Development of computational methods for exploration of cryo-electron tomograms

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Cryo-electron tomography (CET) has the unique ability to image macromolecular complexes in their native environment in three dimensions, making it an important imaging modality in modern structural biology. The three-dimensional (3-D) density of the frozen-hydrated sample is approximated from its projections, which are acquired from the sequentially tilted specimen using a transmission electron microscope (TEM). Here, we suggest developing computational methods that improve the accuracy of 3-D reconstructions derived from cryo-electron tomographic data. Firstly, we will implement a novel algorithm for registration of the two-dimensional (2-D) TEM images to a common coordinate system. In particular, this approach focuses on smaller areas of the specimen volume rather than the whole imaged area. This approach allows compensation for beam-induced alteration of the sample, currently arguably the biggest challenge for obtaining high-resolution from CET. Since the registration algorithm does not rely on fiducial markers it will be of particular importance for the analysis of samples prepared using a focused ion beam and it enables further automation of tomographic reconstruction. Secondly, we will implement a novel iterative method for reconstruction of the 3-D volume from the registered 2-D images. Three features suggest that the methodology will yield substantially more accurate reconstructions than approaches currently used: (i) nonuniform Fourier transforms allows highly accurate interpolation and the use of a metric that considers the geometry of the experiment appropriately; (ii) regularization avoids overfitting to noise for data with low signal-to-noise ratios as in CET; (iii) real space constraints on the shape of the specimen allow partial restoration of structural data that are inaccessible in the experiment (missing wedge). Thirdly, we will integrate this reconstruction methodology into existing frameworks for the statistical analysis of subtomograms depicting single copies of specific macromolecular complexes. Subtomogram analysis involves coherent alignment and averaging to obtain averaged densities with much higher resolution than the individual noisy tomograms, as well as classification of subtomograms according to structural differences of the depicted complexes. Specifically, we will use distinct 3-D reconstructions for alignment/classification and averaging. Finally, the developed methodology will be extensively documented and suitable tutorials will be compiled to maximize the value of the algorithms in the scientific community. In summary, the methodology developed in this proposal will enable much more detailed insights into the structures of complexes in their native settings as well as their conformational changes.