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Abstract
Cryogenic-electron tomography (cryo-ET) offers detailed visualization of cellular environments, but analyzing the vast information within densely packed volumes remains challenging. Accurate macromolecule localization for subtomogram averaging (STA) is crucial but hindered by low signal-to-noise ratios and crowding. Existing methods are either error-prone or require manual training data annotation. TomoTwin, an open-source deep metric learning model, addresses this by embedding tomograms in a high-dimensional space that separates macromolecules based on their 3D structure. This allows de novo protein identification without manual training data or retraining for new proteins.
Publisher
Nature Methods
Published On
May 15, 2023
Authors
Gavin Rice, Thorsten Wagner, Markus Stabrin, Oleg Sitsel, Daniel Prumbaum, Stefan Raunser
Tags
Cryogenic-electron tomography
Deep metric learning
TomoTwin
Subtomogram averaging
Protein identification
Macromolecule localization
3D structure

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