Towards sub-millisecond cryo-EM grid preparation

Klebl, David P.; Kay, Robert W.; Sobott, Frank; Kapur, Nikil; Muench, Stephen P.

doi:10.1039/d2fd00079b

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…Biochemical solutions include occupying the air-water interface with detergents 12,14,18 or chemically modifying the protein specimen 19 , but are specimen-specific, typically require exhaustive screening, and may be detrimental to specimen quality. Physical approaches to alleviating the preferred orientation problem abound, including coating cryoEM grids with support films 11,[20][21][22][23][24] , using time-resolved vitrification devices [25][26][27][28][29] , collecting data from thicker ice regions 30 , and tilting the grids during image acquisition 10,31 . However, these physical methods may be labor-intensive, expensive, and often have unintended consequences, such as higher background noise in images and a different type of preferred orientation induced by a support film-water interface.…”

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confidence: 99%

Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning

Liu,

Fan,

et al. 2024

Preprint

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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.

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