WikiSysop: Created page with "== Citation == Levy, A., Raghu, R., Feathers, J.R., Grzadkowski, M., Poitevin, F., Johnston, J.D., Vallese, F., Clarke, O.B., Wetzstein, G. and Zhong, E.D. 2025. CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nature Methods. (2025), 1–9. == Abstract == Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-elec..."

2025-08-25T08:12:45Z

Created page with "== Citation == Levy, A., Raghu, R., Feathers, J.R., Grzadkowski, M., Poitevin, F., Johnston, J.D., Vallese, F., Clarke, O.B., Wetzstein, G. and Zhong, E.D. 2025. CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nature Methods. (2025), 1–9. == Abstract == Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-elec..."

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== Citation ==

Levy, A., Raghu, R., Feathers, J.R., Grzadkowski, M., Poitevin, F., Johnston, J.D., Vallese, F., Clarke, O.B., Wetzstein, G. and Zhong, E.D. 2025. CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets. Nature Methods. (2025), 1–9.

== Abstract ==

Proteins and other biomolecules form dynamic macromolecular machines
that are tightly orchestrated to move, bind and perform chemistry.
Cryo-electron microscopy and cryo-electron tomography can access
the intrinsic heterogeneity of these complexes and are therefore key
tools for understanding their function. However, three-dimensional
reconstruction of the collected imaging data presents a challenging
computational problem, especially without any starting information,
a setting termed ab initio reconstruction. Here we introduce cryoDRGN-AI,
a method leveraging an expressive neural representation and combining
an exhaustive search strategy with gradient-based optimization to
process challenging heterogeneous datasets. Using cryoDRGN-AI,
we reveal new conformational states in large datasets, reconstruct
previously unresolved motions from unfiltered datasets and demonstrate
ab initio reconstruction of biomolecular complexes from in situ data.
With this expressive and scalable model for structure determination,
we hope to unlock the full potential of cryo-electron microscopy and
cryo-electron tomography as a high-throughput tool for structural biology
and discovery.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41592-025-02720-4

== Related software ==

== Related methods ==

== Comments ==

2025Levy CryoDRGNAI - Revision history