Estimation of the 3D Variance-Covariance Map in Cryo-Electron Microscopy
Single-particle cryo-electron microscopy (cryo-EM) has recently become an important tool for the study of macromolecular structures at high resolution. One challenge, however, is that the data collected are intrinsically heterogeneous, which limits the resolution that can be potentially achieved. One way to address the heterogeneity problem is via computation of the covariance matrix, which captures the correlation between every pair of voxels, thereby revealing the variability and co-variability of the underlying structures, in terms of spatial location and the type of structural change. Specifically, we propose an iterative approach in the image domain to the estimation of the covariance matrix from cryo-EM single-particle images. Although this type of approach is commonly perceived as being slow, it has two important mitigating advantages: constraints on the solution can be easily imposed; and the solution domain can be tailored to have arbitrary shape and size, thereby considerably reducing the number of unknowns, which grows quadratically with the size of the volume. We obtained encouraging results on an experimental data set with 29,000 projections of a 43S ribosomal pre-initiation complex.
- Finding_the_3D_variance8_CU.docx application/vnd.openxmlformats-officedocument.wordprocessingml.document 1.58 MB Download File
- Published In
- Methods in Enzymology
- 295 - 320
- Academic Units
- Biochemistry and Molecular Biophysics