ArticleNature communications2020
Topaz-Denoise: general deep denoising models for cryoEM and cryoET.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 326 papers.
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Who cites it
326 citing papers in PubMed.
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- Interactions of outer membrane lipoproteins Pseudomonas aeruginosa PA3214 and Escherichia coli PqiC with their MCE protein binding partners, PA3213 and PqiB.The Journal of biological chemistry · 2026Article
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- Structural origins of species-specific differences in TRPV2 activation.Nature communications · 2026Article
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- A ubiquitin chain-feeding mechanism for BRCA1-A.Nature communications · 2026Article
- Structural insights into a substrate translocation pathway revealed by the RND efflux pump complex MexJK fromScience advances · 2026Article
- De novo design of potent CRISPR-Cas13 inhibitors.Nature chemical biology · 2026Article
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- Structure-based recasting of a mammalian DNA transpososome as an obligate heterodimer.bioRxiv : the preprint server for biology · 2026Article
- Structural dynamics of RNA polymerase II throughout the nucleotide addition cycle.Nature communications · 2026Article
266 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.
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