Cullin-RING ubiquitin E3 ligase regulation by the COP9 signalosome.

The cullin-RING ubiquitin E3 ligase (CRL) family comprises over 200 members in humans. The COP9 signalosome complex (CSN) regulates CRLs by removing their ubiquitin-like activator NEDD8. The CUL4A-RBX1-DDB1-DDB2 complex (CRL4A(DDB2)) monitors the genome for ultraviolet-light-induced DNA damage. CRL4A(DBB2) is inactive in the absence of damaged DNA and requires CSN to regulate the repair process. The structural basis of CSN binding to CRL4A(DDB2) and the principles of CSN activation are poorly understood. Here we present cryo-electron microscopy structures for CSN in complex with neddylated CRL4A ligases to 6.4 Å resolution. The CSN conformers defined by cryo-electron microscopy and a novel apo-CSN crystal structure indicate an induced-fit mechanism that drives CSN activation by neddylated CRLs. We find that CSN and a substrate cannot bind simultaneously to CRL4A, favouring a deneddylated, inactive state for substrate-free CRL4 complexes. These architectural and regulatory principles appear conserved across CRL families, allowing global regulation by CSN.

Oxidative doping renders graphene hydrophilic, facilitating its use as a support in biological TEM.

Graphene represents the first practical realization of crystalline supports in biological transmission electron microscopy (TEM) since their introduction over 30 years ago. The high transparency, minimal inelastic cross-section, and electrical conductivity of graphene are highly desirable characteristics for a TEM support. However, without a suitable method for rendering graphene supports, hydrophilic applications are limited. This work describes the in situ functionalization of graphene with minimal structural degradation, rendering TEM supports sufficiently hydrophilic for the mounting of biological samples.

Graphene: Substrate preparation and introduction.

This technical note describes the transfer of continuous, single-layer, pristine graphene to standard Quantifoil TEM grids. We compare the transmission properties of pristine graphene substrates to those of graphene oxide and thin amorphous carbon substrates. Positively stained DNA imaged across amorphous carbon is typically indiscernible and requires metal shadowing for sufficient contrast. However, in a practical illustration of the new substrates properties, positively stained DNA is imaged across pristine graphene in striking contrast without the need of metal shadowing. We go onto discuss technical considerations and the potential applications of pristine graphene substrates as well as their ongoing development.

Graphene oxide: a substrate for optimizing preparations of frozen-hydrated samples.

Graphene oxide is a hydrophilic derivative of graphene to which biological macromolecules readily attach, with properties superior to those of amorphous carbon films commonly used in electron microscopy. The single-layered crystalline lattice of carbon is highly electron transparent, and exhibits conductivity higher than amorphous carbon. Hence, graphene oxide is a particularly promising substrate for the examination of biological materials by electron microscopy. In this manuscript we compare graphene oxide films to commonly used amorphous carbon films, describing the use of graphene in optimizing the preparation of unstained, vitrified biological macromolecules.

Cryoelectron microscopy map of Atadenovirus reveals cross-genus structural differences from human adenovirus.

A three-dimensional (3D) cryoelectron microscopy reconstruction of the prototype Atadenovirus (OAdV [an ovine adenovirus isolate]) showing information at a 10.6-A resolution (0.5 Fourier shell correlation) was derived by single-particle analysis. This is the first 3D structure solved for any adenovirus that is not a Mastadenovirus, allowing cross-genus comparisons between structures and the assignment of genus-specific capsid proteins. Viable OAdV mutants that lacked the genus-specific LH3 and p32k proteins in purified virions were also generated. Negatively stained 3D reconstructions of these mutants were used to identify the location of protein LH3 and infer that of p32k within the capsid. The key finding was that LH3 is a critical protein that holds the outer capsid of the virus together. In its absence, the outer viral capsid is unstable. LH3 is located in the same position among the hexon subunits as its protein IX equivalent from mastadenoviruses but sits on top of the hexon trimers, forming prominent "knobs" on the virion surface that visually distinguish OAdV from other known AdVs. Electron density was also assigned to hexon and penton subunits and to proteins IIIa and VIII. There was good correspondence between OAdV density and human AdV hexon structures, which also validated the significant differences that were observed between the penton base protein structures.

Fast automated cell phenotype image classification.

BACKGROUND: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. RESULTS: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. CONCLUSION: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches.

Preparation of 2D crystals of membrane proteins for high-resolution electron crystallography data collection.

Electron crystallography is a powerful technique for the structure determination of membrane proteins as well as soluble proteins. Sample preparation for 2D membrane protein crystals is a crucial step, as proteins have to be prepared for electron microscopy at close to native conditions. In this review, we discuss the factors of sample preparation that are key to elucidating the atomic structure of membrane proteins using electron crystallography.

Bilateral edge filter: photometrically weighted, discontinuity based edge detection.

Edge-detection algorithms have the potential to play an increasingly important role both in single particle analysis (for the detection of randomly oriented particles), and in tomography (for the segmentation of 3D volumes). However, the majority of traditional linear filters are significantly affected by noise as well as artefacts, and offer limited selectivity. The Bilateral edge filter presented here is an adaptation of the Bilateral filter [Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chiu, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struct. Biol. 144, 114-122] designed for enhanced edge detection. It uses photometric weighting to identify significant discontinuities (representing edges), minimizing artefacts and noise. Compared with common edge-detectors (LoG, Marr-Hildreth) the Bilateral edge filter yielded significantly better results. Indeed data was of a similar quality to that of the Canny edge-detector, which is considered as a leading standard in edge detection [Basu, M., 2002. Gaussian-based edge-detection methods-a survey. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 32, 252-260]. Compared to the Canny edge-detector the Bilateral edge-detector has the advantages that it only requires the adjustment of a single parameter, is theoretically faster for reasonably sized images, and can be used in selective contrast enhancement of images. The simplicity and speed of the filter for single particle and tomographic analysis are discussed.

SwarmPS: rapid, semi-automated single particle selection software.

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that approximately 10(4)-10(5) particle projections are required to attain a 3A resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present Swarm(PS), a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (approximately 10(2)), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. Swarm(PS) is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

The discriminative bilateral filter: an enhanced denoising filter for electron microscopy data.

Advances in three-dimensional (3D) electron microscopy (EM) and image processing are providing considerable improvements in the resolution of subcellular volumes, macromolecular assemblies and individual proteins. However, the recovery of high-frequency information from biological samples is hindered by specimen sensitivity to beam damage. Low dose electron cryo-microscopy conditions afford reduced beam damage but typically yield images with reduced contrast and low signal-to-noise ratios (SNRs). Here, we describe the properties of a new discriminative bilateral (DBL) filter that is based upon the bilateral filter implementation of Jiang et al. (Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chiu, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struc. Biol. 128, 82-97.). In contrast to the latter, the DBL filter can distinguish between object edges and high-frequency noise pixels through the use of an additional photometric exclusion function. As a result, high frequency noise pixels are smoothed, yet object edge detail is preserved. In the present study, we show that the DBL filter effectively reduces noise in low SNR single particle data as well as cellular tomograms of stained plastic sections. The properties of the DBL filter are discussed in terms of its usefulness for single particle analysis and for pre-processing cellular tomograms ahead of image segmentation.