英语轻松读发新版了,欢迎下载、更新

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

2025-06-26 09:58:21 英文原文

作者:Zhong, Ellen D.

References

  1. Nakane, T. et al. Single-particle cryo-EM at atomic resolution. Nature 587, 152–156 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Yip, K. M., Fischer, N., Paknia, E., Chari, A. & Stark, H. Atomic-resolution protein structure determination by cryo-EM. Nature 587, 157–161 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Frank, J. & Ourmazd, A. Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM. Methods 100, 61–67 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Punjani, A. & Fleet, D. J. 3D variability analysis: resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM. J. Struct. Biol. 213, 107702 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, M. & Ludtke, S. J. Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. Nat. Methods 18, 930–936 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nakane, T., Kimanius, D., Lindahl, E. & Scheres, S. H. Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION. eLife 7, e36861 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Chua, E. Y. D. et al. Better, faster, cheaper: recent advances in cryo-electron microscopy. Annu. Rev. Biochem. 91, 1–32 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Oikonomou, C. M. & Jensen, G. J. Cellular electron cryotomography: toward structural biology in situ. Annu. Rev. Biochem. 86, 873–896 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Galaz-Montoya, J. G. & Ludtke, S. J. The advent of structural biology in situ by single particle cryo-electron tomography. Biophysics Rep. 3, 17–35 (2017).

    Article  CAS  Google Scholar 

  11. Xue, L. et al. Visualizing translation dynamics at atomic detail inside a bacterial cell. Nature 610, 205–211 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rangan, R. et al. CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells. Nat. Methods 21, 1537–1545 (2024).

    Article  CAS  PubMed  Google Scholar 

  13. Punjani, A. & Fleet, D. J. 3DFlex: determining structure and motion of flexible proteins from cryo-EM. Nat. Methods 20, 860–870 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhong, E. D., Bepler, T., Davis, J. H. & Berger, B. Reconstructing continuous distributions of 3D protein structure from cryo-EM images. In International Conference on Learning Representations (ICLR, 2020).

  15. 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).

    Article  CAS  PubMed  Google Scholar 

  16. Davis, J. H. et al. Modular assembly of the bacterial large ribosomal subunit. Cell 167, 1610–1622 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Plaschka, C., Lin, P. -C. & Nagai, K. Structure of a pre-catalytic spliceosome. Nature 546, 617–621 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Walls, A. C. et al. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 181, 281–292 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. DAmico, K. A. et al. Structure of a membrane tethering complex incorporating multiple snares. Nat. Struct. Mol. Biol. 31, 246–254 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tan, Y. Z. et al. CryoEM of endogenous mammalian V-ATPase interacting with the TLDc protein mEAK-7. Life Sci. Alliance 5, e202201527 (2022).

  21. Vallese, F. et al. Architecture of the human erythrocyte ankyrin-1 complex. Nat. Struct. Mol. Biol. 29, 706–718 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tegunov, D., Xue, L., Dienemann, C., Cramer, P. & Mahamid, J. Multi-particle cryo-EM refinement with m visualizes ribosome-antibiotic complex at 3.5 Å in cells. Nat. Methods 18, 186–193 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bojanowski, P., Joulin, A., Lopez-Paz, D. & Szlam, A. Optimizing the latent space of generative networks. In Proc. 35th International Conference on Machine Learning Vol. 80 (eds Dy, J. & Krause, A.) 600–609 (ICML, 2018).

  24. Edelberg, D. G. & Lederman, R. R. Using VAEs to learn latent variables: observations on applications in cryo-EM. Preprint at https://arxiv.org/abs/2303.07487 (2023).

  25. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gilles, M. A. T. & Singer, A. Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression. PNAS 122, 9 (2025).

    Article  Google Scholar 

  27. 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 Proceedings of the IEEE/CVF International Conference on Computer Vision 4066–4075 (CVPR, 2021).

  28. Herreros, D. et al. Estimating conformational landscapes from cryo-em particles by 3D zernike polynomials. Nat. Commun. 14, 154 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kaur, S. et al. Local computational methods to improve the interpretability and analysis of cryo-EM maps. Nat. Commun. 12, 1240 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  31. Dashti, A. et al. Retrieving functional pathways of biomolecules from single-particle snapshots. Nat. Commun. 11, 4734 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Maji, A. et al. Propagation of conformational coordinates across angular space in mapping the continuum of states from cryo-EM data by manifold embedding. J. Chem. Inf. Model. 60, 2484–2491 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Moscovich, A., Halevi, A., Andén, J. & Singer, A. Cryo-EM reconstruction of continuous heterogeneity by laplacian spectral volumes. Inverse Probl. 36, 024003 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lederman, R. R. & Singer, A. Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions. Preprint at https://arxiv.org/abs/1704.02899 (2017).

  35. Gupta, H., Phan, T. H., Yoo, J. & Unser, M. Multi-CryoGAN: reconstruction of continuous conformations in cryo-EM using generative adversarial networks. In European Conference on Computer Vision (ECCV, 2020).

  36. Zhong, E. D., Lerer, A., Davis, J. H. & Berger, B. Exploring generative atomic models in cryo-EM reconstruction. In NeurIPS Workshop on Machine Learning for Structural Biology (MLSB, 2020).

  37. Jin, Q. et al. Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure 22, 496–506 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Harastani, M., Eltsov, M., Leforestier, A. & Jonic, S. Hemnma-3D: cryo electron tomography method based on normal mode analysis to study continuous conformational variability of macromolecular complexes. Front. Mol. Biosci. 8, 663121 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hamitouche, I. & Jonic, S. DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front. Mol. Biosci. 9, 965645 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nashed, Y et al. Heterogeneous reconstruction of deformable atomic models in cryo-em. Preprint at https://doi.org/10.48550/arXiv.2209.15121 (2022).

  41. Scheres, S. H. et al. Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization. Nat. Methods 4, 27–29 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Elmlund, D. & Elmlund, H. Simple: software for ab initio reconstruction of heterogeneous single-particles. J. Struct. Biol. 180, 420–427 (2012).

    Article  PubMed  Google Scholar 

  43. Scheres, S. H. Relion: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Brubaker, M. A., Punjani, A., & Fleet, D. J. Building proteins in a day: efficient 3D molecular reconstruction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3099–3108 (CVPR, 2015).

  45. Ho, C. -M. et al. Bottom-up structural proteomics: cryoEM of protein complexes enriched from the cellular milieu. Nat. Methods 17, 79–85 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Su, C.-C. et al. A ‘build and retrieve’ methodology to simultaneously solve cryo-EM structures of membrane proteins. Nat. Methods 18, 69–75 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Levy, A., Wetzstein, G., Martel, J. N., Poitevin, F. & Zhong, E. D. Amortized inference for heterogeneous reconstruction in cryo-EM. In Advances in Neural Information Processing Systems (NeurIPS, 2022).

  48. Shekarforoush, S., Lindell, D. B., Brubaker, M. A. & Fleet, D. J. Cryospin: improving ab-initio cryo-EM reconstruction with semi-amortized pose inference. In Advances in Neural Information Processing Systems (NeurIPS, 2024).

  49. Tang, G. et al. Eman2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. Castaño-Díez, D., Kudryashev, M., Arheit, M. & Stahlberg, H. Dynamo: a flexible, user-friendly development tool for subtomogram averaging of cryo-EM data in high-performance computing environments. J. Struct. Biol. 187, 139–151 (2012).

    Article  Google Scholar 

  51. Bharat, T. A. & Scheres, S. H. Resolving macromolecular structures from electron cryo-tomography data using subtomogram averaging in relion. Nat. Protoc. 11, 2054–2065 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Harastani, M., Eltsov, M., Leforestier, A. & Jonic, S. TomoFlow: analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow. J. Struct. Biol. 434, 167381 (2022).

    CAS  Google Scholar 

  53. Himes, B. A. & Zhang, P. emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging. Nat. Methods 15, 955–961 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Chen, M. et al. A complete data processing workflow for cryo-ET and subtomogram averaging. Nat. Methods 16, 1161–1168 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Tegunov, D. & Cramer, P. Real-time cryo-electron microscopy data preprocessing with Warp. Nat. Methods 16, 1146–1152 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Powell, B. M. & Davis, J. H. Learning structural heterogeneity from cryo-electron subtomograms with tomoDRGN. Nat. Methods 21, 1525–1536 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhu, J. et al. A minority of final stacks yields superior amplitude in single-particle cryo-EM. Nat. Commun. 14, 7822 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kingma, D. P. & Welling M. Auto-encoding variational Bayes. In International Conference on Learning Representations (ICLR, 2014)

  59. Kinman, L. F., Powell, B. M., Zhong, E. D., Berger, B. & Davis, J. H. Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN. Nat. Protoc. 18, 319–339 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Jeon, M. et al. CryoBench: diverse and challenging datasets for the heterogeneity problem in cryo-EM. In Advances in Neural Information Processing Systems (NeurIPS, 2024).

  61. Jumper, J. et al. Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Vulović, M. et al. Image formation modeling in cryo-electron microscopy. J. Struct. Biol. 183, 19–32 (2013).

    Article  PubMed  Google Scholar 

  66. Tancik, M. et al. Fourier features let networks learn high frequency functions in low dimensional domains. In Advances in Neural Information Processing Systems (NeurIPS, 2020).

  67. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision 770–778 (CVPR, 2016).

  68. Paszke A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS, 2019).

  69. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In International Conference on Learning Representations (ICLR, 2015)

  70. Yershova, A., Jain, S., Lavalle, S. M. & Mitchell, J. C. Generating uniform incremental grids on SO(3) using the hopf fibration. Int. J. Rob. Res. 29, 801–812 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Gorski, K. M. et al. HEALPix: a framework for high-resolution discretization and fast analysis of data distributed on the sphere. Astrophys. J. 622, 759 (2005).

    Article  CAS  Google Scholar 

  72. Grant, T. & Grigorieff, N. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus vp6. eLife 4, e06980 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Bharat, T. A., Russo, C. J., Löwe, J., Passmore, L. A. & Scheres, S. H. Advances in single-particle electron cryomicroscopy structure determination applied to sub-tomogram averaging. Structure 23, 1743–1753 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Pettersen, E. F. et al. Ucsf chimerax: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article  CAS  PubMed  Google Scholar 

  75. Klindt, D. A., Hyvarinen, A., Levy, A., Miolane, N. & Poitevin, F. Towards interpretable cryo-EM: disentangling latent spaces of molecular conformations. Front. Mol. Biosci. 11, 1393564 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhou, Y., Barnes, C., Lu, J., Yang, J. & Li, H. On the continuity of rotation representations in neural networks. In Proc. IEEE/CVF International Conference on Computer Vision 5745–5753 (CVPR, 2019).

  77. Levy, A. et al. CryoAI: amortized inference of poses for ab initio reconstruction of 3D molecular volumes from real cryo-EM images. In European Conference on Computer Vision 540–557 (ECCV, 2022).

  78. Wong, W. et al. Cryo-EM structure of the Plasmodium falciparum 80s ribosome bound to the anti-protozoan drug emetine. eLife 3, e03080 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Burt, A. et al. An image processing pipeline for electron cryo-tomography in RELION-5. FEBS Open Bio 14, 1788–1804 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Levy, A. et al. Input data for the DSL1/SNARE complex in cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14853184 (2025).

  81. Levy, A. et al. Input data for the V-ATPase complex in cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14853225 (2025).

  82. Levy, A. et al. Input data for the mycoplasma pneumoniae 70s ribosome in cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14853246 (2025).

  83. Levy, A. et al. Input synthetic data for 1D motion in cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14853257 (2024).

  84. Levy, A. et al. Input synthetic data for the 80s ribosome in cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14853270 (2025).

  85. Levy, A. et al. Output data for cryoDRGN-AI. Zenodo https://doi.org/10.5281/zenodo.14847271 (2025).

  86. Zhong, E. D. et al. ml-struct-bio/cryodrgn: v3.4.3v3.4.3: making movies, improving filtering interface, and fixes to landscape analysis. Zenodo https://doi.org/10.5281/zenodo.14538433 (2024).

Download references

关于《CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets》的评论


暂无评论

发表评论

摘要

The provided text is a collection of citations and references from scientific papers and datasets related to cryo-electron microscopy (cryo-EM) and molecular biology, specifically focusing on the use of artificial intelligence (AI) techniques for analyzing single-particle cryo-EM data. Below is an organized summary highlighting key aspects: ### Key Papers and Datasets 1. **CryoDRGN: AI in Cryo-EM** - **Paper:** Levy et al., "Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN," *Nature Protocols* (2023). This paper details a method to uncover structural ensembles from single-particle cryo-EM data using AI. - **Datasets:** - DSL1/SNARE Complex - Zenodo link: https://doi.org/10.5281/zenodo.14853184 - V-ATPase Complex - Zenodo link: https://doi.org/10.5281/zenodo.14853225 - Mycoplasma Pneumoniae 70s Ribosome - Zenodo link: https://doi.org/10.5281/zenodo.14853246 2. **CryoBench Dataset** - **Paper:** Jeon et al., "CryoBench: diverse and challenging datasets for the heterogeneity problem in cryo-EM," *Advances in Neural Information Processing Systems* (NeurIPS, 2024). - This dataset provides a collection of complex structures to test algorithms dealing with structural heterogeneity. 3. **Synthetic Datasets** - **Paper:** Levy et al., "CryoDRGN-AI: Input synthetic data for 1D motion," *Zenodo* (2024). - Zenodo link: https://doi.org/10.5281/zenodo.14853257 - **Paper:** Levy et al., "CryoDRGN-AI: Input synthetic data for the 80s ribosome," *Zenodo* (2025). - Zenodo link: https://doi.org/10.5281/zenodo.14853270 ### Key AI Techniques and Frameworks - **Auto-Encoding Variational Bayes (AEVB)** - **Paper:** Kingma & Welling, "Auto-encoding variational Bayes," *International Conference on Learning Representations* (ICLR, 2014). - **CryoDRGN-AI** - **Version:** ml-struct-bio/cryodrgn: v3.4.3 - Zenodo link: https://doi.org/10.5281/zenodo.14538433 ### Key Software and Libraries - **RELION** - An image processing pipeline for electron cryo-tomography. - **ChimeraX** - A structure visualization tool developed by the UCSF. ### Additional Resources - **Hopf Fibration** (for generating uniform incremental grids on SO(3)) - **Paper:** Yershova et al., "Generating uniform incremental grids on SO(3) using the Hopf fibration," *International Journal of Robotics Research* (2010). These references cover various aspects of AI applications in cryo-EM, from data processing and analysis to synthetic dataset generation for testing algorithms.