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Deep generative modeling for volume reconstruction in cryo-electron microscopy.

ABSTRACT: Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.

SUBMITTER: Donnat C

PROVIDER: S-EPMC10437207 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Deep generative modeling for volume reconstruction in cryo-electron microscopy.

Donnat Claire C Levy Axel A Poitevin Frédéric F Zhong Ellen D ED Miolane Nina N

Journal of structural biology 20221108 4

Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such ...[more]

PMID: 36356882

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