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CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
Open accessAbstract
Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major challenge to existing three-dimensional reconstruction methods. Here, we present cryoDRGN, an algorithm that leverages the representation power of deep neural networks to directly reconstruct continuous distributions of 3D density maps and map per-particle heterogeneity of single-particle cryo-EM datasets. Using cryoDRGN, we uncovered residual heterogeneity in high-resolution datasets of the 80S ribosome and the RAG complex, revealed a new structural state of the assembling 50S ribosome, and visualized large-scale continuous motions of a spliceosome complex. CryoDRGN contains interactive tools to visualize a dataset’s distribution of per-particle variability, generate density maps for exploratory analysis, extract particle subsets for use with other tools and generate trajectories to visualize molecular motions. CryoDRGN is open-source software freely available at http://cryodrgn.csail.mit.edu. CryoDRGN is an unsupervised machine learning algorithm that reconstructs continuous distributions of three-dimensional density maps from heterogeneous single-particle cryo-EM data.
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Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897
Rosenthal, P. B., & Henderson, R. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.07.013 · Cited by 2,416
Cited by
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
Protein structure prediction has reached the single-structure frontier
Lane, T. J. (2023) · Nature Methods · doi:10.1038/s41592-022-01760-4 · Cited by 125
Structural basis for membrane attack complex inhibition by CD59
Couves, E. C., Gardner, S., Voisin, T. B., Bickel, J. K., Stansfeld, P. J., Tate, E. W., & Bubeck, D. (2023) · Nature Communications · doi:10.1038/s41467-023-36441-z · Cited by 85
Giri, N., Roy, R. S., & Cheng, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102536 · Cited by 59
DeepTracer-ID: De novo protein identification from cryo-EM maps
Chang, L., Wang, F., Connolly, K., Meng, H., Su, Z., Cvirkaite-Krupovic, V., Krupovic, M., Egelman, E. H., & Si, D. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.06.025 · Cited by 50
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44
Liu, S., Xia, X., Zhen, J., Li, Z., & Zhou, Z. H. (2022) · Cell Discovery · doi:10.1038/s41421-022-00487-y · Cited by 29
Automated multiconformer model building for X-ray crystallography and cryo-EM
Wankowicz, S. A., Ravikumar, A., Sharma, S., Riley, B., Raju, A., Hogan, D. W., Flowers, J., van den Bedem, H., Keedy, D. A., & Fraser, J. S. (2024) · eLife · doi:10.7554/elife.90606 · Cited by 28
CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets
Levy, A., Raghu, R., Feathers, J. R., Grzadkowski, M., Poitevin, F., Johnston, J. D., Vallese, F., Clarke, O. B., Wetzstein, G., & Zhong, E. D. (2025) · Nature Methods · doi:10.1038/s41592-025-02720-4 · Cited by 25
Self-citationStructural insights into human TFIIIC promoter recognition
(2023) · doi:10.1126/sciadv.adh2019 · Cited by 23
Fadini, A., Li, M., McCoy, A. J., Banjara, S., Okumura, H., Napier, E., Fontana, P., Khan, A. R., Jovine, L., Terwilliger, T. C., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03047-4 · Cited by 22
Gong, R., Reynolds, M. J., Sun, X., & Alushin, G. M. (2025) · Science Advances · doi:10.1126/sciadv.adu0989 · Cited by 21
Predicting the conformational flexibility of antibody and T-cell receptor CDRs
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · doi:10.1101/2025.03.19.644119 · Cited by 20
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01131-6 · Cited by 20
Structural insights into the mechanism of phosphate recognition and transport by XPR1
Zhang, W., Chen, Y., Guan, Z., Wang, Y., Tang, M., Du, Z., Zhang, J., Cheng, M., Zuo, J., Liu, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55471-9 · Cited by 20
Conformational space exploration of cryo-EM structures by variability refinement
Afonine, P. V., Gobet, A., Moissonnier, L., Martin, J., Poon, B. K., & Chaptal, V. (2023) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2023.184133 · Cited by 18
Thorn, A. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102368 · Cited by 16
May, M. B., Lopez-Perez, G. S., & Davis, J. H. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2521210123 · Cited by 13
Self-citationThe Structure of the Drp1 Lattice on Membrane
Peng, R., Rochon, K., Hutson, A. N., Stagg, S. M., & Mears, J. A. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169125 · Cited by 12
Chen, M. (2025) · Communications Biology · doi:10.1038/s42003-025-08202-9 · Cited by 11
Ghazi Esfahani, B., Walia, N., Neselu, K., Garg, Y., Aragon, M., Askenasy, I., Wei, H. A., Mendez, J. H., & Stroupe, M. E. (2025) · Nature Communications · doi:10.1038/s41467-025-58037-5 · Cited by 10
Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens
Saeed, A. A., Klureza, M. A., & Hekstra, D. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01380 · Cited by 9
High-resolution real-space reconstruction of cryo-EM structures using a neural field network
Huang, Y., Zhu, C., Yang, X., & Liu, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00870-2 · Cited by 9
End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed
Wang, J., Tan, C., Gao, Z., Zhang, G., Zhang, Y., & Li, S. Z. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01056-0 · Cited by 8
Cryo-EM reveals the structure and dynamics of a 723-residue malate synthase G
Ho, M.-R., Wu, Y.-M., Lu, Y.-C., Ko, T.-P., & Wu, K.-P. (2023) · Journal of Structural Biology · doi:10.1016/j.jsb.2023.107958 · Cited by 8
Lopez-Mateos, D., Narang, K., & Yarov-Yarovoy, V. (2024) · doi:10.1101/2024.10.15.618559 · Cited by 5
Lopez-Mateos, D., Narang, K., & Yarov-Yarovoy, V. (2026) · Journal of General Physiology · doi:10.1085/jgp.202413705 · Cited by 5
Peripheral membrane protein endophilin B1 probes, perturbs and permeabilizes lipid bilayers
Thorlacius, A., Rulev, M., Sundberg, O., & Sundborger-Lunna, A. (2025) · Communications Biology · doi:10.1038/s42003-025-07610-1 · Cited by 5
Cryo-EM captures early intermediate steps in dynein activation by LIS1
Nguyen, K. H. V., Karasmanis, E. P., Kendrick, A. A., Reck-Peterson, S. L., & Leschziner, A. E. (2025) · Nature Communications · doi:10.1038/s41467-025-62185-z · Cited by 5
Molecular exaptation by the integrin αI domain
Hollis, J. A., Chan, M. C., Malik, H. S., & Campbell, M. G. (2025) · Science Advances · doi:10.1126/sciadv.adx9567 · Cited by 4
Higinbotham, H. R., Arbour, C. A., & Imperiali, B. (2025) · Protein Science · doi:10.1002/pro.70352 · Cited by 2
Stroupe, M. E., Esfahani, B. G., Walia, N., Neselu, K., Aragon, M., Askenasy, I., Wei, H., & Mendez, J. (2024) · doi:10.21203/rs.3.rs-4758050/v1
Metadata
- Authors
- Zhong, E. D., Bepler, T., Berger, B., & Davis, J. H.
- Venue
- Nature Methods
- Published
- 2021-02
- Volume
- 18
- Issue
- 2
- Pages
- 176-185
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3127326657
- Cited by
- 636
- References
- 5
- FWCI
- 39.48
- Type
- journal article
- Language
- en
- Open access
- green
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