WikiSysop: Created page with "== Citation == D. Kimanius et al., “Data-driven regularization lowers the size barrier of cryo-EM structure determination,” Nature Methods, pp. 1–6, 2024. == Abstract == Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is..."

2025-02-04T06:44:57Z

Created page with "== Citation == D. Kimanius et al., “Data-driven regularization lowers the size barrier of cryo-EM structure determination,” Nature Methods, pp. 1–6, 2024. == Abstract == Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is..."

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== Citation ==

D. Kimanius et al., “Data-driven regularization lowers the size barrier of cryo-EM structure determination,” Nature Methods, pp. 1–6, 2024.

== Abstract ==

Macromolecular structure determination by electron cryo-microscopy
(cryo-EM) is limited by the alignment of noisy images of individual particles.
Because smaller particles have weaker signals, alignment errors impose
size limitations on its applicability. Here, we explore how image alignment
is improved by the application of deep learning to exploit prior knowledge
about biological macromolecular structures that would otherwise be
difficult to express mathematically. We train a denoising convolutional
neural network on pairs of half-set reconstructions from the electron
microscopy data bank (EMDB) and use this denoiser as an alternative to a
commonly used smoothness prior. We demonstrate that this approach,
which we call Blush regularization, yields better reconstructions than do
existing algorithms, in particular for data with low signal-to-noise ratios. The
reconstruction of a protein–nucleic acid complex with a molecular weight
of 40 kDa, which was previously intractable, illustrates that denoising neural
networks will expand the applicability of cryo-EM structure determination
for a wide range of biological macromolecules.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41592-024-02304-8

== Related software ==

== Related methods ==

== Comments ==

2024Kimanius Blush - Revision history