Deep generative modeling for volume reconstruction in cryo-electron microscopy

Claire Donnat, Axel Levy, Frédéric Poitevin, Ellen D. Zhong, Nina Miolane

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number107920
JournalJournal of Structural Biology
Volume214
Issue number4
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Structural Biology

Keywords

  • cryoEM
  • Deep neural networks
  • Generative models
  • High-resolution volume reconstruction

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