WikiSysop: Created page with "== Citation == M. C. DiIorio and A. W. Kulczyk, “Novel artificial intelligence-based approaches for ab initio structure determination and atomic model building for cryo-electron microscopy,” Micromachines, vol. 14, no. 9, p. 1674, 2023. == Abstract == Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. Howe..."

2024-12-27T07:58:14Z

Created page with "== Citation == M. C. DiIorio and A. W. Kulczyk, “Novel artificial intelligence-based approaches for ab initio structure determination and atomic model building for cryo-electron microscopy,” Micromachines, vol. 14, no. 9, p. 1674, 2023. == Abstract == Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. Howe..."

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== Citation ==

M. C. DiIorio and A. W. Kulczyk, “Novel artificial intelligence-based approaches for ab initio structure determination and atomic model building for cryo-electron microscopy,” Micromachines, vol. 14, no. 9, p. 1674, 2023.

== Abstract ==

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for
near-atomic structure determination, shedding light on the important molecular mechanisms
of biological macromolecules. However, the inherent dynamics and structural variability of
biological complexes coupled with the large amount of experimental images generated by a
cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction
and atomic model building remain major bottlenecks that demand substantial computational
resources and manual intervention. Approaches utilizing recent innovations in artificial
intelligence (AI) technology, particularly deep learning, have the potential to overcome the
limitations that cannot be adequately addressed by traditional image processing approaches.
Here, we review newly proposed AI-based methods for ab initio volume generation,
heterogeneous 3D reconstruction, and atomic model building. We highlight the
advancements made by the implementation of AI methods, as well as discuss remaining
limitations and areas for future development.

== Keywords ==

== Links ==

https://www.mdpi.com/2072-666X/14/9/1674

== Related software ==

== Related methods ==

== Comments ==

2023DiIorio AbInitio - Revision history