WikiSysop: Created page with "== Citation == P. N. Shah, R. Sanchez-Garcia, and D. I. Stuart, “TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms,” Biological Crystallography, vol. 81, pp. 63–76, 2025. == Abstract == Cryo-electron tomography is a rapidly developing field for studying macromolecular complexes in their native environments and has the potential to revolutionize our understanding of protein function. However, fast and accurate id..."

2025-04-10T08:46:25Z

Created page with "== Citation == P. N. Shah, R. Sanchez-Garcia, and D. I. Stuart, “TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms,” Biological Crystallography, vol. 81, pp. 63–76, 2025. == Abstract == Cryo-electron tomography is a rapidly developing field for studying macromolecular complexes in their native environments and has the potential to revolutionize our understanding of protein function. However, fast and accurate id..."

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== Citation ==

P. N. Shah, R. Sanchez-Garcia, and D. I. Stuart, “TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms,” Biological Crystallography, vol. 81, pp. 63–76, 2025.

== Abstract ==

Cryo-electron tomography is a rapidly developing field for studying macromolecular
complexes in their native environments and has the potential to
revolutionize our understanding of protein function. However, fast and accurate
identification of particles in cryo-tomograms is challenging and represents a
significant bottleneck in downstream processes such as subtomogram averaging.
Here, we present tomoCPT (Tomogram Centroid Prediction Tool), a transformerbased
solution that reformulates particle detection as a centroid-prediction task
using Gaussian labels. Our approach, which is built upon the SwinUNETR
architecture, demonstrates superior performance compared with both conventional
binary labelling strategies and template matching. We show that
tomoCPT effectively generalizes to novel particle types through zero-shot
inference and can be significantly enhanced through fine-tuning with limited
data. The efficacy of tomoCPT is validated using three case studies: apoferritin,
achieving a resolution of 3.0 A ˚ compared with 3.3 A ˚ using template matching,
SARS-CoV-2 spike proteins on cell surfaces, yielding an 18.3 A ˚ resolution map
where template matching proved unsuccessful, and rubisco molecules within
carboxysomes, reaching 8.0 A ˚ resolution. These results demonstrate the ability
of tomoCPT to handle varied scenarios, including densely packed environments
and membrane-bound proteins. The implementation of the tool as a command line
program, coupled with its minimal data requirements for fine-tuning, makes
it a practical solution for high-throughput cryo-ET data-processing workflows.

== Keywords ==

== Links ==

https://journals.iucr.org/d/issues/2025/02/00/sor5001/index.html

== Related software ==

== Related methods ==

== Comments ==

2025Shah TomoCPT - Revision history