6, 5–17 (2019). · DOI
  27. Hu, M. et al. A particle-filter framework for robust cryo-EM 3D reconstruction. Nat. Methods 15, 1083–1089 (2018). · DOI
  28. Lin, W.-C. et al. Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409-410, 17–26 (2017). · DOI · Paper page
  29. Batista, G. E. A. P. A., Prati, R. C. & Monard, M. C. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 6, 20–29 (2004). · DOI
  30. Sun, Z. et al. A novel ensemble method for classifying imbalanced data. Pattern Recognit. 48, 1623–1637 (2015). · DOI
  31. Sorzano, C. O. S. et al. A clustering approach to multireference alignment of single-particle projections in electron microscopy. J. Struct. Biol. 171, 197–206 (2010). · DOI
  32. Sorzano, C. O. S. et al. Algorithmic robustness to preferred orientations in single particle analysis by CryoEM. J. Struct. Biol. 213, 107695 (2021). · DOI
  33. Sorzano, C. O. S. et al. On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy. Acta Crystallogr. Sect. D. 78, 410–423 (2022). · DOI
  34. Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single-particle image processing. eLife 7, e35383 (2018). · DOI
  35. He, J., Li, T. & Huang, S.-Y. Improvement of cryo-EM maps by simultaneous local and non-local deep learning. Nat. Commun. 14, 3217 (2023). · DOI
  36. Paulsen, C. E. et al. Structure of the TRPA1 ion channel suggests regulatory mechanisms. Nature 525, 552–552 (2015). · DOI
  37. Bepler, T. et al. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11, 5208 (2020). · DOI
  38. Henderson, R. Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noise. Proc. Natl Acad. Sci. 110, 18037–18041 (2013). · DOI
  39. Punjani, A. & Fleet, D. J. 3DFlex: Determining structure and motion of flexible proteins from cryo-EM. Nat. Methods 20, 860–870 (2023). · DOI
  40. Child, R. Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images. Preprint at https://doi.org/10.48550/arXiv.2011.10650 (2021). · DOI · Paper page
  41. Vahdat, A. & Kautz, J. NVAE: A Deep Hierarchical Variational Autoencoder. Preprint at https://doi.org/10.48550/arXiv.2007.03898 (2021). · DOI
  42. Sønderby, C. K. et al., Ladder variational autoencoders, in Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, Curran Associates Inc.: Barcelona, Spain. p. 3745–3753.
  43. Zhang, Y. et al. Residual dense network for image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2472–2481 (2018). · DOI
  44. Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980. · arXiv

Metadata

Authors
Zhang, H., Zheng, D., Wu, Q., Yan, N., Peng, H., Hu, Q., Peng, Y., Yan, Z., Shi, Z., Bao, C., & Hu, M.
Affiliations
  • Tsinghua University (CN)
  • University of California, San Francisco (US)
  • Center for Life Sciences (CN)
  • Westlake University (CN)
  • Hunan University (CN)
  • Beijing Institute of Mathematical Sciences and Applications (CN)
  • State Key Laboratory of Membrane Biology (CN)
  • Shenzhen Medical Academy of Research and Translation (CN)
Corresponding author
  • Bao, Chenglong
  • Hu, Mingxu
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2025-05-16
Volume
16
Issue
1
Pages
4565-4565
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-025-59797-w
PMC
PMC12084624
OpenAlex
W4410438894
Cited by
12
References
3
Citations by year
2026: 7 · 2025: 5
FWCI
4.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (12271291, 12071244)
  • National Key R&D Program of China
  • Beijing Frontier Research Center for Biological Structure, Shenzhen Medical Academy of Research and Translation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
crystallographic texture, auxiliary particles, deep generative models, model bias
MeSH terms
Algorithms, Image Processing, Computer-Assisted, Cryoelectron Microscopy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review