*Result*: A comprehensive foundation model for cryo-EM image processing.
Title:
A comprehensive foundation model for cryo-EM image processing.
Authors:
Yan Y; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; School of Engineering, Westlake University, Hangzhou, China., Fan S; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; School of Engineering, Westlake University, Hangzhou, China., Yuan F; Research Center for Industries of the Future, Westlake University, Hangzhou, China. yuanfajie@westlake.edu.cn.; School of Engineering, Westlake University, Hangzhou, China. yuanfajie@westlake.edu.cn., Shen H; Research Center for Industries of the Future, Westlake University, Hangzhou, China. shenhuaizong@westlake.edu.cn.; Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China. shenhuaizong@westlake.edu.cn.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. shenhuaizong@westlake.edu.cn.; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China. shenhuaizong@westlake.edu.cn.
Source:
Nature methods [Nat Methods] 2026 Jan; Vol. 23 (1), pp. 88-95. Date of Electronic Publication: 2025 Nov 27.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101215604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1548-7105 (Electronic) Linking ISSN: 15487091 NLM ISO Abbreviation: Nat Methods Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Nature Pub. Group, c2004-
MeSH Terms:
References:
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Xu, H. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024). (PMID: 3877809811153137)
Ma, C., Tan, W., He, R. & Yan, B. Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration. Nat. Methods 21, 1558–1567 (2024). (PMID: 38609490)
Iudin, A. et al. EMPIAR: the Electron Microscopy Public Image Archive. Nucleic Acids Res. 51, D1503–D1511 (2023). (PMID: 36440762)
Dhakal, A., Gyawali, R., Wang, L. & Cheng, J. A large expert-curated cryo-EM image dataset for machine learning protein particle picking. Sci. Data 10, 392 (2023). (PMID: 3734934510287764)
El Banani, M. et al. Probing the 3d awareness of visual foundation models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 21795–21806 (IEEE, 2024).
Zhong, E. D., Lerer, A., Davis, J. H. & Berger, B. CryoDRGN2: ab initio neural reconstruction of 3D protein structures from real cryo-EM images. In Proc. IEEE/CVF International Conference on Computer Vision 4066–4075 (IEEE, 2021).
Luo, Z., Ni, F., Wang, Q. & Ma, J. OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis. Nat. Methods 20, 1729–1738 (2023). (PMID: 3781398810630141)
Levy, A. et al. CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nat. Methods 22, 1486–1494 (2025). (PMID: 40571741)
Qin, B. et al. Cryo-EM captures early ribosome assembly in action. Nat. Commun. 14, 898 (2023). (PMID: 367972499935924)
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Hu, M. et al. A particle-filter framework for robust cryo-EM 3D reconstruction. Nat. Methods 15, 1083–1089 (2018). (PMID: 30504871)
Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017). (PMID: 286716745533649)
Liu, Z. et al. Determination of the ribosome structure to a resolution of 2.5 Å by single-particle cryo-EM. Protein Sci. 26, 82–92 (2017). (PMID: 27750394)
Fan, X. et al. Single particle cryo-EM reconstruction of 52 kDa streptavidin at 3.2 Angstrom resolution. Nat. Commun. 10, 2386 (2019). (PMID: 311605916546690)
Baxter, W. T., Grassucci, R. A., Gao, H. & Frank, J. Determination of signal-to-noise ratios and spectral SNRs in cryo-EM low-dose imaging of molecules. J. Struct. Biol. 166, 126–132 (2009). (PMID: 192693322700974)
Palovcak, E., Asarnow, D., Campbell, M. G., Yu, Z. & Cheng, Y. Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks. IUCrJ 7, 1142–1150 (2020). (PMID: 332093257642784)
Liu, Y.-T., Hu, J. & Zhou, Z. H. Resolving the Preferred Orientation Problem in CryoEM Reconstruction with Self-Supervised Deep Learning (Oxford Univ. Press, 2023).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
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Lawson, C. L. et al. EMDataBank unified data resource for 3DEM. Nucleic Acids Res. 44, D396–D403 (2016). (PMID: 26578576)
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Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020). (PMID: 33257830)
Yan, Y., Fan, S., Yuan, F. & Shen, H. Simulated cryo-EM particle datasets from paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/records/17066236 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. Genuine particle datasets for paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/uploads/17066297 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. The resampled CryoBench Ribosembly dataset used in paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/records/17066704 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. The reconstruction results of CryoSolver and CryoWizard. Zenodo https://zenodo.org/records/17062718 (2025).
Cheng, Y. Single-particle cryo-EM—how did it get here and where will it go. Science 361, 876–880 (2018). (PMID: 301664846460916)
Bai, X.-C., McMullan, G. & Scheres, S. H. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40, 49–57 (2015). (PMID: 25544475)
Frank, J. Advances in the field of single-particle cryo-electron microscopy over the last decade. Nat. Protoc. 12, 209–212 (2017). (PMID: 280550375479931)
Holcomb, J. et al. Protein crystallization: eluding the bottleneck of X-ray crystallography. AIMS Biophys. 4, 557–575 (2017). (PMID: 290519195645037)
Scheiner, G. The resolution revolution. Diabetes Self Manag. 32, 28–29 (2015).
Amunts, A. et al. Structure of the yeast mitochondrial large ribosomal subunit. Science 343, 1485–1489 (2014). (PMID: 246759564046073)
Liao, M., Cao, E., Julius, D. & Cheng, Y. Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504, 107–112 (2013). (PMID: 243051604078027)
McMullan, G., Faruqi, A. & Henderson, R. Direct electron detectors. Methods Enzymol. 579, 1–17 (2016). (PMID: 27572721)
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017). (PMID: 282504665494038)
Li, X. et al. Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10, 584–590 (2013). (PMID: 236445473684049)
Bai, X.-C., Fernandez, I. S., McMullan, G. & Scheres, S. H. Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles. Elife 2, e00461 (2013). (PMID: 234270243576727)
Scheres, S. H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012). (PMID: 230007013690530)
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017). (PMID: 28165473)
Zhou, Y., Moscovich, A., Bendory, T. & Bartesaghi, A. Unsupervised particle sorting for high-resolution single-particle cryo-EM. Inverse Probl. 36, 044002 (2020).
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. Elife 7, e42166 (2018). (PMID: 304120516250425)
Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019). (PMID: 315915786858545)
Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2, 218 (2019). (PMID: 312402566584505)
Kimanius, D., Dong, L., Sharov, G., Nakane, T. & Scheres, S. H. W. New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem. J. 478, 4169–4185 (2021). (PMID: 34783343)
Li, Y., Cash, J. N., Tesmer, J. J. G. & Cianfrocco, M. A. High-throughput cryo-EM enabled by user-free preprocessing routines. Structure 28, 858–869 (2020). (PMID: 322944687347462)
Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat. Methods 18, 176–185 (2021). (PMID: 335425108183613)
Pfab, J., Phan, N. M. & Si, D. DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes. Proc. Natl Acad. Sci. USA 118, e2017525118 (2021). (PMID: 33361332)
Jamali, K. et al. Automated model building and protein identification in cryo-EM maps. Nature 628, 450–457 (2024). (PMID: 3840848811006616)
Scheres, S. H. Processing of structurally heterogeneous cryo-EM data in RELION. Methods Enzymol. 579, 125–157 (2016). (PMID: 27572726)
Zhu, D. et al. Correction of preferred orientation-induced distortion in cryo-electron microscopy maps.Sci. Adv. 10, eadn0092 (2024). (PMID: 3905877111892697)
Zhang, H. et al. CryoPROS: Correcting misalignment caused by preferred orientation using AI-generated auxiliary particles. Nat. Commun. 16, 4565 (2025). (PMID: 4037967412084624)
Liu, Y. et al. Overcoming the preferred-orientation problem in cryo-EM with self-supervised deep learning. Nat. Methods 22, 113–123 (2025). (PMID: 39558095)
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning 1597–1607 (PMLR, 2020).
He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF International Conference on Computer Vision 9726–9735 (IEEE, 2020).
Chen, X., Xie, S. & He, K. An empirical study of training self-supervised vision transformers. In Proc. IEEE/CVF International Conference on Computer Vision 9640–9649 (IEEE, 2021).
Oquab, M. et al. Dinov2: learning robust visual features without supervision. In Transactions on Machine Learning Research https://openreview.net/pdf?id=a68SUt6zFt (2024).
Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023). (PMID: 3770472810550819)
Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354–367 (2024). (PMID: 3852367910957482)
Wang, X. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970–978 (2024). (PMID: 3923216412186853)
Xu, H. A whole-slide foundation model for digital pathology from real-world data. Nature 630, 181–188 (2024). (PMID: 3877809811153137)
Ma, C., Tan, W., He, R. & Yan, B. Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration. Nat. Methods 21, 1558–1567 (2024). (PMID: 38609490)
Iudin, A. et al. EMPIAR: the Electron Microscopy Public Image Archive. Nucleic Acids Res. 51, D1503–D1511 (2023). (PMID: 36440762)
Dhakal, A., Gyawali, R., Wang, L. & Cheng, J. A large expert-curated cryo-EM image dataset for machine learning protein particle picking. Sci. Data 10, 392 (2023). (PMID: 3734934510287764)
El Banani, M. et al. Probing the 3d awareness of visual foundation models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 21795–21806 (IEEE, 2024).
Zhong, E. D., Lerer, A., Davis, J. H. & Berger, B. CryoDRGN2: ab initio neural reconstruction of 3D protein structures from real cryo-EM images. In Proc. IEEE/CVF International Conference on Computer Vision 4066–4075 (IEEE, 2021).
Luo, Z., Ni, F., Wang, Q. & Ma, J. OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis. Nat. Methods 20, 1729–1738 (2023). (PMID: 3781398810630141)
Levy, A. et al. CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nat. Methods 22, 1486–1494 (2025). (PMID: 40571741)
Qin, B. et al. Cryo-EM captures early ribosome assembly in action. Nat. Commun. 14, 898 (2023). (PMID: 367972499935924)
Jeon, M. et al. CryoBench: diverse and challenging datasets for the heterogeneity problem in cryo-EM. In Proc. 38th Conference on Neural Information Processing Systems 89468–89512 (Curran, 2024).
Hu, M. et al. A particle-filter framework for robust cryo-EM 3D reconstruction. Nat. Methods 15, 1083–1089 (2018). (PMID: 30504871)
Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017). (PMID: 286716745533649)
Liu, Z. et al. Determination of the ribosome structure to a resolution of 2.5 Å by single-particle cryo-EM. Protein Sci. 26, 82–92 (2017). (PMID: 27750394)
Fan, X. et al. Single particle cryo-EM reconstruction of 52 kDa streptavidin at 3.2 Angstrom resolution. Nat. Commun. 10, 2386 (2019). (PMID: 311605916546690)
Baxter, W. T., Grassucci, R. A., Gao, H. & Frank, J. Determination of signal-to-noise ratios and spectral SNRs in cryo-EM low-dose imaging of molecules. J. Struct. Biol. 166, 126–132 (2009). (PMID: 192693322700974)
Palovcak, E., Asarnow, D., Campbell, M. G., Yu, Z. & Cheng, Y. Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks. IUCrJ 7, 1142–1150 (2020). (PMID: 332093257642784)
Liu, Y.-T., Hu, J. & Zhou, Z. H. Resolving the Preferred Orientation Problem in CryoEM Reconstruction with Self-Supervised Deep Learning (Oxford Univ. Press, 2023).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021). (PMID: 32881101)
Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015). (PMID: 262789806760662)
Lawson, C. L. et al. EMDataBank unified data resource for 3DEM. Nucleic Acids Res. 44, D396–D403 (2016). (PMID: 26578576)
Dosovitskiy, A. An image is worth 16 × 16 words: transformers for image recognition at scale. In International Conference on Learning Representations https://openreview.net/pdf?id=YicbFdNTTy (2021).
Grill, J.-B. et al. Bootstrap your own latent—a new approach to self-supervised learning. In 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://papers.nips.cc/paper/2020/file/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf (2020).
Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020). (PMID: 33257830)
Yan, Y., Fan, S., Yuan, F. & Shen, H. Simulated cryo-EM particle datasets from paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/records/17066236 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. Genuine particle datasets for paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/uploads/17066297 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. The resampled CryoBench Ribosembly dataset used in paper ‘A comprehensive foundation model for cryo-EM image processing’. Zenodo https://zenodo.org/records/17066704 (2025).
Yan, Y., Fan, S., Yuan, F. & Shen, H. The reconstruction results of CryoSolver and CryoWizard. Zenodo https://zenodo.org/records/17062718 (2025).
Grant Information:
2024YFA0916903 Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology); 2022ZD0115100 Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology); 32122042 National Natural Science Foundation of China (National Science Foundation of China); 32071208 National Natural Science Foundation of China (National Science Foundation of China); U21A20427 National Natural Science Foundation of China (National Science Foundation of China); DQ24C050001 Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)
Substance Nomenclature:
0 (Macromolecular Substances)
Entry Date(s):
Date Created: 20251127 Date Completed: 20260111 Latest Revision: 20260111
Update Code:
20260130
DOI:
10.1038/s41592-025-02916-8
PMID:
41310054
Database:
MEDLINE
*Further Information*
*Cryogenic electron microscopy (cryo-EM) has become a premier technique for determining high-resolution structures of biological macromolecules. However, its broad application is constrained by the demand for specialized expertise. Here, to address this limitation, we introduce the Cryo-EM Image Evaluation Foundation (Cryo-IEF) model, a versatile tool pre-trained on ~65 million cryo-EM particle images through unsupervised learning. Cryo-IEF performs diverse cryo-EM processing tasks, including particle classification by structure, pose-based clustering and image quality assessment. Building on this foundation, we developed CryoWizard, a fully automated single-particle cryo-EM processing pipeline enabled by fine-tuned Cryo-IEF for efficient particle quality ranking. CryoWizard resolves high-resolution structures across samples of varied properties and effectively mitigates the prevalent challenge of preferred orientation in cryo-EM.
(© 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.)*
*Competing interests: The authors declare no competing interests.*