Topaz-Denoise: general deep denoising models for cryoEM and cryoET

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Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology

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Paper Summary

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Topaz-Denoise helps see through the noise to identify previously unseen protein conformations and reduce microscope exposure times!

This paper presents Topaz-Denoise, a general deep-learning method and trained models for removing noise from cryoEM micrographs and cryoET tomograms. The authors demonstrate that denoising with Topaz allows for the identification of particles previously unseen due to low SNR, thus allowing for solving protein structures with more complete particle orientations and identifying new conformations, as well as substantially reducing electron dose and microscope exposure time without sacrificing data quality.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Hallucination of information

The general denoising model does not perform well when doing de novo ab initio reconstruction, thus there is some hallucination of information from the training datasets being imprinted on the denoised data, and thus the general models may not be ideal for 3D refinement where this may become a confounder.

Rating Explanation

This paper presents a useful method for improving interpretability of low SNR cryoEM and cryoET data that, if used properly, can lead to new discoveries.

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Topic Hierarchy

Domain:

Life Sciences

Field:

Biochemistry, Genetics and Molecular Biology

Subfield:

Structural Biology

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Topaz-Denoise: general deep denoising models for cryoEM and cryoET

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