New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples.
AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.
AutoDrug; fragment-based drug discovery; workflow automation
A repetitive measurement of the same diffraction image allows to judge the performance of a data collection facility.
The accuracy of X-ray diffraction data depends on the properties of the crystalline sample and on the performance of the data-collection facility (synchrotron beamline elements, goniostat, detector etc.). However, it is difficult to evaluate the level of performance of the experimental setup from the quality of data sets collected in rotation mode, as various crystal properties such as mosaicity, non-uniformity and radiation damage affect the measured intensities. A multiple-image experiment, in which several analogous diffraction frames are recorded consecutively at the same crystal orientation, allows minimization of the influence of the sample properties. A series of 100 diffraction images of a thaumatin crystal were measured on the SBC beamline 19BM at the APS (Argonne National Laboratory). The obtained data were analyzed in the context of the performance of the data-collection facility. An objective way to estimate the uncertainties of individual reflections was achieved by analyzing the behavior of reflection intensities in the series of analogous diffraction images. The multiple-image experiment is found to be a simple and adequate method to decompose the random errors from the systematic errors in the data, which helps in judging the performance of a data-collection facility. In particular, displaying the intensity as a function of the frame number allows evaluation of the stability of the beam, the beamline elements and the detector with minimal influence of the crystal properties. Such an experiment permits evaluation of the highest possible data quality potentially achievable at the particular beamline.
diffraction data precision; signal-to-noise ratio; measurement uncertainty; beamline performance
The macromolecular crystallography experiment lends itself perfectly to high-throughput technologies. The initial steps including the expression, purification and crystallization of protein crystals, along with some of the later steps involving data processing and structure determination have all been automated to the point where some of the last remaining bottlenecks in the process have been crystal mounting, crystal screening and data collection. At the Stanford Synchrotron Radiation Laboratory (SSRL), a National User Facility which provides extremely brilliant X-ray photon beams for use in materials science, environmental science and structural biology research, the incorporation of advanced robotics has enabled crystals to be screened in a true high-throughput fashion, thus dramatically accelerating the final steps. Up to 288 frozen crystals can be mounted by the beamline robot (the Stanford Automated Mounter, or SAM) and screened for diffraction quality in a matter of hours without intervention. The best quality crystals can then be remounted for the collection of complete X-ray diffraction data sets. Furthermore, the entire screening and data collection experiment can be controlled from the experimenter’s home laboratory by means of advanced software tools that enable network-based control of the highly automated beamlines.
protein crystallography; cryocrystallography; high-throughput screening; robotics; remote access
A system for the automatic reduction of single- and multi-position macromolecular crystallography data is presented.
The development of automated high-intensity macromolecular crystallography (MX) beamlines at synchrotron facilities has resulted in a remarkable increase in sample throughput. Developments in X-ray detector technology now mean that complete X-ray diffraction datasets can be collected in less than one minute. Such high-speed collection, and the volumes of data that it produces, often make it difficult for even the most experienced users to cope with the deluge. However, the careful reduction of data during experimental sessions is often necessary for the success of a particular project or as an aid in decision making for subsequent experiments. Automated data reduction pipelines provide a fast and reliable alternative to user-initiated processing at the beamline. In order to provide such a pipeline for the MX user community of the European Synchrotron Radiation Facility (ESRF), a system for the rapid automatic processing of MX diffraction data from single and multiple positions on a single or multiple crystals has been developed. Standard integration and data analysis programs have been incorporated into the ESRF data collection, storage and computing environment, with the final results stored and displayed in an intuitive manner in the ISPyB (information system for protein crystallography beamlines) database, from which they are also available for download. In some cases, experimental phase information can be automatically determined from the processed data. Here, the system is described in detail.
automation; data processing; macromolecular crystallography; computer programs
Two sample-scanning features have been implemented for the macromolecular crystallography beamlines at APS sector 23: automated diffraction-based rastering employing multiple polygon-shaped two-dimensional grids overlaid on a sample to locate and center small and invisible crystals or to find the best-diffracting regions in a larger crystal, and automated data collection along a three-dimensional vector to mitigate the effects of radiation damage.
Automated scanning capabilities have been added to the data acquisition software, JBluIce-EPICS, at the National Institute of General Medical Sciences and the National Cancer Institute Collaborative Access Team (GM/CA CAT) at the Advanced Photon Source. A ‘raster’ feature enables sample centering via diffraction scanning over two-dimensional grids of simple rectangular or complex polygonal shape. The feature is used to locate crystals that are optically invisible owing to their small size or are visually obfuscated owing to properties of the sample mount. The raster feature is also used to identify the best-diffracting regions of large inhomogeneous crystals. Low-dose diffraction images taken at grid positions are automatically processed in real time to provide a quick quality ranking of potential data-collection sites. A ‘vector collect’ feature mitigates the effects of radiation damage by scanning the sample along a user-defined three-dimensional vector during data collection to maximize the use of the crystal volume and the quality of the collected data. These features are integrated into the JBluIce-EPICS data acquisition software developed at GM/CA CAT where they are used in combination with a robust mini-beam of rapidly changeable diameter from 5 µm to 20 µm. The powerful software–hardware combination is being applied to challenging problems in structural biology.
macromolecular crystallography; beamline automation; data acquisition; high-throughput crystallography; crystal centering; radiation damage; rastering
The Computational Crystallography Toolbox (cctbx) is a flexible software platform that has been used to develop high-throughput crystal-screening tools for both synchrotron sources and X-ray free-electron lasers. Plans for data-processing and visualization applications are discussed, and the benefits and limitations of using graphics-processing units are evaluated.
Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units.
data processing; reusable code; multiprocessing; cctbx
An iOS app has been developed as a front end to ISPyB, a laboratory information system for macromolecular crystallography synchrotron beamlines.
The macromolecular crystallography (MX) user experience at synchrotron radiation facilities continues to evolve, with the impact of developments in X-ray detectors, computer hardware and automation methods making it possible for complete data sets to be collected on timescales of tens of seconds. Data can be reduced in a couple of minutes and in favourable cases structures solved and refined shortly after. The information-rich database ISPyB, automatically populated by data acquisition software, data processing and structure solution pipelines at the Diamond Light Source beamlines, allows users to automatically track MX experiments in real time. In order to improve the synchrotron users’ experience, efficient access to the data contained in ISPyB is now provided via an iOS 6.0+ app for iPhones and iPads. This provides users, both local and remote, with a succinct summary of data collection, visualization of diffraction images and crystals, and key metrics for data quality in real time.
remote data collection; synchrotron radiation; macromolecular crystallography; laboratory information management systems (LIMS)
The ultimate goal of synchrotron data collection is to obtain the best possible data from the best available crystals, and the combination of automation and remote access at Stanford Synchrotron Radiation Lightsource (SSRL) has revolutionized the way in which scientists achieve this goal. This has also seen a change in the way novice crystallographers are trained in the use of the beamlines, and a wide range of remote tools and hands-on workshops are now offered by SSRL to facilitate the education of the next generation of protein crystallographers.
For the past five years, the Structural Molecular Biology group at the Stanford Synchrotron Radiation Lightsource (SSRL) has provided general users of the facility with fully remote access to the macromolecular crystallography beamlines. This was made possible by implementing fully automated beamlines with a flexible control system and an intuitive user interface, and by the development of the robust and efficient Stanford automated mounting robotic sample-changing system. The ability to control a synchrotron beamline remotely from the comfort of the home laboratory has set a new paradigm for the collection of high-quality X-ray diffraction data and has fostered new collaborative research, whereby a number of remote users from different institutions can be connected at the same time to the SSRL beamlines. The use of remote access has revolutionized the way in which scientists interact with synchrotron beamlines and collect diffraction data, and has also triggered a shift in the way crystallography students are introduced to synchrotron data collection and trained in the best methods for collecting high-quality data. SSRL provides expert crystallographic and engineering staff, state-of-the-art crystallography beamlines, and a number of accessible tools to facilitate data collection and in-house remote training, and encourages the use of these facilities for education, training, outreach and collaborative research.
protein crystallography; high-throughput screening; robotics; remote access; crystallographic education and training; outreach
A powerful and easy-to-use workflow environment has been developed at the ESRF for combining experiment control with online data analysis on synchrotron beamlines. This tool provides the possibility of automating complex experiments without the need for expertise in instrumentation control and programming, but rather by accessing defined beamline services.
The automation of beam delivery, sample handling and data analysis, together with increasing photon flux, diminishing focal spot size and the appearance of fast-readout detectors on synchrotron beamlines, have changed the way that many macromolecular crystallography experiments are planned and executed. Screening for the best diffracting crystal, or even the best diffracting part of a selected crystal, has been enabled by the development of microfocus beams, precise goniometers and fast-readout detectors that all require rapid feedback from the initial processing of images in order to be effective. All of these advances require the coupling of data feedback to the experimental control system and depend on immediate online data-analysis results during the experiment. To facilitate this, a Data Analysis WorkBench (DAWB) for the flexible creation of complex automated protocols has been developed. Here, example workflows designed and implemented using DAWB are presented for enhanced multi-step crystal characterizations, experiments involving crystal reorientation with kappa goniometers, crystal-burning experiments for empirically determining the radiation sensitivity of a crystal system and the application of mesh scans to find the best location of a crystal to obtain the highest diffraction quality. Beamline users interact with the prepared workflows through a specific brick within the beamline-control GUI MXCuBE.
workflows; automation; data processing; macromolecular crystallography; experimental protocols; characterization; reorientation; radiation damage
Single- and multi-crystal data collection strategy and automated data processing have been tightly integrated into the JBluIce graphical user interface. Grid Engine is used to distribute these processes into multiple workstations to make efficient use of all available computing resources.
The calculation of single- and multi-crystal data collection strategies and a data processing pipeline have been tightly integrated into the macromolecular crystallographic data acquisition and beamline control software JBluIce. Both tasks employ wrapper scripts around existing crystallographic software. JBluIce executes scripts through a distributed resource management system to make efficient use of all available computing resources through parallel processing. The JBluIce single-crystal data collection strategy feature uses a choice of strategy programs to help users rank sample crystals and collect data. The strategy results can be conveniently exported to a data collection run. The JBluIce multi-crystal strategy feature calculates a collection strategy to optimize coverage of reciprocal space in cases where incomplete data are available from previous samples. The JBluIce data processing runs simultaneously with data collection using a choice of data reduction wrappers for integration and scaling of newly collected data, with an option for merging with pre-existing data. Data are processed separately if collected from multiple sites on a crystal or from multiple crystals, then scaled and merged. Results from all strategy and processing calculations are displayed in relevant tabs of JBluIce.
automated data processing; multi-crystal data collection strategies; X-ray crystallography; Grid Engine
Hydrogen/deuterium exchange mass spectrometry (H/DX-MS) experiments implemented to characterize protein interaction and protein folding generate large quantities of data. Organizing, processing and visualizing data requires an automated solution, particularly when accommodating new tandem mass spectrometry modes for H/DX measurement. We sought to develop software that offers flexibility in defining workflows so as to support exploratory treatments of H/DX-MS data, with a particular focus on the analysis of very large protein systems and the mining of tandem mass spectrometry data.
We present a software package ("Hydra") that supports both traditional and exploratory treatments of H/DX-MS data. Hydra's software architecture tolerates flexible data analysis procedures by allowing the addition of new algorithms without significant change to the underlying code base. Convenient user interfaces ease the organization of raw data files and input of peptide data. After executing a user-defined workflow, extracted deuterium incorporation values can be visualized in tabular and graphical formats. Hydra also automates the extraction and visualization of deuterium distribution values. Manual validation and assessment of results is aided by an interface that aligns extracted ion chromatograms and mass spectra, while providing a means of rapidly reprocessing the data following manual adjustment. A unique feature of Hydra is the automated processing of tandem mass spectrometry data, demonstrated on a large test data set in which 40,000 deuterium incorporation values were extracted from replicate analysis of approximately 1000 fragment ions in one hour using a typical PC.
The customizable workflows and user-friendly interfaces of Hydra removes a significant bottleneck in processing and visualizing H/DX-MS data and helps the researcher spend more time executing new experiments and interpreting results. This increased efficiency will encourage the analysis of larger protein systems. The ability to accommodate the tandem MS dimension supports alternative data collection and analysis strategies, as well as higher resolution localization of deuteration where permitted by the fragmentation mechanism.
The X-CHIP (X-ray Crystallography High-throughput Integrated Platform) is a novel microchip that has been developed to combine multiple steps of the crystallographic pipeline from crystallization to diffraction data collection on a single device to streamline the entire process.
The X-CHIP (X-ray Crystallization High-throughput Integrated Platform) is a novel microchip that has been developed to combine multiple steps of the crystallographic pipeline from crystallization to diffraction data collection on a single device to streamline the entire process. The system has been designed for crystallization condition screening, visual crystal inspection, initial X-ray screening and data collection in a high-throughput fashion. X-ray diffraction data acquisition can be performed directly on-the-chip at room temperature using an in situ approach. The capabilities of the chip eliminate the necessity for manual crystal handling and cryoprotection of crystal samples, while allowing data collection from multiple crystals in the same drop. This technology would be especially beneficial for projects with large volumes of data, such as protein-complex studies and fragment-based screening. The platform employs hydrophilic and hydrophobic concentric ring surfaces on a miniature plate transparent to visible light and X-rays to create a well defined and stable microbatch crystallization environment. The results of crystallization and data-collection experiments demonstrate that high-quality well diffracting crystals can be grown and high-resolution diffraction data sets can be collected using this technology. Furthermore, the quality of a single-wavelength anomalous dispersion data set collected with the X-CHIP at room temperature was sufficient to generate interpretable electron-density maps. This technology is highly resource-efficient owing to the use of nanolitre-scale drop volumes. It does not require any modification for most in-house and synchrotron beamline systems and offers a promising opportunity for full automation of the X-ray structure-determination process.
protein crystallization devices; in situ X-ray analysis; crystallization; crystal visual inspection; diffraction data collection
Through the combination of robust mechanized experimental hardware and a flexible control system with an intuitive user interface, SSRL researchers have screened over 200 000 biological crystals for diffraction quality in an automated fashion. Three quarters of SSRL researchers are using these data-collection tools from remote locations.
Complete automation of the macromolecular crystallography experiment has been achieved at SSRL through the combination of robust mechanized experimental hardware and a flexible control system with an intuitive user interface. These highly reliable systems have enabled crystallography experiments to be carried out from the researchers’ home institutions and other remote locations while retaining complete control over even the most challenging systems. A breakthrough component of the system, the Stanford Auto-Mounter (SAM), has enabled the efficient mounting of cryocooled samples without human intervention. Taking advantage of this automation, researchers have successfully screened more than 200 000 samples to select the crystals with the best diffraction quality for data collection as well as to determine optimal crystallization and cryocooling conditions. These systems, which have been deployed on all SSRL macromolecular crystallography beamlines and several beamlines worldwide, are used by more than 80 research groups in remote locations, establishing a new paradigm for macromolecular crystallography experimentation.
remote crystallography data collection; robotics
A mail-in data collection system at SPring-8, which is a web application with automated beamline operation, has been developed.
A mail-in data collection system makes it possible for beamline users to collect diffraction data without visiting a synchrotron facility. In the mail-in data collection system at SPring-8, users pack crystals into sample trays and send the trays to SPring-8 via a courier service as the first step. Next, the user specifies measurement conditions and checks the diffraction images via the Internet. The user can also collect diffraction data using an automated sample changer robot and beamline control software. For distant users there is a newly developed data management system, D-Cha. D-Cha provides a graphical user interface that enables the user to specify the experimental conditions for samples and to check and download the diffraction images using a web browser. This system is now in routine operation and is contributing to high-throughput beamline operation.
mail-in data collection; high-throughput data collection; beamline automation; web application; database system
Hardware and software solutions for MX data-collection strategies using the EMBL/ESRF miniaturized multi-axis goniometer head are presented.
Most macromolecular crystallography (MX) diffraction experiments at synchrotrons use a single-axis goniometer. This markedly contrasts with small-molecule crystallography, in which the majority of the diffraction data are collected using multi-axis goniometers. A novel miniaturized κ-goniometer head, the MK3, has been developed to allow macromolecular crystals to be aligned. It is available on the majority of the structural biology beamlines at the ESRF, as well as elsewhere. In addition, the Strategy for the Alignment of Crystals (STAC) software package has been developed to facilitate the use of the MK3 and other similar devices. Use of the MK3 and STAC is streamlined by their incorporation into online analysis tools such as EDNA. The current use of STAC and MK3 on the MX beamlines at the ESRF is discussed. It is shown that the alignment of macromolecular crystals can result in improved diffraction data quality compared with data obtained from randomly aligned crystals.
kappa goniometer; crystal alignment; data-collection strategies
Gas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.
PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).
PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.
Implementation of the RED software package for automated collection and processing of rotation electron diffraction data is described.
Implementation of a computer program package for automated collection and processing of rotation electron diffraction (RED) data is described. The software package contains two computer programs: RED data collection and RED data processing. The RED data collection program controls the transmission electron microscope and the camera. Electron beam tilts at a fine step (0.05–0.20°) are combined with goniometer tilts at a coarse step (2.0–3.0°) around a common tilt axis, which allows a fine relative tilt to be achieved between the electron beam and the crystal in a large tilt range. An electron diffraction (ED) frame is collected at each combination of beam tilt and goniometer tilt. The RED data processing program processes three-dimensional ED data generated by the RED data collection program or by other approaches. It includes shift correction of the ED frames, peak hunting for diffraction spots in individual ED frames and identification of these diffraction spots as reflections in three dimensions. Unit-cell parameters are determined from the positions of reflections in three-dimensional reciprocal space. All reflections are indexed, and finally a list with hkl indices and intensities is output. The data processing program also includes a visualizer to view and analyse three-dimensional reciprocal lattices reconstructed from the ED frames. Details of the implementation are described. Data collection and data processing with the software RED are demonstrated using a calcined zeolite sample, silicalite-1. The structure of the calcined silicalite-1, with 72 unique atoms, could be solved from the RED data by routine direct methods.
rotation electron diffraction; electron diffraction tomography; three-dimensional electron diffraction; structure analysis; electron diffraction; computer programs
Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.
With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC.
pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
We demonstrate that it is feasible to determine high-resolution protein structures by electron crystallography of three-dimensional crystals in an electron cryo-microscope (CryoEM). Lysozyme microcrystals were frozen on an electron microscopy grid, and electron diffraction data collected to 1.7 Å resolution. We developed a data collection protocol to collect a full-tilt series in electron diffraction to atomic resolution. A single tilt series contains up to 90 individual diffraction patterns collected from a single crystal with tilt angle increment of 0.1–1° and a total accumulated electron dose less than 10 electrons per angstrom squared. We indexed the data from three crystals and used them for structure determination of lysozyme by molecular replacement followed by crystallographic refinement to 2.9 Å resolution. This proof of principle paves the way for the implementation of a new technique, which we name ‘MicroED’, that may have wide applicability in structural biology.
X-ray crystallography has been used to work out the atomic structure of a large number of proteins. In a typical X-ray crystallography experiment, a beam of X-rays is directed at a protein crystal, which scatters some of the X-ray photons to produce a diffraction pattern. The crystal is then rotated through a small angle and another diffraction pattern is recorded. Finally, after this process has been repeated enough times, it is possible to work backwards from the diffraction patterns to figure out the structure of the protein.
The crystals used for X-ray crystallography must be large to withstand the damage caused by repeated exposure to the X-ray beam. However, some proteins do not form crystals at all, and others only form small crystals. It is possible to overcome this problem by using extremely short pulses of X-rays, but this requires a very large number of small crystals and ultrashort X-ray pulses are only available at a handful of research centers around the world. There is, therefore, a need for other approaches that can determine the structure of proteins that only form small crystals.
Electron crystallography is similar to X-ray crystallography in that a protein crystal scatters a beam to produce a diffraction pattern. However, the interactions between the electrons in the beam and the crystal are much stronger than those between the X-ray photons and the crystal. This means that meaningful amounts of data can be collected from much smaller crystals. However, it is normally only possible to collect one diffraction pattern from each crystal because of beam induced damage. Researchers have developed methods to merge the diffraction patterns produced by hundreds of small crystals, but to date these techniques have only worked with very thin two-dimensional crystals that contain only one layer of the protein of interest.
Now Shi et al. report a new approach to electron crystallography that works with very small three-dimensional crystals. Called MicroED, this technique involves placing the crystal in a transmission electron cryo-microscope, which is a fairly standard piece of equipment in many laboratories. The normal ‘low-dose’ electron beam in one of these microscopes would normally damage the crystal after a single diffraction pattern had been collected. However, Shi et al. realized that it was possible to obtain diffraction patterns without severely damaging the crystal if they dramatically reduced the normal low-dose electron beam. By reducing the electron dose by a factor of 200, it was possible to collect up to 90 diffraction patterns from the same, very small, three-dimensional crystal, and then—similar to what happens in X-ray crystallography—work backwards to figure out the structure of the protein. Shi et al. demonstrated the feasibility of the MicroED approach by using it to determine the structure of lysozyme, which is widely used as a test protein in crystallography, with a resolution of 2.9 Å. This proof-of principle study paves the way for crystallographers to study protein that cannot be studied with existing techniques.
electron crystallography; electron diffraction; electron cryomicroscopy (cryo-EM); microED; protein structure; microcrystals; None
Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.
We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.
The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.
MxCuBE is a beamline control environment optimized for the needs of macromolecular crystallography. This paper describes the design of the software and the features that MxCuBE currently provides.
The design and features of a beamline control software system for macromolecular crystallography (MX) experiments developed at the European Synchrotron Radiation Facility (ESRF) are described. This system, MxCuBE, allows users to easily and simply interact with beamline hardware components and provides automated routines for common tasks in the operation of a synchrotron beamline dedicated to experiments in MX. Additional functionality is provided through intuitive interfaces that enable the assessment of the diffraction characteristics of samples, experiment planning, automatic data collection and the on-line collection and analysis of X-ray emission spectra. The software can be run in a tandem client-server mode that allows for remote control and relevant experimental parameters and results are automatically logged in a relational database, ISPyB. MxCuBE is modular, flexible and extensible and is currently deployed on eight macromolecular crystallography beamlines at the ESRF. Additionally, the software is installed at MAX-lab beamline I911-3 and at BESSY beamline BL14.1.
automation; macromolecular crystallography; synchrotron beamline control; graphical user interface
Electron crystallography of two-dimensional crystals allows the structural study of membrane proteins in their native environment, the lipid bilayer. Determining the structure of a membrane protein at near-atomic resolution by electron crystallography remains, however, a very labor-intense and time-consuming task. To simplify and accelerate the data processing aspect of electron crystallography, we implemented a pipeline for the processing of electron diffraction data using the Image Processing Library & Toolbox (IPLT), which provides a modular, flexible, integrated, and extendable cross-platform, open-source framework for image processing. The diffraction data processing pipeline is organized as several independent modules implemented in Python. The modules can be accessed either from a graphical user interface or through a command line interface, thus meeting the needs of both novice and expert users. The low-level image processing algorithms are implemented in C++ to achieve optimal processing performance, and their interface is exported to Python using a wrapper. For enhanced performance, the Python processing modules are complemented with a central data managing facility that provides a caching infrastructure. The validity of our data processing algorithms was verified by processing a set of aquaporin-0 diffraction patterns with the IPLT pipeline and comparing the resulting merged data set with that obtained by processing the same diffraction patterns with the classical set of MRC programs.
electron crystallography; electron diffraction; image processing; software
Multi-faceted approaches are generally recognised as the most effective way to support the implementation of evidence into practice. Audit and feedback often constitute one element of a multi-faceted implementation package, alongside other strategies, such as interactive education and facilitated support mechanisms. This paper describes a multi-faceted implementation strategy that used the Joanna Briggs Institute Practical Application of Clinical Evidence System (PACES) as an online audit tool to support facilitators working to introduce evidence-based continence recommendations in nursing homes in four different European countries.
The paper describes the experience of using PACES with an international group of nursing home facilitators. In particular, the objectives of the paper are: to describe the process of introducing PACES to internal facilitators in eight nursing homes; to discuss the progress made during a 12-month period of collecting and analysing audit data using PACES; to summarise the collective experience of using PACES, including reflections on its strengths and limitations.
Descriptive data were collected during the 12-month period of working with PACES in the eight nursing home sites. These data included digital and written notes taken at an initial 3-day introductory programme, at monthly teleconferences held between the external and internal facilitators and at a final 2-day meeting. Qualitative analysis of the data was undertaken on an ongoing basis throughout the implementation period, which enabled formative evaluation of PACES. A final summative evaluation of the experience of using PACES was undertaken as part of the closing project meeting in June 2011.
The nursing home facilitators took longer than anticipated to introduce PACES and it was only after 9–10 months that they became confident and comfortable using the system. This was due to a combination of factors, including a lack of audit knowledge and skills, limited IT access and skills, language difficulties and problems with the PACES system itself. The initial plan of undertaking a full baseline audit followed by focused action cycles had to be revised to allow a more staged, smaller-scale approach to implementation and audit. This involved simplifying the audit process and removing steps such as the calculation of population size estimates. As a result, an accurate baseline measure, prior to introducing changes to continence care, was not achieved. However, by the end of the 12 months, the majority of facilitators had undertaken a full audit and reported value in the process. In particular, they benefited from comparing audit data across sites to share learning and best practice.
Working with PACES as part of a facilitated programme to support the implementation of evidence-based continence recommendations in nursing homes in four European countries has been a valuable learning experience, although not without its challenges. The findings highlight the importance of thorough training and support for first time users of PACES and the need to make the audit process as simple as possible in the initial stages.
audit; continence care; evidence-based practice; facilitation; implementation; PACES
The SIBYLS beamline of the Advanced Light Source at Lawrence Berkeley National Laboratory is a dual endstation small-angle X-ray scattering and macromolecular crystallography beamline. Key features and capabilities are described along with implementation and performance.
The SIBYLS beamline (12.3.1) of the Advanced Light Source at Lawrence Berkeley National Laboratory, supported by the US Department of Energy and the National Institutes of Health, is optimized for both small-angle X-ray scattering (SAXS) and macromolecular crystallography (MX), making it unique among the world’s mostly SAXS or MX dedicated beamlines. Since SIBYLS was commissioned, assessments of the limitations and advantages of a combined SAXS and MX beamline have suggested new strategies for integration and optimal data collection methods and have led to additional hardware and software enhancements. Features described include a dual mode monochromator [containing both Si(111) crystals and Mo/B4C multilayer elements], rapid beamline optics conversion between SAXS and MX modes, active beam stabilization, sample-loading robotics, and mail-in and remote data collection. These features allow users to gain valuable insights from both dynamic solution scattering and high-resolution atomic diffraction experiments performed at a single synchrotron beamline. Key practical issues considered for data collection and analysis include radiation damage, structural ensembles, alternative conformers and flexibility. SIBYLS develops and applies efficient combined MX and SAXS methods that deliver high-impact results by providing robust cost-effective routes to connect structures to biology and by performing experiments that aid beamline designs for next generation light sources.
small-angle X-ray scattering (SAXS); macromolecular crystallography (MX); synchrotron beamlines; SIBYLS
A number of microarray analysis software packages exist already; however, none combines the user-friendly features of a web-based interface with potential ability to analyse multiple arrays at once using flexible analysis steps. The ArrayPipe web server (freely available at www.pathogenomics.ca/arraypipe) allows the automated application of complex analyses to microarray data which can range from single slides to large data sets including replicates and dye-swaps. It handles output from most commonly used quantification software packages for dual-labelled arrays. Application features range from quality assessment of slides through various data visualizations to multi-step analyses including normalization, detection of differentially expressed genes, andcomparison and highlighting of gene lists. A highly customizable action set-up facilitates unrestricted arrangement of functions, which can be stored as action profiles. A unique combination of web-based and command-line functionality enables comfortable configuration of processes that can be repeatedly applied to large data sets in high throughput. The output consists of reports formatted as standard web pages and tab-delimited lists of calculated values that can be inserted into other analysis programs. Additional features, such as web-based spreadsheet functionality, auto-parallelization and password protection make this a powerful tool in microarray research for individuals and large groups alike.