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TitleTopaz-Denoise: general deep denoising models for cryoEM and cryoET.
Journal, issue, pagesNat Commun, Vol. 11, Issue 1, Page 5208, Year 2020
Publish dateOct 15, 2020
AuthorsTristan Bepler / Kotaro Kelley / Alex J Noble / Bonnie Berger /
PubMed AbstractCryo-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 ...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.
External linksNat Commun / PubMed:33060581 / PubMed Central
MethodsEM (single particle)
Resolution2.9 - 12.54 Å
Structure data

EMDB-22052:
Apoferritin short exposure 3D reconstruction with 10% total dose
Method: EM (single particle) / Resolution: 3.35 Å

EMDB-22053:
Apoferritin short exposure 3D reconstruction with 25% total dose
Method: EM (single particle) / Resolution: 2.9 Å

EMDB-22056:
Apoferritin short exposure 3D reconstruction with 50% total dose
Method: EM (single particle) / Resolution: 3.0 Å

EMDB-22057:
Apoferritin short exposure 3D reconstruction with 75% total dose
Method: EM (single particle) / Resolution: 3.13 Å

EMDB-22058:
Apoferritin short exposure 3D reconstruction with 100% total dose
Method: EM (single particle) / Resolution: 3.13 Å

EMDB-22059:
Clustered protocadherin partially open conformation
Method: EM (single particle) / Resolution: 12.54 Å

EMDB-22060:
Clustered protocadherin closed conformation
Method: EM (single particle) / Resolution: 11.51 Å

Source
  • Equus caballus (horse)
  • Mus musculus (house mouse)

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