Visualizing the transfer-messenger RNA as the ribosome resumes translation

Bacterial ribosomes that are stalled on mRNAs lacking a stop codon can be rescued by a process called ‘transtranslation' that involves the ribonucleoprotein complex tmRNA–SmpB. This cryo-EM study, and the copublished study by Weis et al, reveal how translation on tmRNA is resume

Sparse representations for limited data tomography

In limited data tomography, with applications such as electron microscopy and medical imaging, the scanning views are within an angular range that is often both limited and sparsely sampled. In these situations, standard algorithms produce reconstructions with notorious artifacts. We show in this paper that a sparsity image representation principle, based on learning dictionaries for sparse representations of image patches, leads to significantly improved reconstructions of the unknown density from its limited angle projections. The presentation of the underlying framework is complemented with illustrative results on artificial and real data

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