Novel Methods for Rapid Comparison and Multimeric Protein Complex Fitting for Low-Resolution Electron Microscopy Data

Lee Sael, Juan Esquivel-Rodriguez, Daisuke Kihara
2011 Biophysical Journal  
representative for the class. The traditional cross correlation method searches a large space of rotations and translations since information about the alignment is not known a priori. Furthermore, that method requires an initial reference image to be provided by the user. In contrast, in our new version of the cross correlation method, the size of the search space is reduced by preprocessing class images as described below, and this peprocessing step circumvents the need for a reference image.
more » ... During preprocessing, the centers of mass and the principal axes of images within a class are aligned, resulting in a blurred version of the underlying image. This blurry image is then used in place of an initial reference image. Even though the initial alignment is coarse, the statistics of the resulting misalignment can be estimated well based on the ergodic properties of the additive background noise. Using the statistical properties of the misalignments, a targeted search within the set of all translations and rotations of images is performed, resulting in reduced computational time and increased alignment accuracy. Using synthetic data, we compare the new method to both the classical cross correlation approach and the maximum likelihood method, and demonstrate the improvement in performance that results when using our method. Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The 3DZD has been successfully applied for global protein surface shape comparison, local pocket shape comparison, protein docking prediction, and rapid small ligand molecule search. Here, we apply the 3DZD for comparing low-resolution structure data from the electron microscopy (EM). Two applications are presented. First, we use the 3DZD for rapid comparison for an EM density map of a protein structure to a database of EM data. We examined EM maps of varying resolutions and found that the method has good performance in identifying the structures of the same fold even for EM maps at a resolution of 15 Angstroms (Sael, Kihara , BMC Bioinformatics, in press). Next, we applied the 3DZD for fitting multiple component proteins into an EM map. The method integrates a multiple protein docking procedure and the 3DZD, which compares surface shape of docking conformation to the EM map. The multiple docking is performed by the Multi-LZerD algorithm, which starts by computing pairwise docking prediction of component chains by using the LZerD docking program, which our group have developed recently (Venkatraman et al., BMC Bioinformatics, 2010). Multi-LZerD combines the pairwise docking results generating a couple of hundreds solutions. Then the fitness of the multiple docking decoys and the EM map is quantified by using the 3DZD. Overall, we show that the 3DZD is powerful in comparing low-resolution structure data for comparison and multiple-docking guided by the low-resolution data. 1751-Pos Board B661 Helex: an Evolutionary Tabu Search Strategy for the Identification of Helical Regions in cryo-Electron Microscopy Reconstructions Mirabela Rusu, Stefan Birmanns. Cryo-electron microscopy (cryo-EM) enables the imaging of macromolecular complexes in near-native environments at resolutions that in many instances approach atomic level of detail. Already covering 9.8% of the EBI database for cryo-EM reconstructions, the maps at intermediate-to high-resolution (better than 8A) potentially allow the identification of secondary structure elements. Especially, alpha helices which frequently show consistent patterns in volumetric maps, may be annotated using pattern matching methods. Previously introduced approaches predict secondary structure elements in cryo-EM datasets by applying multi-step heuristics to characterize the geometric features of the volumetric data. Here, we introduce helex (Helix Extractor)a novel technique for the identification of helical regions in cryo-EM data sets. Helex is a hybrid optimization technique that combines a genetic algorithm, a tabu-search strategy, and a one-dimensional iterative optimization to locate and characterize helical regions. Our method takes advantage of the stochastic nature of genetic algorithms to identify optimal placements for a template helix. These placements are then used to characterize the length of the helical region, using an adaptive one-dimensional search that allows suboptimal steps during the optimization. Moreover, the tabu-search strategy prevents further exploration of already characterized helical regions. The method was extensively evaluated using maps at various resolutions, sampling rates and system complexity. Helex reliably identified medium to large helices (> 6 amino acids) in both synthetic and experimental test cases, placing them frequently with root mean square deviations of 1A or less from the correct helical axes. Helex is distributed in our molecular modeling software Sculptor, freely available at http://sculptor.biomachina.org. 1752-Pos Board B662 Scanning Transmission Electron Microscopy of Eukaryotic Cells in Liquid Niels de Jonge, Madeline J. Dukes, Elizabeth A. Ring, Diana B. Peckys. We have recently introduced liquid scanning transmission electron microscopy (STEM) [1-3], a novel electron microscopy technique for the imaging of whole cells in liquid. Eukaryotic cells in liquid were placed in a microfluidic chamber with a thickness of~5 mm enclosed between two ultra-thin electron-transparent windows. Images were obtained by scanning a focused electron beam over the sample and detecting the elastically scattered electrons with an annular dark field detector. On account of the atomic number (Z) contrast of the STEM, nanoparticles of a high-Z material, e.g. gold, can be detected within the background signal produced by a micrometers-thick layer of a low-Z liquid, e.g. water, or cellular material. Nanoparticles specifically attached to proteins can be used to study protein distributions in whole cells in liquid, similar as proteins tagged with fluorescent labels can be used to study cellular function with fluorescence microscopy. COS7 fibroblast cells were labeled with gold nanoparticles conjugated with epidermal growth factor (EGF). Intact fixed cells in liquid were imaged with STEM with a spatial resolution of 4 nm and a pixel dwell time of 20 ms [1]. In addition, proteins were labeled with quantum dots (QDs), fluorescent nanoparticles visible both with light-and with electron microscopy. STEM images showed individual QDs, and their locations were correlated with the cellular regions, as imaged with fluorescence microscopy [4]. Liquid STEM also obtains contrast on the native structure of live cells. Wild type S. pombe cells and several mutants were imaged with a spatial resolution of 30 nm. [1] N. de Jonge et al.
doi:10.1016/j.bpj.2010.12.1962 fatcat:omiqxzzpwbdnrijmq7n3a3pcga