Dataset Information

Gradient-based high precision alignment of cryo-electron subtomograms.

ABSTRACT: Whole cell cryo-electron tomography emerges as an important component for structural system biology approaches. It allows the localization and structural characterization of macromolecular complexes in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties one can align and average a large set of subtomograms. Existing alignment methods are mostly based on an exhaustive scanning and sampling of all but discrete relative rotations and translations of one subtomogram with respect to the other. In this paper, we propose a gradient-guided alignment method based on two subtomogram similarity measures. We also propose a stochastic parallel optimization that increases significantly the efficiency for the simultaneous refinement of a set of alignment candidates. Results on simulated data of model complexes and experimental structures of protein complexes show that even for highly distorted subtomograms and with only a small number of very sparsely distributed initial alignment seeds, our method can accurately recover true transformations with a significantly higher precision than scanning based alignment methods.

SUBMITTER: Xu M

PROVIDER: S-EPMC4108348 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Gradient-based high precision alignment of cryo-electron subtomograms.

Xu Min M Alber Frank F

IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology 20110101

Whole cell cryo-electron tomography emerges as an important component for structural system biology approaches. It allows the localization and structural characterization of macromolecular complexes in near living conditions. However, the method is hampered by low resolution, missing data and low signal-to-noise ratio (SNR). To overcome some of these difficulties one can align and average a large set of subtomograms. Existing alignment methods are mostly based on an exhaustive scanning and sampl ...[more]

PMID: 25068871

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Project description:MotivationCryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms.ResultsIn this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with 'warp' modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data.Availabilityand implementationhttps://github.com/xulabs/aitom.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Gradient-based high precision alignment of cryo-electron subtomograms.

Publications

Gradient-based high precision alignment of cryo-electron subtomograms.

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