The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.

Centro Nacional de Biotecnologia CSIC C Darwin 3 28049 Cantoblanco Madrid Spain

Faculty of Informatics Masaryk University Botanická 68a 60200 Brno Czech Republic

Institute of Computer Science Masaryk University Botanická 68a 60200 Brno Czech Republic

The Department of Statistics and Data Science Yale University New Haven CT USA

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Carroni M, Saibil HR. Cryo electron microscopy to determine the structure of macromolecular complexes. Methods. 2016;95:78–85. doi: 10.1016/j.ymeth.2015.11.023. PubMed DOI PMC

Serna M. Hands on methods for high resolution cryo-electron microscopy structures of heterogeneous macromolecular complexes. Front. Mol. Biosci. 2019;6:33. doi: 10.3389/fmolb.2019.00033. PubMed DOI PMC

Gomez-Blanco J, Kaur S, Strauss M, Vargas J. Hierarchical autoclassification of cryo-EM samples and macromolecular energy landscape determination. Comput. Methods Prog. Biomed. 2022;216:106673. doi: 10.1016/j.cmpb.2022.106673. PubMed DOI

Jin Q, et al. Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure. 2014;22:496–506. doi: 10.1016/j.str.2014.01.004. PubMed DOI

Zhong ED, Bepler T, Berger B, Davis JH. CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat. Methods. 2021;18:176–185. doi: 10.1038/s41592-020-01049-4. PubMed DOI PMC

Ludtke SJ, Muyuan C. Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. Nat. Methods. 2021;18:930–936. doi: 10.1038/s41592-021-01220-5. PubMed DOI PMC

Frank J, Abbas O. Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM. Methods. 2016;100:61–67. doi: 10.1016/j.ymeth.2016.02.007. PubMed DOI PMC

A. Punjani, A. & Fleet, D. J. 3D flexible refinement: structure and motion of flexible proteins from Cryo-EM. bioRxiv, https://www.biorxiv.org/content/10.1101/2021.04.22.440893v1 (2021). PubMed DOI PMC

Lederman, R. R., Anden, J. & Singer, A. Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM. arXiv, https://arxiv.org/abs/1907.01589 (2020). PubMed PMC

Herreros D, et al. Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials. IUCrJ. 2021;8:992–1005. doi: 10.1107/S2052252521008903. PubMed DOI PMC

Wong W, et al. Cryo-EM structure of the 80S ribosome bound to the anti-protozoan drug emetine. eLife. 2014;3:e03080. doi: 10.7554/eLife.03080. PubMed DOI PMC

de la Rosa-Trevín JM, et al. Scipion: a software framework toward integration, reproducibility and validation in 3D electron microscopy. J. Struct. Biol. 2016;195:93–99. doi: 10.1016/j.jsb.2016.04.010. PubMed DOI

McInnes L, Healy J, Saul N, Großberger L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 2018;3:861. doi: 10.21105/joss.00861. DOI

Pettersen EF, et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 2021;30:70–82. doi: 10.1002/pro.3943. PubMed DOI PMC

Punjani A, Rubinstein JL, Fleet DJ, Brubaker MA. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods. 2017;14:290–296. doi: 10.1038/nmeth.4169. PubMed DOI

Plaschka C, Lin PC, Nagai K. Structure of a pre-catalytic spliceosome. Nature. 2017;564:617–621. doi: 10.1038/nature22799. PubMed DOI PMC

Melero R, et al. Continuous flexibility analysis of SARS-CoV-2 spike prefusion structures. IUCrJ. 2020;7:1059–1069. doi: 10.1107/S2052252520012725. PubMed DOI PMC

Jolliffe I, Cadima J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 2016;374:20150202. PubMed PMC

Sorzano COS, et al. On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy. Acta Crystallogr. Sect. D. 2022;78:410–423. doi: 10.1107/S2059798322001978. PubMed DOI PMC

Sorzano COS, et al. A survey of the use of iterative reconstruction algorithms in electron microscopy. BioMed. Res. Int. 2017;2017:1–17. doi: 10.1155/2017/6482567. PubMed DOI PMC

Herreros, D. Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials 10.5281/zenodo.7334391, (2022). PubMed PMC

de la Rosa-Trevín JM, et al. Xmipp 3.0: an improved software suite for image processing in electron microscopy. J. Struct. Biol. 2013;184:321–328. doi: 10.1016/j.jsb.2013.09.015. PubMed DOI

Heymann JB. Guidelines for using Bsoft for high resolution reconstruction and validation of biomolecular structures from electron micrographs. Protein Sci. 2018;27:159–171. doi: 10.1002/pro.3293. PubMed DOI PMC

Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials