Optimal and fast rotational alignment of volumes with missing data in Fourier space

AbstractElectron tomography of intact cells has the potential to reveal the entire cellular content at a resolution corresponding to individual macromolecular complexes. Characterization of macromolecular complexes in tomograms is nevertheless an extremely challenging task due to the high level of noise, and due to the limited tilt angle that results in missing data in Fourier space. By identifying particles of the same type and averaging their 3D volumes, it is possible to obtain a structure at a more useful resolution for biological interpretation. Currently, classification and averaging of sub-tomograms is limited by the speed of computational methods that optimize alignment between two sub-tomographic volumes. The alignment optimization is hampered by the fact that the missing data in Fourier space has to be taken into account during the rotational search. A similar problem appears in single particle electron microscopy where the random conical tilt procedure may require averaging of volumes with a missing cone in Fourier space. We present a fast implementation of a method guaranteed to find an optimal rotational alignment that maximizes the constrained cross-correlation function (cCCF) computed over the actual overlap of data in Fourier space

High-throughput isolation and characterization of untagged membrane protein complexes: outer membrane complexes of Desulfovibrio vulgaris.

Cell membranes represent the "front line" of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a "tagless" process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium Desulfovibrio vulgaris. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein-protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a D. vulgaris outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

Local Structural Comparison with Global Structural Descriptors

Protein sequence and structure are fundamental objects in computational biology. The sequence comparison problem has been widely addressed, resulting in a spectrum of algorithms ranging from the sensitive ones such as profile-HMM to fast ones such as k-mer indexing, arguably culminated in BLAST, where a practical balance of sensitivity and speed is achieved. Current structural comparison methods achieve results generally satisfactory to biologists. However, fast and accurate data base searches, in spirit to BLAST, are not possible due to the nature of the structural comparison methodology. Similarity of protein structures is typically measured at the residue level via structural alignment, whose goal is to find a 3D transformation that brings into correspondence the largest number of atoms. The quality of a 3D superposition is typically measured by the number of matched C-alpha atoms and their RMSD. The exact solution for the pairwise structural alignment is computationally expensive [1]. Therefore, heuristic approaches have been developed to find a good solution efficiently (for a review see [3]). An alternative approach to assess protein structure similarity is based on global topological properties, for example, by means of writhe number [2] and Gauss integrals (GIs) [5], or by mean