Rapid increase of near atomic resolution virus capsid structures determined by cryo-electron microscopy.

The recent technological advances in electron microscopes, detectors, as well as image processing and reconstruction software have brought single particle cryo-electron microscopy (cryo-EM) into prominence for determining structures of bio-molecules at near atomic resolution. This has been particularly true for virus capsids, ribosomes, and other large assemblies, which have been the ideal specimens for structural studies by cryo-EM approaches. An analysis of time series metadata of virus structures on the methods of structure determination, resolution of the structures, and size of the virus particles revealed a rapid increase in the virus structures determined by cryo-EM at near atomic resolution since 2010. In addition, the data highlight the median resolution (∼3.0 Å) and size (∼310.0 Šin diameter) of the virus particles determined by X-ray crystallography while no such limits exist for cryo-EM structures, which have a median diameter of 508 Å. Notably, cryo-EM virus structures in the last four years have a median resolution of 3.9 Å. Taken together with minimal sample requirements, not needing diffraction quality crystals, and being able to achieve similar resolutions of the crystal structures makes cryo-EM the method of choice for current and future virus capsid structure determinations.

Phaser.MRage: automated molecular replacement.

Phaser.MRage is a molecular-replacement automation framework that implements a full model-generation workflow and provides several layers of model exploration to the user. It is designed to handle a large number of models and can distribute calculations efficiently onto parallel hardware. In addition, phaser.MRage can identify correct solutions and use this information to accelerate the search. Firstly, it can quickly score all alternative models of a component once a correct solution has been found. Secondly, it can perform extensive analysis of identified solutions to find protein assemblies and can employ assembled models for subsequent searches. Thirdly, it is able to use a priori assembly information (derived from, for example, homologues) to speculatively place and score molecules, thereby customizing the search procedure to a certain class of protein molecule (for example, antibodies) and incorporating additional biological information into molecular replacement.

Advances in Rosetta structure prediction for difficult molecular-replacement problems.

Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed.

Molecular replacement then and now.

The `phase problem' in crystallography results from the inability to directly measure the phases of individual diffracted X-ray waves. While intensities are directly measured during data collection, phases must be obtained by other means. Several phasing methods are available (MIR, SAR, MAD, SAD and MR) and they all rely on the premise that phase information can be obtained if the positions of marker atoms in the unknown crystal structure are known. This paper is dedicated to the most popular phasing method, molecular replacement (MR), and represents a personal overview of the development, use and requirements of the methodology. The first description of noncrystallographic symmetry as a tool for structure determination was explained by Rossmann and Blow [Rossmann & Blow (1962), Acta Cryst. 15, 24-31]. The term `molecular replacement' was introduced as the name of a book in which the early papers were collected and briefly reviewed [Rossmann (1972), The Molecular Replacement Method. New York: Gordon & Breach]. Several programs have evolved from the original concept to allow faster and more sophisticated searches, including six-dimensional searches and brute-force approaches. While careful selection of the resolution range for the search and the quality of the data will greatly influence the outcome, the correct choice of the search model is probably still the main criterion to guarantee success in solving a structure using MR. Two of the main parameters used to define the `best' search model are sequence identity (25% or more) and structural similarity. Another parameter that may often be undervalued is the quality of the probe: there is clearly a relationship between the quality and the correctness of the chosen probe and its usefulness as a search model. Efforts should be made by all structural biologists to ensure that their deposited structures, which are potential search probes for future systems, are of the best possible quality.

United we stand: combining structural methods.

High-resolution techniques are the mainstay of structural biologists; however, to address challenging biological systems many are now turning to hybrid approaches that use complementary structural data. In this review we outline the types of structural problems that benefit from combining results of many methods, we summarise the types of data that can be generated by complementary approaches, and we highlight the application of combined methods in structural biology with recent structural studies of membrane proteins, mega-complexes and inherently flexible proteins.

Neutron crystallography: opportunities, challenges, and limitations.

Neutron crystallography has had an important, but relatively small role in structural biology over the years. In this review of recently determined neutron structures, a theme emerges of a field currently expanding beyond its traditional boundaries, to address larger and more complex problems, with smaller samples and shorter data collection times, and employing more sophisticated structure determination and refinement methods. The origin of this transformation can be found in a number of advances including first, the development of neutron image-plates and quasi-Laue methods at nuclear reactor neutron sources and the development of time-of-flight Laue methods and electronic detectors at spallation neutron sources; second, new facilities and methods for sample perdeuteration and crystallization; third, new approaches and computational tools for structure determination.

Macromolecular X-ray crystallography: soon to be a road less travelled?

The number of new X-ray crystallography-based submissions to the Protein Data Bank appears to be at the beginning of a decline, perhaps signalling an end to the era of the dominance of X-ray crystallography within structural biology. This letter, from the viewpoint of a young structural biologist, applies the Copernican method to the life expectancy of crystallography and asks whether the technique is still the mainstay of structural biology. A study of the rate of Protein Data Bank depositions allows a more nuanced analysis of the fortunes of macromolecular X-ray crystallography and shows that cryo-electron microscopy might now be outcompeting crystallography for new labour and talent, perhaps heralding a change in the landscape of the field.

Advances in the EDM-DEDM procedure.

The DEDM (difference electron-density modification) algorithm has been described in a recent paper [Caliandro et al. (2008), Acta Cryst. A64, 519-528]: it breaks down the collinearity between model structure phases and difference structure phase estimates. The new difference electron-density produced by DEDM, summed to the calculated Fourier maps, is expected to provide a representation of the full structure that is more accurate than that obtained by the observed Fourier synthesis. In the same paper, the DEDM algorithm was combined with the EDM (electron-density modification) approach to give the EDM-DEDM procedure which, when applied to practical molecular-replacement cases, was able to improve the model structures. In this paper, it is shown that EDM-DEDM suffers from some critical points that did not allow cyclical application of the procedure. These points are identified and modifications are made to allow iteration of the procedure. The applications indicate that EDM-DEDM may become a fundamental tool in protein crystallography.

Recent Advances in X-Ray Hydroxyl Radical Footprinting at the Advanced Light Source Synchrotron.

BACKGROUND: Synchrotron hydroxyl radical footprinting is a relatively new structural method used to investigate structural features and conformational changes of nucleic acids and proteins in the solution state. It was originally developed at the National Synchrotron Light Source at Brookhaven National Laboratory in the late nineties, and more recently, has been established at the Advanced Light Source at Lawrence Berkeley National Laboratory. The instrumentation for this method is an active area of development, and includes methods to increase dose to the samples while implementing high-throughput sample delivery methods. CONCLUSION: Improving instrumentation to irradiate biological samples in real time using a sample droplet generator and inline fluorescence monitoring to rapidly determine dose response curves for samples will significantly increase the range of biological problems that can be investigated using synchrotron hydroxyl radical footprinting.

Image-based crystal detection: a machine-learning approach.

The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures.

Cinder: keeping crystallographers app-y.

The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools.

Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1.

Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the first beamline in the world to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014, the beamline has processed over 42 000 samples. Improvements have been made to the speed of the sample-handling robotics and error management within the software routines. The workflows initially put into place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterization; this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal. Complex multi-position and multi-crystal data collections have now also been integrated into the selection of experiments available. This has led to increased data quality and throughput, allowing even the most challenging samples to be treated automatically.