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Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch‐powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real‐time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real‐space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652–2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694–704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690–1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX. [ABSTRACT FROM AUTHOR]

Serial crystallography, born from groundbreaking experiments at the Linac Coherent Light Source in 2009, has evolved into a pivotal technique in structural biology. Initially pioneered at X‐ray free‐electron laser facilities, it has now expanded to synchrotron‐radiation facilities globally, with dedicated experimental stations enhancing its accessibility. This review gives an overview of current developments in serial crystallography, emphasizing recent results in time‐resolved crystallography, and discussing challenges and shortcomings. [ABSTRACT FROM AUTHOR]

Over the past two decades, serial X-ray crystallography has enabled the structure determination of a wide range of proteins. With the advent of X-ray free-electron lasers (XFELs), ever-smaller crystals have yielded high-resolution diffraction and structure determination. A crucial need to continue advancement is the efficient delivery of fragile and micrometre-sized crystals to the X-ray beam intersection. This paper presents an improved design of an all-polymer microfluidic `chip for room-temperature fixed-target serial crystallography that can be tailored to broadly meet the needs of users at either synchrotron or XFEL light sources. The chips are designed to be customized around different types of crystals and offer users a friendly, quick, convenient, ultra-low-cost and robust sample-delivery platform. Compared with the previous iteration of the chip [Gilbile et al. (2021), Lab Chip, 21, 4831-4845], the new design eliminates cleanroom fabrication. It has a larger imaging area to volume, while maintaining crystal hydration stability for both in situ crystallization or direct crystal slurry loading. Crystals of two model proteins, lysozyme and thaumatin, were used to validate the effectiveness of the design at both synchrotron (lysozyme and thaumatin) and XFEL (lysozyme only) facilities, yielding complete data sets with resolutions of 1.42, 1.48 and 1.70 Å, respectively. Overall, the improved chip design, ease of fabrication and high modifiability create a powerful, all-around sample-delivery tool that structural biologists can quickly adopt, especially in cases of limited sample volume and small, fragile crystals.

Structure-based drug design is highly dependent on the availability of structures of the protein of interest in complex with lead compounds. Ideally, this information can be used to guide the chemical optimization of a compound into a pharmaceutical drug candidate. A limitation of the main structural method used today – conventional X-ray crystallography – is that it only provides structural information about the protein complex in its frozen state. Serial crystallography is a relatively new approach that offers the possibility to study protein structures at room temperature (RT). Here, we explore the use of serial crystallography to determine the structures of the pharmaceutical target, soluble epoxide hydrolase. We introduce a new method to screen for optimal microcrystallization conditions suitable for use in serial crystallography and present a number of RT ligand-bound structures of our target protein. From a comparison between the RT structural data and previously published cryo-temperature structures, we describe an example of a temperature-dependent difference in the ligand-binding mode and observe that flexible loops are better resolved at RT. Finally, we discuss the current limitations and potential future advances of serial crystallography for use within pharmaceutical drug discovery.

Crystallography is a quintessential method for determining the atomic structure of crystals. The most common implementation of crystallography uses single crystals that must be of sufficient size, typically tens of micrometres or larger, depending on the complexity of the crystal structure. The emergence of serial data-collection methods in crystallography, particularly for time-resolved experiments, opens up opportunities to develop new routes to structure determination for nanocrystals and ensembles of crystals. Fluctuation X-ray scattering is a correlation-based approach for single-particle imaging from ensembles of identical particles, but has yet to be applied to crystal structure determination. Here, an iterative algorithm is presented that recovers crystal structure-factor intensities from fluctuation X-ray scattering correlations. The capabilities of this algorithm are demonstrated by recovering the structure of three small-molecule crystals and a protein crystal from simulated fluctuation X-ray scattering correlations. This method could facilitate the recovery of structure-factor intensities from crystals in serial crystallography experiments and relax sample requirements for crystallography experiments.

Serial crystallography experiments at synchrotron and X‐ray free‐electron laser (XFEL) sources are producing crystallographic data sets of ever‐increasing volume. While these experiments have large data sets and high‐frame‐rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real‐time data classification pipeline is essential to differentiate reliably between useful and non‐useful images, typically known as 'hit' and 'miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature‐point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real‐time feature extraction algorithm called modified and parallelized FAST (MP‐FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field‐programmable gate arrays are implemented and their performances compared. Finally, MP‐FAST‐based image classification is evaluated using a multi‐layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers. [ABSTRACT FROM AUTHOR]

Data collection at X‐ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high‐dynamic‐range gain‐switching X‐ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure‐factor amplitudes for serial crystallography or single‐particle imaging experiments. Here, a new data‐classification tool is reported that offers a variety of machine‐learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy‐to‐use graphical user interface, specifically designed to support the detectors, file formats and software available at most X‐ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X‐ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data‐analysis tasks such as hit finding during an experiment, without needing to write code. [ABSTRACT FROM AUTHOR]

A considerable bottleneck in serial crystallography at XFEL and synchrotron sources is the efficient production of large quantities of homogenous, well diffracting microcrystals. Efficient high‐throughput screening of batch‐grown microcrystals and the determination of ground‐state structures from different conditions is thus of considerable value in the early stages of a project. Here, a highly sample‐efficient methodology to measure serial crystallography data from microcrystals by raster scanning within standard in situ 96‐well crystallization plates is described. Structures were determined from very small quantities of microcrystal suspension and the results were compared with those from other sample‐delivery methods. The analysis of a two‐dimensional batch crystallization screen using this method is also described as a useful guide for further optimization and the selection of appropriate conditions for scaling up microcrystallization. [ABSTRACT FROM AUTHOR]

Enzymes populate ensembles of structures with intrinsically different catalytic proficiencies that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography (MISC) at an X-ray free electron laser (XFEL) to observe catalysis in a designed mutant (G150T) isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations and formation of the thioimidate catalytic intermediate selects for catalytically competent substates. A prior proposal for active site cysteine charge-coupled conformational changes in ICH is validated by determining structures of the enzyme over a range of pH values. A combination of large molecular dynamics simulations of the enzyme in crystallo and time-resolved electron density maps shows that ionization of the general acid Asp17 during catalysis causes additional conformational changes that propagate across the dimer interface, connecting the two active sites. These ionization-linked changes in the ICH conformational ensemble permit water to enter the active site in a location that is poised for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

Temperature directly influences the function and structure of proteins. Crystal structures determined at room temperature offer more biologically relevant structural information regarding flexibility, rigidity, and thermal motion than those determined by conventional cryocrystallography. Crystal structures can be determined at room temperature using conventional macromolecular crystallography (MX) or serial crystallography (SX) techniques. Among these, MX may theoretically be affected by radiation damage or X-ray heating, potentially resulting in differences between the room temperature structures determined by MX and SX, but this has not been fully elucidated. In this study, the room temperature structure of xylanase GH11 from Thermoanaerobacterium saccharolyticum was determined by MX (RT-TsaGH11-MX). The RT-TsaGH11-MX exhibited both the open and closed conformations of the substrate-binding cleft within the β-sandwich fold. The RT-TsaGH11-MX showed distinct structural changes and molecular flexibility when compared with the RT-TsaGH11 determined via serial synchrotron crystallography. The notable molecular conformation and flexibility of the RT-TsaGH11-MX may be induced by radiation damage and X-ray heating. These findings will broaden our understanding of the potential limitations of room temperature structures determined by MX. [ABSTRACT FROM AUTHOR]

Recently, we introduced the liquid application method for time‐resolved analyses (LAMA). The time‐consuming cleaning cycles required for the substrate solution exchange and storage of the sensitive droplet‐dispenser nozzles present practical challenges. In this work, a dispenser cleaning system for the semi‐automated cleaning of the piezo‐actuator‐driven picolitre‐droplet dispensers required for LAMA is introduced to streamline typical workflows. [ABSTRACT FROM AUTHOR]

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses. [ABSTRACT FROM AUTHOR]

[Display omitted] The use of monoolein/water mixtures in serial crystallography experiments using high-viscosity injectors (HVI) results in significant departures from equilibrium behaviour. This is expected to include changes in phase, viscosity, and associated flow behaviour. It should be possible to detect these changes, in-situ, using a combination of polarisation and rheology characterisation techniques. A systematic study was performed using monoolein, varying the water content to create a range of mixtures. Injection induced phase changes within the HVI flow were established using real-time cross-polarization measurements. Dynamic flow characteristics and viscosity was characterized by particle tracking and rheology. HVI injection induces deformation and phase changes within monoolein (MO)/water mixtures which can be detected through variations in the transmitted intensity during in-situ polarisation studies. The heterogeneity of the extruded sample results in a highly viscous cubic phase in the central region of the stream and a less viscous lamellar-rich phase at the edges adjacent to the walls. The extent of these variations depends on sample composition and injection conditions. Shear-thinning behaviour and increasing heterogeneity in the vicinity of the capillary walls under dynamic flow conditions. This is the first report observing injection induced dynamical behaviour in MO/water mixtures under realistic flow conditions. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per‐pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X‐ray free‐electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real‐time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python‐based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics‐based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis. [ABSTRACT FROM AUTHOR]

Fixed targets are a popular form of sample-delivery system used in serial crystallography at synchrotron and X-ray free-electron laser sources. They offer a wide range of sample-preparation options and are generally easy to use. The supports are typically made from silicon, quartz or polymer. Of these, currently, only silicon offers the ability to perform an aperture-aligned data collection where crystals are loaded into cavities in precise locations and sequentially rastered through, in step with the X-ray pulses. The polymer-based fixed targets have lacked the precision fabrication to enable this data-collection strategy and have been limited to directed-raster scans with crystals randomly distributed across the polymer surface. Here, the fabrication and first results from a new polymer-based fixed target, the micro-structured polymer fixed targets (MISP chips), are presented. MISP chips, like those made from silicon, have a precise array of cavities and fiducial markers. They consist of a structured polymer membrane and a stabilization frame. Crystals can be loaded into the cavities and the excess crystallization solution removed through apertures at their base. The fiducial markers allow for a rapid calculation of the aperture locations. The chips have a low X-ray background and, since they are optically transparent, also allow for an a priori analysis of crystal locations. This location mapping could, ultimately, optimize hit rates towards 100%. A black version of the MISP chip was produced to reduce light contamination for optical-pump/X-ray probe experiments. A study of the loading properties of the chips reveals that these types of fixed targets are best optimized for crystals of the order of 25 µm, but quality data can be collected from crystals as small as 5 µm. With the development of these chips, it has been proved that polymer-based fixed targets can be made with the precision required for aperture-alignment-based data-collection strategies. Further work can now be directed towards more cost-effective mass fabrication to make their use more sustainable for serial crystallography facilities and users.

Serial crystallography (SX) enables the determination of the structure of macromolecules or small molecules with minimal radiation damage. In particular, biomolecule structures determined using the SX technique have the advantage of providing room-temperature crystal structures with high biological relevance. The SX technique requires numerous crystals to be collected to complete three-dimensional structural information. To minimize crystal sample consumption, we introduced SX data collection with fixed-target (FT) pink-beam serial synchrotron crystallography (SSX) at the 1C beamline of Pohang Light Source II. A new sample holder consisting of a magnetic frame with a nylon mesh was developed for easy sample handling. The FT-pink-SSX diffraction data were collected by continuously scanning X-rays using a stepping motor. The room-temperature structures of glucose isomerase and lysozyme were successfully determined at a resolution of 1.7 and 2.2 Å, respectively. The use of pink-beam FT-SSX in experimental applications and data acquisition for large beam sizes is discussed. Our results provide useful information for future pink-beam SSX and SX data collection using large X-ray beams. [ABSTRACT FROM AUTHOR]

Serial crystallography has emerged as an important tool for structural studies of integral membrane proteins. The ability to collect data from micrometre-sized weakly diffracting crystals at room temperature with minimal radiation damage has opened many new opportunities in time-resolved studies and drug discovery. However, the production of integral membrane protein microcrystals in lipidic cubic phase at the desired crystal density and quantity is challenging. This paper introduces VIALS (versatile approach to high-density microcrystals in lipidic cubic phase for serial crystallography), a simple, fast and efficient method for preparing hundreds of microlitres of high-density microcrystals suitable for serial X-ray diffraction experiments at both synchrotron and free-electron laser sources. The method is also of great benefit for rational structure-based drug design as it facilitates in situ crystal soaking and rapid determination of many cocrystal structures. Using the VIALS approach, room-temperature structures are reported of (i) the archaerhodopsin-3 protein in its dark-adapted state and 110 ns photocycle intermediate, determined to 2.2 and 1.7 Å, respectively, and (ii) the human A2A adenosine receptor in complex with two different ligands determined to a resolution of 3.5 Å. [ABSTRACT FROM AUTHOR]

Serial crystallography (SX) is an emerging technique that can be used to determine the noncryogenic crystal structure of macromolecules while minimizing radiation damage. Applying SX using pump-probe or mix-and-inject techniques enables the observation of time-resolved molecular reactions and dynamics in macromolecules. After the successful demonstration of the SX experimental technique with structure determination in serial femtosecond crystallography using an X-ray free electron laser, this method was adapted to the synchrotron, leading to the development of serial synchrotron crystallography (SSX). SSX offers new opportunities for researchers to leverage SX techniques, contributing to the advancement of structural biology and offering a deeper understanding of the structure and function of macromolecules. This review covers the background and advantages of SSX and its experimental approach. It also discusses important considerations when conducting SSX experiments.

Serial femtosecond crystallography (SFX) is a powerful technique that exploits X-ray free-electron lasers to determine the structure of macro-molecules at room temperature. Despite the impressive exposition of structural details with this novel crystallographic approach, the methods currently available to introduce crystals into the path of the X-ray beam sometimes exhibit serious drawbacks. Samples requiring liquid injection of crystal slurries consume large quantities of crystals (at times up to a gram of protein per data set), may not be compatible with vacuum configurations on beamlines or provide a high background due to additional sheathing liquids present during the injection. Proposed and characterized here is the use of an immiscible inert oil phase to supplement the flow of sample in a hybrid microfluidic 3D-printed co-flow device. Co-flow generation is reported with sample and oil phases flowing in parallel, resulting in stable injection conditions for two different resin materials experimentally. A numerical model is presented that adequately predicts these flow-rate conditions. The co-flow generating devices reduce crystal clogging effects, have the potential to conserve protein crystal samples up to 95% and will allow degradation-free light-induced time-resolved SFX.

The term robustness in statistics refers to methods that are generally insensitive to deviations from model assumptions. In other words, robust methods are able to preserve their accuracy even when the data do not perfectly fit the statistical models. Robust statistical analyses are particularly effective when analysing mixtures of probability distributions. Therefore, these methods enable the discretization of X‐ray serial crystallography data into two probability distributions: a group comprising true data points (for example the background intensities) and another group comprising outliers (for example Bragg peaks or bad pixels on an X‐ray detector). These characteristics of robust statistical analysis are beneficial for the ever‐increasing volume of serial crystallography (SX) data sets produced at synchrotron and X‐ray free‐electron laser (XFEL) sources. The key advantage of the use of robust statistics for some applications in SX data analysis is that it requires minimal parameter tuning because of its insensitivity to the input parameters. In this paper, a software package called Robust Gaussian Fitting library (RGFlib) is introduced that is based on the concept of robust statistics. Two methods are presented based on the concept of robust statistics and RGFlib for two SX data‐analysis tasks: (i) a robust peak‐finding algorithm and (ii) an automated robust method to detect bad pixels on X‐ray pixel detectors. [ABSTRACT FROM AUTHOR]

Fixed‐target crystallography has become a widely used approach for serial crystallography at both synchrotron and X‐ray free‐electron laser (XFEL) sources. A plethora of fixed targets have been developed at different facilities and by various manufacturers, with different characteristics and dimensions and with little or no emphasis on standardization. These many fixed targets have good reasons for their design, shapes, fabrication materials and the presence or absence of apertures and fiducials, reflecting the diversity of serial experiments. Given this, it would be a Sisyphean task to design and manufacture a new standard fixed target that would satisfy all possible experimental configurations. Therefore, a simple standardized descriptor to fully describe fixed targets is proposed rather than a standardized device. This descriptor is a dictionary that could be read by fixed‐target beamline software and straightforwardly allow data collection from fixed targets new to that beamline. The descriptor would therefore allow a much easier exchange of fixed targets between sources and facilitate the uptake of new fixed targets, benefiting beamlines, users and manufacturers. This descriptor was first presented at, and was developed following, a meeting of representatives from multiple synchrotron and XFEL sources in Hamburg in January 2023. [ABSTRACT FROM AUTHOR]

Serial crystallography is a rapidly developing method for the determination of the structure of biomolecules at room temperature at near-atomic resolution from an ensemble of small crystals. Numerous advances in detectors, data analysis pipelines, sample delivery methods, and crystallization protocols expand the scope of structural biology to understand the fundamental processes that take place in living cells. Many experimental strategies for serial crystallography are in use, depending on the type and sizes of the crystals or other needs of the experiment. Such strategies should ideally minimize the wastage of samples or beamtime without compromising experimental goals. This paper proposes a way to optimize beamtime utilization in serial crystallography experiments that use fixed-target sample delivery methods, such as chips. The strategy involves two key steps. Firstly, a fast raster scan of the chip is performed to determine the positions of the crystals based on their diffraction. Subsequently, a rotational series is collected at each identified crystal position, covering a narrow range of chip orientations. This approach enables the exclusion of empty positions during data acquisition, resulting in significant savings in beam time utilization and a reduced volume of measured data. [ABSTRACT FROM AUTHOR]

Cyanobacteriochromes (CBCRs) are small, linear tetrapyrrole (bilin)-binding photoreceptors in the phytochrome superfamily that regulate diverse light-mediated adaptive processes in cyanobacteria. More spectrally diverse than canonical red/far-red-sensing phytochromes, CBCRs were thought to be restricted to sensing visible and near UV light until recently when several subfamilies with far-red-sensing representatives (frCBCRs) were discovered. Two of these frCBCRs subfamilies have been shown to incorporate bilin precursors with larger pi-conjugated chromophores, while the third frCBCR subfamily uses the same phycocyanobilin precursor found in the bulk of the known CBCRs. To elucidate the molecular basis of far-red light perception by this third frCBCR subfamily, we determined the crystal structure of the far-red-absorbing dark state of one such frCBCR Anacy_2551g3 from Anabaena cylindrica PCC 7122 which exhibits a reversible far-red/orange photocycle. Determined by room temperature serial crystallography and cryocrystallography, the refined 2.7-Å structure reveals an unusual all-Z,syn configuration of the phycocyanobilin (PCB) chromophore that is considerably less extended than those of previously characterized red-light sensors in the phytochrome superfamily. Based on structural and spectroscopic comparisons with other bilin-binding proteins together with site-directed mutagenesis data, our studies reveal protein-chromophore interactions that are critical for the atypical bathochromic shift. Based on these analyses, we propose that far-red absorption in Anacy_2551g3 is the result of the additive effect of two distinct red-shift mechanisms involving cationic bilin lactim tautomers stabilized by a constrained all-Z,syn conformation and specific interactions with a highly conserved anionic residue.

Most crystallographic data processing methods use pixel integration. In serial femtosecond crystallography (SFX), the intricate interaction between the reciprocal lattice point and the Ewald sphere is integrated out by averaging symmetrically equivalent observations recorded across a large number (104-106) of exposures. Although sufficient for generating biological insights, this approach converges slowly, and using it to accurately measure anomalous differences has proved difficult. This report presents a novel approach for increasing the accuracy of structure factors obtained from SFX data. A physical model describing all observed pixels is defined to a degree of complexity such that it can decouple the various contributions to the pixel intensities. Model dependencies include lattice orientation, unit-cell dimensions, mosaic structure, incident photon spectra and structure factor amplitudes. Maximum likelihood estimation is used to optimize all model parameters. The application of prior knowledge that structure factor amplitudes are positive quantities is included in the form of a reparameterization. The method is tested using a synthesized SFX dataset of ytterbium(III) lysozyme, where each X-ray laser pulse energy is centered at 9034 eV. This energy is 100 eV above the Yb3+ L-III absorption edge, so the anomalous difference signal is stable at 10 electrons despite the inherent energy jitter of each femtosecond X-ray laser pulse. This work demonstrates that this approach allows the determination of anomalous structure factors with very high accuracy while requiring an order-of-magnitude fewer shots than conventional integration-based methods would require to achieve similar results.

Temperature directly influences the function and structure of proteins. Crystal structures determined at room temperature offer more biologically relevant structural information regarding flexibility, rigidity, and thermal motion than those determined by conventional cryocrystallography. Crystal structures can be determined at room temperature using conventional macromolecular crystallography (MX) or serial crystallography (SX) techniques. Among these, MX may theoretically be affected by radiation damage or X-ray heating, potentially resulting in differences between the room temperature structures determined by MX and SX, but this has not been fully elucidated. In this study, the room temperature structure of xylanase GH11 from Thermoanaerobacterium saccharolyticum was determined by MX (RT-TsaGH11-MX). The RT-TsaGH11-MX exhibited both the open and closed conformations of the substrate-binding cleft within the β-sandwich fold. The RT-TsaGH11-MX showed distinct structural changes and molecular flexibility when compared with the RT-TsaGH11 determined via serial synchrotron crystallography. The notable molecular conformation and flexibility of the RT-TsaGH11-MX may be induced by radiation damage and X-ray heating. These findings will broaden our understanding of the potential limitations of room temperature structures determined by MX.

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses.

A 'catcher' based on a revolving cylindrical collector is described. The simple and inexpensive device reduces free‐jet instabilities inherent to high‐viscosity extrusion injection, facilitating delivery of microcrystals for serial diffraction X‐ray crystallography. [ABSTRACT FROM AUTHOR]

Macromolecular crystallography (MX) is the dominant means of determining the three-dimensional structures of biological macromolecules. Over the last few decades, most MX data have been collected at synchrotron beamlines using a large number of different detectors produced by various manufacturers and taking advantage of various protocols and goniometries. These data came in their own formats: sometimes proprietary, sometimes open. The associated metadata rarely reached the degree of completeness required for data management according to Findability, Accessibility, Interoperability and Reusability (FAIR) principles. Efforts to reuse old data by other investigators or even by the original investigators some time later were often frustrated. In the culmination of an effort dating back more than two decades, a large portion of the research community concerned with high data-rate macromolecular crystallography (HDRMX) has now agreed to an updated specification of data and metadata for diffraction images produced at synchrotron light sources and X-ray free-electron lasers (XFELs). This 'Gold Standard' will facilitate the processing of data sets independent of the facility at which they were collected and enable data archiving according to FAIR principles, with a particular focus on interoperability and reusability. This agreed standard builds on the NeXus/HDF5 NXmx application definition and the International Union of Crystallo-graphy (IUCr) imgCIF/CBF dictionary, and it is compatible with major data-processing programs and pipelines. Just as with the IUCr CBF/imgCIF standard from which it arose and to which it is tied, the NeXus/HDF5 NXmx Gold Standard application definition is intended to be applicable to all detectors used for crystallography, and all hardware and software developers in the field are encouraged to adopt and contribute to the standard.

Serial crystallography data can be challenging to index, as each frame is processed individually, rather than being processed as a whole like in conventional X-ray single-crystal crystallography. An algorithm has been developed to index still diffraction patterns arising from small-unit-cell samples. The algorithm is based on the matching of reciprocal-lattice vector pairs, as developed for Laue microdiffraction data indexing, combined with three-dimensional pattern matching using a nearest-neighbors approach. As a result, large-bandpass data (e.g. 5-24 keV energy range) and monochromatic data can be processed, the main requirement being prior knowledge of the unit cell. Angles calculated in the vicinity of a few theoretical and experimental reciprocal-lattice vectors are compared, and only vectors with the highest number of common angles are selected as candidates to obtain the orientation matrix. Global matching on the entire pattern is then checked. Four indexing options are available, two for the ranking of the theoretical reciprocal-lattice vectors and two for reducing the number of possible candidates. The algorithm has been used to index several data sets collected under different experimental conditions on a series of model samples. Knowing the crystallographic structure of the sample and using this information to rank the theoretical reflections based on the structure factors helps the indexing of large-bandpass data for the largest-unit-cell samples. For small-bandpass data, shortening the candidate list to determine the orientation matrix should be based on matching pairs of reciprocal-lattice vectors instead of triplet matching.

Innovative new crystallographic methods are facilitating structural studies from ever smaller crystals of biological macromolecules. In particular, serial X-ray crystallography and microcrystal electron diffraction (MicroED) have emerged as useful methods for obtaining structural information from crystals on the nanometre to micrometre scale. Despite the utility of these methods, their implementation can often be difficult, as they present many challenges that are not encountered in traditional macromolecular crystallography experiments. Here, XFEL serial crystallography experiments and MicroED experiments using batch-grown microcrystals of the enzyme cyclophilin A are described. The results provide a roadmap for researchers hoping to design macromolecular microcrystallography experiments, and they highlight the strengths and weaknesses of the two methods. Specifically, we focus on how the different physical conditions imposed by the sample-preparation and delivery methods required for each type of experiment affect the crystal structure of the enzyme.

For serial femtosecond crystallography at X-ray free-electron lasers, which entails collection of single-pulse diffraction patterns from a constantly refreshed supply of microcrystalline sample, delivery of the sample into the X-ray beam path while maintaining low background remains a technical challenge for some experiments, especially where this methodology is applied to relatively low-ordered samples or those difficult to purify and crystallize in large quantities. This work demonstrates a scheme to encapsulate biological samples using polymer thin films and graphene to maintain sample hydration in vacuum conditions. The encapsulated sample is delivered into the X-ray beam on fixed targets for rapid scanning using the Roadrunner fixed-target system towards a long-term goal of low-background measurements on weakly diffracting samples. As a proof of principle, we used microcrystals of the 24 kDa rapid encystment protein (REP24) to provide a benchmark for polymer/graphene sandwich performance. The REP24 microcrystal unit cell obtained from our sandwiched in-vacuum sample was consistent with previously established unit-cell parameters and with those measured by us without encapsulation in humidified helium, indicating that the platform is robust against evaporative losses. While significant scattering from water was observed because of the sample-deposition method, the polymer/graphene sandwich itself was shown to contribute minimally to background scattering.

A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built. Estimating the error in the merged reflection intensities requires the understanding and propagation of all of the sources of error arising from the measurements. One type of error, which is well understood, is the counting error introduced when the detector counts X-ray photons. Thus, if other types of random errors (such as readout noise) as well as uncertainties in systematic corrections (such as from X-ray attenuation) are completely understood, they can be propagated along with the counting error, as appropriate. In practice, most software packages propagate as much error as they know how to model and then include error-adjustment terms that scale the error estimates until they explain the variance among the measurements. If this is performed carefully, then during SAD phasing likelihood-based approaches can make optimal use of these error estimates, increasing the chance of a successful structure solution. In serial crystallography, SAD phasing has remained challenging, with the few examples of de novo protein structure solution each requiring many thousands of diffraction patterns. Here, the effects of different methods of treating the error estimates are estimated and it is shown that using a parametric approach that includes terms proportional to the known experimental uncertainty, the reflection intensity and the squared reflection intensity to improve the error estimates can allow SAD phasing even from weak zinc anomalous signal.

Serial crystallography at conventional synchrotron light sources (SSX) offers the possibility to routinely collect data at room temperature using micrometre-sized crystals of biological macromolecules. However, SSX data collection is not yet as routine and currently takes significantly longer than the standard rotation series cryo-crystallography. Thus, its use for high-throughput approaches, such as fragment-based drug screening, where the possibility to measure at physiological temperatures would be a great benefit, is impaired. On the way to high-throughput SSX using a conveyor belt based sample delivery system – the CFEL TapeDrive – with three different proteins of biological relevance (Klebsiella pneumoniae CTX-M-14 β-lactamase, Nectria haematococca xylanase GH11 and Aspergillus flavus urate oxidase), it is shown here that complete datasets can be collected in less than a minute and only minimal amounts of sample are required.

Rhodopsin is a G‐protein‐coupled receptor that detects light and initiates the intracellular signalling cascades that underpin vertebrate vision. Light sensitivity is achieved by covalent linkage to 11‐cis retinal, which isomerizes upon photo‐absorption. Serial femtosecond crystallography data collected from rhodopsin microcrystals grown in the lipidic cubic phase were used to solve the room‐temperature structure of the receptor. Although the diffraction data showed high completeness and good consistency to 1.8 Å resolution, prominent electron‐density features remained unaccounted for throughout the unit cell after model building and refinement. A deeper analysis of the diffraction intensities uncovered the presence of a lattice‐translocation defect (LTD) within the crystals. The procedure followed to correct the diffraction intensities for this pathology enabled the building of an improved resting‐state model. The correction was essential to both confidently model the structure of the unilluminated state and interpret the light‐activated data collected after photo‐excitation of the crystals. It is expected that similar cases of LTD will be observed in other serial crystallography experiments and that correction will be required in a variety of systems. [ABSTRACT FROM AUTHOR]

Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large‐scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the 'bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers. [ABSTRACT FROM AUTHOR]

For roughly two decades, cryocrystallography has been the overwhelmingly dominant method for determining high‐resolution biomolecular structures. Competition from single‐particle cryo‐electron microscopy and micro‐electron diffraction, increased interest in functionally relevant information that may be missing or corrupted in structures determined at cryogenic temperature, and interest in time‐resolved studies of the biomolecular response to chemical and optical stimuli have driven renewed interest in data collection at room temperature and, more generally, at temperatures from the protein–solvent glass transition near 200 K to ∼350 K. Fischer has recently reviewed practical methods for room‐temperature data collection and analysis [Fischer (2021), Q. Rev. Biophys.54, e1]. Here, the key advantages and physical principles of, and methods for, crystallographic data collection at noncryogenic temperatures and some factors relevant to interpreting the resulting data are discussed. For room‐temperature data collection to realize its potential within the structural biology toolkit, streamlined and standardized methods for delivering crystals prepared in the home laboratory to the synchrotron and for automated handling and data collection, similar to those for cryocrystallography, should be implemented. [ABSTRACT FROM AUTHOR]

Serial crystallography (SX) enables the determination of the structure of macromolecules or small molecules with minimal radiation damage. In particular, biomolecule structures determined using the SX technique have the advantage of providing room-temperature crystal structures with high biological relevance. The SX technique requires numerous crystals to be collected to complete three-dimensional structural information. To minimize crystal sample consumption, we introduced SX data collection with fixed-target (FT) pink-beam serial synchrotron crystallography (SSX) at the 1C beamline of Pohang Light Source II. A new sample holder consisting of a magnetic frame with a nylon mesh was developed for easy sample handling. The FT-pink-SSX diffraction data were collected by continuously scanning X-rays using a stepping motor. The room-temperature structures of glucose isomerase and lysozyme were successfully determined at a resolution of 1.7 and 2.2 Å, respectively. The use of pink-beam FT-SSX in experimental applications and data acquisition for large beam sizes is discussed. Our results provide useful information for future pink-beam SSX and SX data collection using large X-ray beams.

Structural biology is in the midst of a revolution fueled by faster and more powerful instruments capable of delivering orders of magnitude more data than their predecessors. This increased pace in data gathering introduces new experimental and computational challenges, frustrating real-time processing and interpretation of data and requiring long-term solutions for data archival and retrieval. This combination of challenges and opportunities is driving the exploration of new areas of structural biology, including studies of macromolecular dynamics and the investigation of molecular ensembles in search of a better understanding of conformational landscapes. The next generation of instruments promises to yield even greater data rates, requiring a concerted effort by institutions, centers and individuals to extract meaning from every bit and make data accessible to the community at large, facilitating data mining efforts by individuals or groups as analysis tools improve.

X‐ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X‐ray sources and enabled by employing high‐frame‐rate X‐ray detectors. The analysis of large data sets is done via automatic algorithms that are vulnerable to imperfections in the detector and noise inherent with the detection process. By improving the model of the behaviour of the detector, data can be analysed more reliably and data storage costs can be significantly reduced. One major requirement is a software mask that identifies defective pixels in diffraction frames. This paper introduces a methodology and program based upon concepts of machine learning, called robust mask maker (RMM), for the generation of bad‐pixel masks for large‐area X‐ray pixel detectors based on modern robust statistics. It is proposed to discriminate normally behaving pixels from abnormal pixels by analysing routine measurements made with and without X‐ray illumination. Analysis software typically uses a Bragg peak finder to detect Bragg peaks and an indexing method to detect crystal lattices among those peaks. Without proper masking of the bad pixels, peak finding methods often confuse the abnormal values of bad pixels in a pattern with true Bragg peaks and flag such patterns as useful regardless, leading to storage of enormous uninformative data sets. Also, it is computationally very expensive for indexing methods to search for crystal lattices among false peaks and the solution may be biased. This paper shows how RMM vastly improves peak finders and prevents them from labelling bad pixels as Bragg peaks, by demonstrating its effectiveness on several serial crystallography data sets. [ABSTRACT FROM AUTHOR]

An approach for serial crystallography experiments based on wedged-data collection is described. This is an alternative method for recording in situ X-ray diffraction data on crystalline samples efficiently loaded in an X-ray compatible microfluidic chip. Proper handling of the microfluidic chip places crystalline samples at geometrically known positions with respect to the focused X-ray interaction area for serial data collection of small wedges. The integration of this strategy takes advantage of the greatly modular sample environment available on the endstation, which allows access to both in situ and more classical cryo-crystallography with minimum time loss. The method represents another optional data collection approach that adds up to the already large set of methods made available to users. Coupled with the advances in processing serial crystallography data, the wedged-data collection strategy proves highly efficient in minimizing the amount of required sample crystals for recording a complete dataset. From the advances in microfluidic technology presented here, high-throughput room-temperature crystallography experiments may become routine and should be easily extended to industrial use.

CmABCB1 is a Cyanidioschyzon merolae homolog of human ABCB1, a well known ATP-binding cassette (ABC) transporter responsible for multi-drug resistance in various cancers. Three-dimensional structures of ABCB1 homologs have revealed the snapshots of inward- and outward-facing states of the transporters in action. However, sufficient information to establish the sequential movements of the open–close cycles of the alternating-access model is still lacking. Serial femtosecond crystallography (SFX) using X-ray free-electron lasers has proven its worth in determining novel structures and recording sequential conformational changes of proteins at room temperature, especially for medically important membrane proteins, but it has never been applied to ABC transporters. In this study, 7.7 monoacylglycerol with cholesterol as the host lipid was used and obtained well diffracting microcrystals of the 130 kDa CmABCB1 dimer. Successful SFX experiments were performed by adjusting the viscosity of the crystal suspension of the sponge phase with hydroxypropyl methylcellulose and using the high-viscosity sample injector for data collection at the SACLA beamline. An outward-facing structure of CmABCB1 at a maximum resolution of 2.22 Å is reported, determined by SFX experiments with crystals formed in the lipidic cubic phase (LCP-SFX), which has never been applied to ABC transporters. In the type I crystal, CmABCB1 dimers interact with adjacent molecules via not only the nucleotide-binding domains but also the transmembrane domains (TMDs); such an interaction was not observed in the previous type II crystal. Although most parts of the structure are similar to those in the previous type II structure, the substrate-exit region of the TMD adopts a different configuration in the type I structure. This difference between the two types of structures reflects the flexibility of the substrate-exit region of CmABCB1, which might be essential for the smooth release of various substrates from the transporter.

Serial crystallography is a rapidly developing method for the determination of the structure of biomolecules at room temperature at near-atomic resolution from an ensemble of small crystals. Numerous advances in detectors, data analysis pipelines, sample delivery methods, and crystallization protocols expand the scope of structural biology to understand the fundamental processes that take place in living cells. Many experimental strategies for serial crystallography are in use, depending on the type and sizes of the crystals or other needs of the experiment. Such strategies should ideally minimize the wastage of samples or beamtime without compromising experimental goals. This paper proposes a way to optimize beamtime utilization in serial crystallography experiments that use fixed-target sample delivery methods, such as chips. The strategy involves two key steps. Firstly, a fast raster scan of the chip is performed to determine the positions of the crystals based on their diffraction. Subsequently, a rotational series is collected at each identified crystal position, covering a narrow range of chip orientations. This approach enables the exclusion of empty positions during data acquisition, resulting in significant savings in beam time utilization and a reduced volume of measured data.

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

Previous proof-of-concept measurements on single-layer two-dimensional membrane-protein crystals performed at X-ray free-electron lasers (FELs) have demonstrated that the collection of meaningful diffraction patterns, which is not possible at synchrotrons because of radiation-damage issues, is feasible. Here, the results obtained from the analysis of a thousand single-shot, room-temperature X-ray FEL diffraction images from two-dimensional crystals of a bacteriorhodopsin mutant are reported in detail. The high redundancy in the measurements boosts the intensity signal-to-noise ratio, so that the values of the diffracted intensities can be reliably determined down to the detector-edge resolution of 4 Å. The results show that two-dimensional serial crystallography at X-ray FELs is a suitable method to study membrane proteins to near-atomic length scales at ambient temperature. The method presented here can be extended to pump-probe studies of optically triggered structural changes on submillisecond timescales in two-dimensional crystals, which allow functionally relevant large-scale motions that may be quenched in three-dimensional crystals.

Room‐temperature biological crystallography has seen a resergence in recent years and a collection of articles recently published in IUCrJ, Acta Cryst. D Structural Biology and Acta Cryst. F Structural Biology Communications, have been collected together to produce a virtual special issue at https://journals.iucr.org/special_issues/2022/RT/. [ABSTRACT FROM AUTHOR]

Serial femtosecond crystallography (SFX) enables the determination of roomtemperature crystal structures of macromolecules with minimized radiation damage and provides time-resolved molecular dynamics by pump--probe or mixand- inject experiments. In SFX, a variety of sample delivery methods with unique advantages have been developed and applied. The combination of existing sample delivery methods can enable a new approach to SFX data collection that combines the advantages of the individual methods. This study introduces a combined inject-and-transfer system (BITS) method for sample delivery in SFX experiments: a hybrid injection and fixed-target scanning method. BITS allows for solution samples to be reliably deposited on ultraviolet ozone (UVO)-treated polyimide films, at a minimum flow rate of 0.5 nl min-1, in both vertical and horizontal scanning modes. To utilize BITS in SFX experiments, lysozyme crystal samples were embedded in a viscous lard medium and injected at flow rates of 50-100 nl min-1 through a syringe needle onto a UVO-treated polyimide film, which was mounted on a fixed-target scan stage. The crystal samples deposited on the film were raster scanned with an X-ray free electron laser using a motion stage in both horizontal and vertical directions. Using the BITS method, the room-temperature structure of lysozyme was successfully determined at a resolution of 2.1 Å, and thus BITS could be utilized in future SFX experiments. [ABSTRACT FROM AUTHOR]

Protein crystals grown in microfluidic droplets have been shown to be an effective and robust platform for storage, transport and serial crystallography data collection with a minimal impact on diffraction quality. Single macromolecular microcrystals grown in nanolitre‐sized droplets allow the very efficient use of protein samples and can produce large quantities of high‐quality samples for data collection. However, there are challenges not only in growing crystals in microfluidic droplets, but also in delivering the droplets into X‐ray beams, including the physical arrangement, beamline and timing constraints and ease of use. Here, the crystallization of two human gut microbial hydrolases in microfluidic droplets is described: a sample‐transport and data‐collection approach that is inexpensive, is convenient, requires small amounts of protein and is forgiving. It is shown that crystals can be grown in 50–500 pl droplets when the crystallization conditions are compatible with the droplet environment. Local and remote data‐collection methods are described and it is shown that crystals grown in microfluidics droplets and housed as an emulsion in an Eppendorf tube can be shipped from the US to the UK using a FedEx envelope, and data can be collected successfully. Details of how crystals were delivered to the X‐ray beam by depositing an emulsion of droplets onto a silicon fixed‐target serial device are provided. After three months of storage at 4°C, the crystals endured and diffracted well, showing only a slight decrease in diffracting power, demonstrating a suitable way to grow crystals, and to store and collect the droplets with crystals for data collection. This sample‐delivery and data‐collection strategy allows crystal droplets to be shipped and set aside until beamtime is available. [ABSTRACT FROM AUTHOR]

Cyclic-olefin-copolymer (COC)-based microfluidic devices are increasingly becoming the center of highly valuable research for in situ X-ray measurements due to their compatibility with X-rays, biological compounds, chemical resistance, optical properties, low cost, and simplified handling. COC microfluidic devices present potential solutions to challenging biological applications such as protein binding, folding, nucleation, growth kinetics, and structural changes. In recent years, the techniques applied to manufacturing and handling these devices have capitalized on enormous progress toward small-scale sample probing. Here, we describe the new and innovative design aspects, fabrication, and experimental implementation of low-cost and micron-sized X-ray-compatible microfluidic sample environments that address diffusion-based crystal formation for crystallographic characterization. The devices appear fully compatible with crystal growth and subsequent X-ray diffraction experiments, resulting in remarkably low background data recording. The results highlighted in this research demonstrate how the engineered microfluidic devices allow the recording of accurate crystallographic data at room temperature and structure determination at high resolution. [ABSTRACT FROM AUTHOR]

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection‐intensity correlation coefficients to infer 'distances' among sets of reflections. This increases the sensitivity to differences in unit‐cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit‐cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit‐cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data‐clustering software by using a multi‐stage partitioning strategy. Here, one possible approach to multi‐stage data clustering is demonstrated. The strategy is to use unit‐cell clustering until the merged data are sufficiently complete and then to use intensity‐based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences. [ABSTRACT FROM AUTHOR]