Overcoming the Preferred Orientation Problem in cryoEM with Self-Supervised Deep-Learning. Liu, Y., Fan, H., Hu, J. J., & Zhou, Z. H. April, 2024. doi abstract bibtex While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called ``preferred'' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning-based software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, $β$-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred orientation problem.
@misc{liuOvercomingPreferredOrientation2024,
title = {Overcoming the Preferred Orientation Problem in {{cryoEM}} with Self-Supervised Deep-Learning},
author = {Liu, Yun-Tao and Fan, Hongcheng and Hu, Jason J. and Zhou, Z. Hong},
year = {2024},
month = apr,
primaryclass = {New Results},
pages = {2024.04.11.588921},
publisher = {bioRxiv},
doi = {10.1101/2024.04.11.588921},
urldate = {2024-06-13},
abstract = {While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called ``preferred'' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning-based software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, {$\beta$}-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred orientation problem.},
archiveprefix = {bioRxiv},
chapter = {New Results},
copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
langid = {english},
file = {C:\Users\shervinnia\Zotero\storage\6M8U6YJ2\Liu et al. - 2024 - Overcoming the preferred orientation problem in cr.pdf}
}
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H."],"bibdata":{"bibtype":"misc","type":"misc","title":"Overcoming the Preferred Orientation Problem in cryoEM with Self-Supervised Deep-Learning","author":[{"propositions":[],"lastnames":["Liu"],"firstnames":["Yun-Tao"],"suffixes":[]},{"propositions":[],"lastnames":["Fan"],"firstnames":["Hongcheng"],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["Jason","J."],"suffixes":[]},{"propositions":[],"lastnames":["Zhou"],"firstnames":["Z.","Hong"],"suffixes":[]}],"year":"2024","month":"April","primaryclass":"New Results","pages":"2024.04.11.588921","publisher":"bioRxiv","doi":"10.1101/2024.04.11.588921","urldate":"2024-06-13","abstract":"While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called ``preferred'' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning-based software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, $β$-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. 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Hong},\n year = {2024},\n month = apr,\n primaryclass = {New Results},\n pages = {2024.04.11.588921},\n publisher = {bioRxiv},\n doi = {10.1101/2024.04.11.588921},\n urldate = {2024-06-13},\n abstract = {While advances in single-particle cryoEM have enabled the structural determination of macromolecular complexes at atomic resolution, particle orientation bias (the so-called ``preferred'' orientation problem) remains a complication for most specimens. Existing solutions have relied on biochemical and physical strategies applied to the specimen and are often complex and challenging. Here, we develop spIsoNet, an end-to-end self-supervised deep-learning-based software to address the preferred orientation problem. Using preferred-orientation views to recover molecular information in under-sampled views, spIsoNet improves both angular isotropy and particle alignment accuracy during 3D reconstruction. We demonstrate spIsoNet's capability of generating near-isotropic reconstructions from representative biological systems with limited views, including ribosomes, {$\\beta$}-galactosidases, and a previously intractable hemagglutinin trimer dataset. spIsoNet can also be generalized to improve map isotropy and particle alignment of preferentially oriented molecules in subtomogram averaging. Therefore, without additional specimen-preparation procedures, spIsoNet provides a general computational solution to the preferred orientation problem.},\n archiveprefix = {bioRxiv},\n chapter = {New Results},\n copyright = {{\\copyright} 2024, Posted by Cold Spring Harbor Laboratory. 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