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Cryo-electron tomography allows the imaging of macromolecular complexes in near living conditions. To enhance the nominal resolution of a structure it is necessary to align and average individual subtomograms each containing identical complexes. However, if the sample of complexes is heterogeneous, it is necessary to first classify subtomograms into groups of identical complexes. This task becomes challenging when tomograms contain mixtures of unknown complexes extracted from a crowded environment. Two main challenges must be overcomed: First, classification of subtomograms must be performed without knowledge of template structures. However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms. Second, subtomograms extracted from crowded cellular environments, contain often fragments of other structures besides the target complex. However, alignment methods generally assume that each subtomogram only contains one complex. Automatic methods are needed to identify the target complexes in a subtomogram even when its shape is unknown.

bioinformatics

Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms