Journal of Applied Crystallography

X-ray free electron laser (XFEL) light sources have allowed for the rapid growth of time-resolved structural experiments, which provide crucial information on the function of biological machines and their mechanisms. We set out to commission the SwissMX fixed-target sample delivery system at the SwissFEL Cristallina experimental station using the PSI developed MISP-chip for pump-probe time-resolved experiments. To characterise the system, we used the light-sensitive protein crystals of the Light-Oxygen-Voltage domain 1 (LOV1) from Chlamydomonas reinhardtii. Using different experimental settings, the adjacent-well light contamination was carefully assessed, indicating that it is crucial to control the light scattering from solid supports otherwise significant contamination can occur. However, our results show that, after the initial experiments and parameter refinement, the opaque MISP-chips are suitable for pump-probing a light-sensitive protein. This crystallographic experiment also probed the sub-millisecond structural dynamics of the LOV1 and indicated that at {Delta}t=10 s the covalent thioether bond is already established between the reactive Cys57 and FMN cofactor. This experiment validated the crystals to be suitable for in-depth follow up studies of the still poorly understood signal transduction mechanism. Importantly, the fixed-target delivery system also permitted a tenfold reduction in protein sample consumption compared to the most successful system used at XFEL, the high-viscosity extruder. This development creates the prospect of an exciting increase in XFEL project throughput for the field.

Time resolved X-ray crystallography is experiencing a resurgence, in part, because of serial methods that readily allow scientists to create stop-motion movies of macromolecular function of photoactivation, enzyme catalysed reactions, and ligand-induced conformational changes triggering further downstream signalling events. While some reactions can be initiated with light, either naturally or using photocaged compounds, a more generally applicable approach is to mix microcrystals with reagents at varying time points prior to exposure to the X-ray beam. A powerful approach has been to combine droplet on demand tape drive sample delivery with X-ray emission spectroscopy (XES) that correlates atomic structure with electronic states of metal ions within the sample. To our knowledge, such a combined methodology has not been deployed previously at a synchrotron beamline and has been restricted to XFELs. Here we describe prototype experiments along the development pathway to a combined droplet on demand diffraction and XES system at the microfocus beamline VMXi at Diamond Light Source. We demonstrate the collection of a high-quality serial diffraction data set from microcrystals within hundreds of picolitre-volume droplets deposited on a moving tape. In separate experiments at VMXi, we collected XES data from microcrystals of a copper enzyme delivered using a high viscosity extruder. Together, these results demonstrate the feasibility of combined droplet on demand serial crystallography and XES experiments using a third-generation synchrotron beamline; project work currently underway at Diamond Light Source. SynopsisWe describe proof of concept experiments towards correlated serial crystallography (SSX) and X-ray emission spectroscopy (XES) from microcrystals at a microfocus synchrotron beamline. A droplet on demand tape drive system delivers microcrystals to the beam within well-separated, hundreds of picolitre-volume droplets while XES allows validation of redox states of metals within protein crystals.

We report on recent methodological advances at the Life Science X-ray Scattering (LiX) beamline of the National Synchrotron Light Source II (NSLS-II) to support small-angle X-ray scattering experiments on skeletal and cardiac muscle tissues. These experiments have been routinely performed at the BioCAT beamline of the Advanced Photon Source (APS) over the past two decades to measure sarcomeric protein organization within healthy and diseased muscle tissues and provide direct molecular evidence for their functional roles and dynamics. Many recent advances in our understanding of sarcomeric proteins relied on diffraction data and include, as examples, MyBP-C, crossbridge SRX/DRX states, and titin. With LiX now available for muscle experimentation, more muscle users can be supported which will speed up research of sarcomeric proteins, muscle biomechanics, and skeletal and cardiac myopathies. LiX explicitly focuses on high-throughput muscle diffraction with rapid sample turnover and semi-automated data processing. These operations have been tested and validated on skeletal and cardiac tissues sourced from both humans and multiple animal models including pig, rat, mouse, and zebrafish.

Diffuse scattering is a powerful technique to study disorder and dynamics of macromolecules at atomic resolution. Although diffuse scattering is always present in diffraction images from macromolecular crystals, the signal is weak compared with Bragg peaks and background, making it a challenge to visualize and measure accurately. Recently, this challenge has been addressed using the reciprocal space mapping technique, which leverages ideal properties of modern X-ray detectors to reconstruct the complete three-dimensional volume of continuous diffraction from diffraction images of a crystal (or crystals) in many different orientations. This chapter will review recent progress in reciprocal space mapping with a particular focus on the strategy implemented in the mdx-lib and mdx2 software packages. The chapter concludes with an introductory data processing tutorial using Python packages DIALS, NeXpy, and mdx2.

Despite the tremendous success of x-ray cryocrystallography over recent decades, the transfer of crystals from the drops where they grow to diffractometer sample mounts, remains a manual process in almost all laboratories. Here we describe the Shifter, a semi-automated microscope stage that offers an accessible and scalable approach to crystal mounting that exploits on the strengths of both humans and machines. The Shifter control software manoeuvres sample drops beneath a hole in a clear protective cover, for human mounting under a microscope. By allowing complete removal of film seals the tedium of cutting or removing the seal is eliminated. The control software also automatically captures experimental annotations for uploading to the users data repository, removing the overhead of manual documentation. The Shifter facilitates mounting rates of 100-240 crystals per hour, in a more controlled process than manual mounting, which greatly extends the lifetime of drops and thus allows for a dramatic increase in the number of crystals retrievable from any given drop, without loss of X-ray diffraction quality. In 2015 the first in a series of three Shifter devices was deployed as part of the XChem fragment screening facility at Diamond Light Source (DLS), where they have since facilitated the mounting of over 100,000 crystals. The Shifter was engineered to be simple, allowing for a low-cost device to be commercialised and thus potentially transformative as many research initiatives as possible. SynopsisA motorised X/Y microscope stage is presented that combines human fine motor control with machine automation and automated experiment documentation, to transform productivity in protein crystal harvesting.

Room-temperature (RT) X-ray diffraction experiments enable us to investigate protein dynamics, efficiently probe fragment binding, and perform time-resolved crystallography experiments. The Versatile Macromolecular Crystallography in-situ (VMXi) beamline at Diamond Light Source (DLS) in the United Kingdom specializes in the collection of RT X-ray diffraction data in situ directly from crystallization trays without any manipulation of protein crystals, improving crystal integrity for fragile crystals. While many X-ray sources are now equipped to grow crystals on site for in-situ experiments, to date there has been no comprehensive analysis that we are aware of on the effect of shipping crystals on plates at ambient temperature for RT data collection, while the equivalent methodology for cryo-cooled crystals is well established. Here we examine the impact of shipping on crystals grown on MiTeGen In Situ-1 plates at the University of Buffalo Hauptman Woodward Research Institute (UB-HWI) in Buffalo, NY, United States transatlantic to DLS in Didcot, United Kingdom. We utilized the Stanford Synchrotron Radiation Lightsource (SSRL) Blue Box Thermal Shipper (Blue Box), which can maintain temperature for up to 168 hours, to ship crystallization plates at room temperature from UB-HWI to DLS. We hypothesized that long-distance shipping might compromise data quality through mechanical stress or temperature fluctuations. Instead, we found that room-temperature data collected at VMXi showed no significant differences for crystals set up at UB-HWI and shipped relative to crystals set up on site in the UK. High-resolution structures were successfully determined for all proteins in the study, demonstrating that long-distance shipment of crystals at non-cryogenic temperatures is feasible without compromising diffraction quality. This study provides a proof-of-concept workflow for expanding access to room-temperature crystallography worldwide, enabling more researchers to leverage cutting-edge techniques without needing to grow crystals on site.

Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robotic systems for crystal growth and diffraction analysis is widespread and high throughput protein-to-structure pipelines for ligand and fragment screening are revolutionizing the field. However, the identification of crystals is still largely carried out through manual inspection, sometimes involving tens of thousands of images, which represents a bottleneck in an otherwise highly automated process. Here we describe AXIS, an AI-based Crystal Identification System combining the DINOv2 computer vision model, state-of-the-art transfer learning and MARCO, the largest crystallization dataset available to date, for automated crystal detection. AXIS can operate both with visible and UV light images and integrates a Lab-In-The-Loop approach combining ML and expert inputs for continuous learning and specialization. AXIS enables automated annotation of large crystallization image datasets with performance and accuracy comparable to that of human experts and the Lab-In-The-Loop approach introduced here enables efficient adaptation to local conditions facilitating widespread application, which has been a major limitation to date. AXIS can help correct human errors in image annotation and removes critical bottlenecks, particularly in the context of extensive crystallization screens or high throughput applications like fragment and ligand screening unlocking the potential for higher levels of automation that are key both in fundamental and translational research. Appendix A. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/685844v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@c47ab4org.highwire.dtl.DTLVardef@153ce2dorg.highwire.dtl.DTLVardef@1050086org.highwire.dtl.DTLVardef@165965b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Despite rapid adaptation of micro-electron diffraction (MicroED) for protein and small molecule structure determination to sub-angstrom resolution, the lack of automation tools for easy MicroED data processing remains a challenge for expanding to the broader scientific community. In particular, automation tools, which are novice user friendly, compatible with heterogenous datasets and can be run in unison with data collection to judge the quality of incoming data (similar to cryosparc LIVE for single particle cryoEM) do not exist. Here, we present AutoMicroED, a cohesive and semi-automatic MicroED data processing pipeline that runs through image conversion, indexing, integration and scaling of data, followed by merging of successful datasets that are pushed through phasing and final structure determination. AutoMicroED is compatible with both small molecule and protein datasets and creates a straightforward and reproducible method to solve single structures from pure samples, or multiple structures from mixed populations. The immediate feedback on data quality, data completeness and more parameters, aids users to identify whether they have collected enough data for their needs. Overall, AutoMicroED permits efficient structure elucidation for both novice and experienced users with comparable results to more laborious manual processing.

The cctbx.xfel suite of processing programs and tools allows fast, visual analysis of serial diffraction images from synchrotrons and XFELs. Built on DIALS and cctbx, cctbx.xfel is designed for real-time and post-experiment processing with a fully featured graphical user interface. Users can quickly identify hitrates, view diffraction patterns, analyze unit-cell isomorphism using clustering, and merge data using a metadata tagging approach that allows on-the-fly organization and visualization of processing results. This paper describes the fundamental algorithms and command-line programs used by cctbx.xfel, including the two main program dials.stills process, which performs spot-finding, indexing, geometric refinement, and integration, and cctbx.xfel.merge, which performs scaling, post-refinement, and merging. A discussion of merging statis-tics is presented and newer features are described, including random sub-sampling for indexing multi-lattice hits and {Delta}CC1/2 filtering to remove outliers. Finally we show a complex, heterogeneous sample containing hexagonal and monoclinic isoforms in P 63 and P 21. The isoforms are separated by unit cell clustering, and for each isoform we resolve a (pseudo-)merohedral indexing ambiguity.

Three-dimensional electron diffraction (3D ED), also known as microcrystal electron diffraction (MicroED), is an emerging method for determining structures of submicron-sized crystals. With the development of rapid and convenient data collection protocols, acquiring dozens of datasets in a single MicroED session has become routine. A fast and automated workflow for processing, scaling and merging a large number of MicroED datasets can significantly accelerate the structure determination process. Herein, we present an XDS-based graphical user interface for automated real-time and offline batch 3D ED/MicroED data processing (AutoLEI). We illustrate the functionality of the GUI through four examples, demonstrating both offline and real-time data processing capabilities. These examples include small organic molecules, metal-organic frameworks (MOFs), and proteins, showcasing the versatility and efficiency of the GUI in various applications. SynopsisA graphical user interface for real-time and offline 3D ED/MicroED data processing by XDS was developed. The GUI aims to improve efficiency, minimize redundant data processing work, and provide users with real-time feedback during data collection.

In cellulo crystallization is a rarely occurring event in nature. Recent advances, making use of heterologous overexpression, can promote the intracellular formation of protein crystals, but new tools are required to detect and to characterize these targets in the complex cell environment. In the present work we make use of Mask R-CNN, a Convolutional Neural Network (CNN) based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim to extract relevant information to support a semi-automated screening pipeline, with the purpose to aid in the development of the intracellular protein crystallization approach.

In standard rotational data collection for macromolecular crystallography data are normally collected from a single crystal, and the resulting data processing delivers metrics for data completeness and signal to noise that are well established. However, in serial crystallography it can be difficult to assess quickly whether enough data have been recorded to deliver a well scaled and complete dataset with sufficient signal to noise to address the scientific question being asked. Completeness alone is not an appropriate metric, as a nominally complete dataset can be obtained with a much smaller number of images, and thus multiplicity, than is needed to produce a final dataset with well estimated merged intensity values. Insufficient data result in alarmingly reasonable processing statistics and plausible electron density maps that contain almost no experimental signal, instead being dominated by the phases from the phasing model. We have therefore established a simple electron density-based test to determine whether enough data have been collected, and implemented this in the autoprocessing pipeline at the T-REXX endstation on beamline P14 at PETRA III. Importantly, the results of this test help guide decisions as to whether more data should be collected, or whether the experimenter can move onto a new time-point or sample. SynopsisWe describe a simple test to determine whether sufficient data have been collected during a serial crystallographic experiment, and its incorporation into the autoprocessing pipeline at the T- REXX endstation on beamline P14 at the PETRA III synchrotron.

The advent of ultrabright fourth generation X-ray light sources, including X-ray free-electron lasers (XFELs) and diffraction limited synchrotrons, has significantly advanced the field of serial macromolecular protein crystallography (SX). SX experiments demand a continuous supply of fresh microcrystalline sample, ideally while minimizing overall sample consumption. Here, we introduce a novel, robust, and user-friendly polymer film technology that can be assembled in various configurations to encapsulate protein microcrystals and provide sample support for SX. This system provides an efficient hydration barrier over extended durations while maintaining an exceptionally low X-ray background. We have validated this technology by assessing hydration retention under both ambient and ultra-high vacuum conditions, and by evaluating its mechanical stability under XFEL pulses. Furthermore, we have demonstrated the effectiveness of this approach in two room-temperature serial crystallography studies to determine the structure of a 24 kDa Rapid Encystment Phenotype (REP24) protein from Franciscella tularensis.

X-ray crystal diffraction has provided atomic-level structural information on biological macromolecules. Data quality determines the reliability of structural models. In most cases, multiple data sets are available from different crystals and/or collected with different experimental settings. Reliable metrics are critical to rank and select the data set with the highest quality. Many measures have been created or modified for data quality evaluation. However, some are duplicate in functionality, and some are likely misused due to misunderstanding, which causes confusion or problems, especially at synchrotron beamlines where experiments proceed quickly. In this work, these measures are studied through both theoretical analysis and experimental data with various characteristics, which demonstrated that: 1). {Rmerg, Rmeas, Rpim, CC1/2} all measure the equivalence of reflections, and the low-shell values of these metrics can be used as reliable indicators for correctness (or trueness) of Laue symmetry; 2). High-shell I/{sigma}I is a reliable and better indicator to select resolution cutoff while the overall value measures the overall strength of the data.

Powder X-ray diffraction (PXRD) measurements performed on platforms originally designed for single-crystal diffraction are strongly affected by how the powder sample is presented to the X-ray beam, including the delivery configuration and support geometry. Here, we developed a modified Terasaki-plate-based sample-delivery method for PXRD using a laboratory single-crystal diffractometer implemented with the XtalCheck-S plate-reader operational mode at Turkish Light Source. The method was regarded under comparable measurement conditions relative to a standard loop/pin-based and a grease-based Terasaki setup using 5-{[4-(2-Methoxyphenyl)piperazin-1-yl]methyl}-4-ethyl-4H-1,2,4-triazole-3-thiol as a model analyte. The loop-based method allowed only limited powder sampling, whereas the grease-based Terasaki setup enabled multi-well sample delivery but produced higher background and weaker diffraction profiles. Conversely, Kapton-sealed Terasaki ensured secure retention of small amounts powder while providing lower background and clearer diffraction patterns. Within short total data collection times of only 1-2 min, the Kapton-Terasaki method delivered the best overall PXRD performance among the tested methods. Search-match and profile-fitting analyses showed that all three approaches sampled the same crystalline material, while the Kapton-based method gave the lowest profile residual (Rp = 9.6%) and the most reliable whole-pattern profile. These results demonstrate that optimizing sample delivery, rather than modifying the core instrument hardware, can substantially extend PXRD capability on an existing in situ crystallography platform for rapid, laboratory-based screening and comparative multi-sample measurements.

The advent of serial crystallography has rejuvenated and popularised room temperature X-ray crystal structure determination. Structures determined at physiological temperature reveal protein flexibility and dynamics. In addition, challenging samples (e.g., large complexes, membrane proteins, and viruses) forming fragile crystals, are often difficult to harvest for cryo-crystallography. Moreover, a typical serial crystallography experiment requires a large number of microcrystals, mainly achievable through batch crystallisation. Many medically relevant samples are expressed in mammalian cell-lines, producing a meagre quantity of protein that is incompatible for batch crystallisation. This can limit the scope of serial crystallography approaches. Direct in-situ data collection from a 96-well crystallisation plate enables not only the identification of the best diffracting crystallisation condition, but also the possibility for structure determination at ambient conditions. Here, we describe an in situ serial crystallography (iSX) approach, facilitating direct measurement from crystallisation plates, mounted on a rapidly exchangeable universal plate holder deployed at a microfocus beamline, ID23-2, at the European Synchrotron Radiation Facility (ESRF). We applied our iSX approach on a challenging project, Autotaxin, a therapeutic target expressed in a stable human cell-line, to determine a structure in the lowest symmetry P1 space group at 3.0 [A] resolution. Our in situ data collection strategy provided a complete dataset for structure determination, while screening various crystallisation conditions. Our data analysis reveals that the iSX approach is highly efficient at a microfocus beamline, improving throughput and demonstrating how crystallisation plates can be routinely used as an alternative method of presenting samples for serial crystallography experiments at synchrotrons. SynopsisThe determination of a challenging structure in the P1 space group, the lowest symmetry possible, shows how our in-situ serial crystallography approach expands the application of crystallisation plates as a robust sample delivery method.

Time-resolved X-ray crystallography (TR-X) at synchrotrons and free electron lasers is a promising technique for recording dynamics of molecules at atomic resolution. While experimental methods for TR-X have proliferated and matured, data analysis is often difficult. Extracting small, time-dependent changes in signal is frequently a bottleneck for practitioners. Recent work demonstrated this challenge can be addressed when merging redundant observations by a statistical technique known as variational inference (VI). However, the variational approach to time-resolved data analysis requires identification of successful hyperparameters in order to optimally extract signal. In this case study, we present a successful application of VI to time-resolved changes in an enzyme, DJ-1, upon mixing with a substrate molecule, methylglyoxal. We present a strategy to extract high signal-to-noise changes in electron density from these data. Furthermore, we conduct an ablation study, in which we systematically remove one hyperparameter at a time to demonstrate the impact of each hyperparameter choice on the success of our model. We expect this case study will serve as a practical example for how others may deploy VI in order to analyze their time-resolved diffraction data.

The combination of high sensitivity and rapid readout makes it possible for electron-counting detectors to record cryogenic electron microscopy data faster and more accurately without increasing the exposure. This is especially useful for MicroED of macromolecular crystals where the strength of the diffracted signal at high resolution is comparable to the surrounding background. The ability to decrease the exposure also alleviates concerns about radiation damage which limits the information that can be recovered from a diffraction measurement. However, the dynamic range of electron-counting detectors requires careful data collection to avoid errors from coincidence loss. Nevertheless, these detectors are increasingly deployed in cryo-EM facilities, and several have been successfully used for MicroED. Provided coincidence loss can be minimized, electron-counting detectors bring high potential rewards.

In macromolecular crystallography radiation damage limits the amount of data that can be collected from a single crystal. It is often necessary to merge data sets from multiple crystals, for example small-wedge data collections on micro-crystals, in situ room-temperature data collections, and collection from membrane proteins in lipidic mesophase. Whilst indexing and integration of individual data sets may be relatively straightforward with existing software, merging multiple data sets from small wedges presents new challenges. Identification of a consensus symmetry can be problematic, particularly in the presence of a potential indexing ambiguity. Furthermore, the presence of non-isomorphous or poor-quality data sets may reduce the overall quality of the final merged data set. To facilitate and help optimise the scaling and merging of multiple data sets, we developed a new program, xia2.multiplex, which takes data sets individually integrated with DIALS and performs symmetry analysis, scaling and merging of multicrystal data sets. xia2.multiplex also performs analysis of various pathologies that typically affect multi-crystal data sets, including non-isomorphism, radiation damage and preferential orientation. After describing a number of use cases, we demonstrate the benefit of xia2.multiplex within a wider autoprocessing framework in facilitating a multi-crystal experiment collected as part of in situ room-temperature fragment screening experiments on the SARS-CoV-2 main protease.

Monte Carlo neutron ray-tracing simulations of time-of-flight (TOF)-Laue neutron macromolecular crystal diffraction (n-MX) using the McStas software package were done for the upcoming NMX Macromolecular Diffractometer at the European Spallation Source. Splitting neutron rays that arrive at the crystal lead to dramatic improvements in event formation with minimal computational overhead. The simulated event probability data was sampled using a new single-pass weighted reservoir sampling method, and processed like real n-MX data using DIALS. The effects of air and beamstop scatter on simulated data was investigated. SynopsisMonte Carlo simulations of neutron protein diffraction experiments provide useful data that models instrumental components that interact with neutrons, as well as the crystal diffraction itself. These data can be applied to instrument development, such as the commissioning of the NMX Macromolecular Diffractometer at ESS.