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Metals are essential components for the structure and function of many proteins. However, accurate modelling of their coordination environments remains a challenge due to the complexity and diversity of metal-coordination geometries. To address this, a method is presented for extracting and analysing coordination information, including bond lengths and angles, from the Crystallography Open Database. By using these data, comprehensive descriptions of metal-containing components are generated. A stereochemical information generator for a particular component within a specific macromolecule leverages an example PDB/mmCIF file containing the component to account for the actual surrounding environment. A matching process has been developed and implemented to align the derived metal structures with idealized coordinates from a coordination geometry library. Additionally, various strategies, depending on the quality of the matches, were employed to compile distance and angle statistics for the refinement of macromolecular structures. The developed methods were implemented in a new program, MetalCoord, that classifies and utilizes the metal-coordination geometry. The effectiveness of the developed algorithms was tested using metal-containing components from the PDB. As a result, metal-containing components from the CCP4 monomer library have been updated. The updated monomer dictionaries, in concert with the derived restraints, can be used in most structural biology computations, including macromolecular crystallography, single-particle cryo-EM and even molecular mechanics., (open access.)

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets., (open access.)

Advances in structural biology have relied heavily on synchrotron cryo-crystallography and cryogenic electron microscopy to elucidate biological processes and for drug discovery. However, disparities between cryogenic and room-temperature (RT) crystal structures pose challenges. Here, Cryo2RT, a high-throughput RT data-collection method from cryo-cooled crystals that leverages the cryo-crystallography workflow, is introduced. Tested on endothiapepsin crystals with four soaked fragments, thaumatin and SARS-CoV-2 3CL pro , Cryo2RT reveals unique ligand-binding poses, offers a comparable throughput to cryo-crystallography and eases the exploration of structural dynamics at various temperatures., (open access.)

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macromolecular crystallography experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three-dimensional reciprocal-space map that is further analyzed to determine correlated motion. To make diffuse scattering techniques more accessible, software for data processing called mdx2 has been created that is both convenient to use and simple to extend and modify. mdx2 is written in Python, and it interfaces with DIALS to implement self-contained data-reduction workflows. Data are stored in NeXus format for software interchange and convenient visualization. mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, mdx2 version 1.0 is described, a new release incorporating state-of-the-art techniques for data reduction. The implementation of a complete multi-crystal scaling and merging workflow is described, and the methods are tested using a high-redundancy data set from cubic insulin. It is shown that redundancy can be leveraged during scaling to correct systematic errors and obtain accurate and reproducible measurements of weak diffuse signals., (open access.)

Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated., (open access.)

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership., (open access.)

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement., (open access.)

Sequence-register shifts remain one of the most elusive errors in experimental macromolecular models. They may affect model interpretation and propagate to newly built models from older structures. In a recent publication, it was shown that register shifts in cryo-EM models of proteins can be detected using a systematic reassignment of short model fragments to the target sequence. Here, it is shown that the same approach can be used to detect register shifts in crystal structure models using standard, model-bias-corrected electron-density maps (2mF o - DF c ). Five register-shift errors in models deposited in the PDB detected using this method are described in detail., (open access.)

The evolution of the Collaborative Computational Project No. 4 (CCP4) has been described in a new article by Agirre et al. [(2023). Acta Cryst. D79, 449-461] that should provide the definitive reference for the CCP4 suite of programs., (open access.)

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world., (open access.)

Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which remains a primary bottleneck in crystal-based structure determination. The National High-Throughput Crystallization Center at Hauptman-Woodward Medical Research Institute has focused on overcoming obstacles to crystallization through a combination of robotics-enabled high-throughput screening and advanced imaging to increase the success of finding crystallization conditions. This paper will describe the lessons learned from over 20 years of operation of our high-throughput crystallization services. The current experimental pipelines, instrumentation, imaging capabilities and software for image viewing and crystal scoring are detailed. New developments in the field and opportunities for further improvements in biomolecular crystallization are reflected on., (open access.)

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities., (open access.)

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4., (open access.)

Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software., (open access.)

Microcrystal electron diffraction (MicroED) has recently emerged as a promising method for macromolecular structure determination in structural biology. Since the first protein structure was determined in 2013, the method has been evolving rapidly. Several protein structures have been determined and various studies indicate that MicroED is capable of (i) revealing atomic structures with charges, (ii) solving new protein structures by molecular replacement, (iii) visualizing ligand-binding interactions and (iv) determining membrane-protein structures from microcrystals embedded in lipidic mesophases. However, further development and optimization is required to make MicroED experiments more accurate and more accessible to the structural biology community. Here, we provide an overview of the current status of the field, and highlight the ongoing development, to provide an indication of where the field may be going in the coming years. We anticipate that MicroED will become a robust method for macromolecular structure determination, complementing existing methods in structural biology., (open access.)

Synchrotron serial crystallography (SSX) is an emerging data-collection method for micro-crystallography on synchrotron macromolecular (MX) crystallography beamlines. At SPring-8, the feasibility of the fixed-target approach was examined by collecting data using a 2D raster scan combined with goniometer rotation. Results at cryogenic temperatures demonstrated that rotation is effective for efficient data collection in SSX and the method was named serial synchrotron rotation crystallography (SS-ROX). To use this method for room-temperature (RT) data collection, a humid air and glue-coating (HAG) method was developed in which data were collected from polyvinyl alcohol-coated microcrystals fixed on a loop under humidity-controlled air. The performance and the RT data-collection strategy for micro-crystallography were evaluated using microcrystals of lysozyme. Although a change in unit-cell dimensions of up to 1% was observed during data collection, the impact on data quality was marginal. A comparison of data obtained at various absorbed doses revealed that absorbed doses of up to 210 kGy were tolerable in both global and local damage. Although this limits the number of photons deposited on each crystal, increasing the number of merged images improved the resolution. On the basis of these results, an equation was proposed that relates the achievable resolution to the total photon flux used to obtain a data set., (open access.)

The Canadian Macromolecular Crystallography Facility (CMCF) consists of two beamlines dedicated to macromolecular crystallography: CMCF-ID and CMCF-BM. After the first experiments were conducted in 2006, the facility has seen a sharp increase in usage and has produced a significant amount of data for the Canadian crystallographic community. Upgrades aimed at increasing throughput and flux to support the next generation of more demanding experiments are currently under way or have recently been completed. At CMCF-BM, this includes an enhanced monochromator, automounter software upgrades and a much faster detector. CMCF-ID will receive a major upgrade including a new undulator, a new monochromator and new optics to stably focus the beam onto a smaller sample size, as well as a brand-new detector., (open access.)

The performance of automated protein model building usually decreases with resolution, mainly owing to the lower information content of the experimental data. This calls for a more elaborate use of the available structural information about macromolecules. Here, a new method is presented that uses structural homologues to improve the quality of protein models automatically constructed using ARP/wARP. The method uses local structural similarity between deposited models and the model being built, and results in longer main-chain fragments that in turn can be more reliably docked to the protein sequence. The application of the homology-based model extension method to the example of a CFA synthase at 2.7 Å resolution resulted in a more complete model with almost all of the residues correctly built and docked to the sequence. The method was also evaluated on 1493 molecular-replacement solutions at a resolution of 4.0 Å and better that were submitted to the ARP/wARP web service for model building. A significant improvement in the completeness and sequence coverage of the built models has been observed., (open access.)

Reducing the sample-exchange time is a crucial issue in maximizing the throughput of macromolecular crystallography (MX) beamlines because the diffraction data collection itself is completed within a minute in the era of pixel-array detectors. To this end, an upgraded sample changer, SPACE-II, has been developed on the basis of the previous model, SPACE (SPring-8 Precise Automatic Cryo-sample Exchanger), at the BL41XU beamline at SPring-8. SPACE-II achieves one sample-exchange step within 16 s, of which its action accounts for only 11 s, because of three features: (i) the implementation of twin arms that enable samples to be exchanged in one cycle of mount-arm action, (ii) the implementation of long-stroke mount arms that allow samples to be exchanged without withdrawal of the detector and (iii) the use of a fast-moving translation and rotation stage for the mount arms. By pre-holding the next sample prior to the sample-exchange sequence, the time was further decreased to 11 s in the case of automatic data collection, of which the action of SPACE-II accounted for 8 s. Moreover, the sample capacity was expanded from four to eight Uni-Pucks. The performance of SPACE-II has been demonstrated in over two years of operation at BL41XU; the average number of samples mounted on the diffractometer in one day was increased from 132 to 185, with an error rate of 0.089%, which counted incidents in which users could not continue with an experiment without recovery work by entering the experimental hutch. On the basis of these results, SPACE-II has been installed at three other MX beamlines at SPring-8 as of July 2019. The fast and highly reliable SPACE-II is now one of the most important pieces of infrastructure for the MX beamlines at SPring-8, providing users with the opportunity to fully make use of limited beamtime with brilliant X-rays., (open access.)

Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method. Phenix is a comprehensive software package for macromolecular structure determination that handles data from any of these techniques. Tasks performed with Phenix include data-quality assessment, map improvement, model building, the validation/rebuilding/refinement cycle and deposition. Each tool caters to the type of experimental data. The design of Phenix emphasizes the automation of procedures, where possible, to minimize repetitive and time-consuming manual tasks, while default parameters are chosen to encourage best practice. A graphical user interface provides access to many command-line features of Phenix and streamlines the transition between programs, project tracking and re-running of previous tasks., (open access.)

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP., (open access.)

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

In macromolecular crystallography, mesh (raster) scans are carried out either as part of X-ray-based crystal-centring routines or to identify positions on the sample holder from which diffraction images can be collected. Here, the methods used in MeshBest, software which automatically analyses diffraction images collected during a mesh scan and produces a two-dimensional crystal map showing estimates of the dimensions, centre positions and diffraction qualities of each crystal contained in the mesh area, are presented. Sample regions producing diffraction images resulting from the superposition of more than one crystal are also distinguished from regions with single-crystal diffraction. The applicability of the method is demonstrated using several cases.

Macromolecular crystallography (MX) has been a motor for biology for over half a century and this continues apace. A series of revolutions, including the production of recombinant proteins and cryo-crystallography, have meant that MX has repeatedly reinvented itself to dramatically increase its reach. Over the last 30 years synchrotron radiation has nucleated a succession of advances, ranging from detectors to optics and automation. These advances, in turn, open up opportunities. For instance, a further order of magnitude could perhaps be gained in signal to noise for general synchrotron experiments. In addition, X-ray free-electron lasers offer to capture fragments of reciprocal space without radiation damage, and open up the subpicosecond regime of protein dynamics and activity. But electrons have recently stolen the limelight: so is X-ray crystallography in rude health, or will imaging methods, especially single-particle electron microscopy, render it obsolete for the most interesting biology, whilst electron diffraction enables structure determination from even the smallest crystals? We will lay out some information to help you decide.

An addendum to the Introduction of Cole et al. [(2017), Acta Cryst. D73, 234-239] is made to recognize the work of Bricogne, Smart and others in the development of methods to make use of Cambridge Structural Database data in protein structure solution.

In this paper, AUSPEX, a new software tool for experimental X-ray data analysis, is presented. Exploring the behaviour of diffraction intensities and the associated estimated uncertainties facilitates the discovery of underlying problems and can help users to improve their data acquisition and processing in order to obtain better structural models. The program enables users to inspect the distribution of observed intensities (or amplitudes) against resolution as well as the associated estimated uncertainties (sigmas). It is demonstrated how AUSPEX can be used to visually and automatically detect ice-ring artefacts in integrated X-ray diffraction data. Such artefacts can hamper structure determination, but may be difficult to identify from the raw diffraction images produced by modern pixel detectors. The analysis suggests that a significant portion of the data sets deposited in the PDB contain ice-ring artefacts. Furthermore, it is demonstrated how other problems in experimental X-ray data caused, for example, by scaling and data-conversion procedures can be detected by AUSPEX.

The development of serial crystallography has been driven by the sample requirements imposed by X-ray free-electron lasers. Serial techniques are now being exploited at synchrotrons. Using a fixed-target approach to high-throughput serial sampling, it is demonstrated that high-quality data can be collected from myoglobin crystals, allowing room-temperature, low-dose structure determination. The combination of fixed-target arrays and a fast, accurate translation system allows high-throughput serial data collection at high hit rates and with low sample consumption.

The Cambridge Structural Database (CSD) is the worldwide resource for the dissemination of all published three-dimensional structures of small-molecule organic and metal-organic compounds. This paper briefly describes how this collection of crystal structures can be used en masse in the context of macromolecular crystallography. Examples highlight how the CSD and associated software aid protein-ligand complex validation, and show how the CSD could be further used in the generation of geometrical restraints for protein structure refinement.

A short introduction is provided to the concept of restraints in macromolecular crystallographic refinement. A typical ligand restraint-generation process is then described, covering types of input, the methodology and the mechanics behind the software in general terms, how this has evolved over recent years and what to look for in the output. Finally, the currently available restraint-generation software is compared, concluding with some thoughts for the future.

The development of single-photon-counting detectors, such as the PILATUS, has been a major recent breakthrough in macromolecular crystallography, enabling noise-free detection and novel data-acquisition modes. The new EIGER detector features a pixel size of 75 × 75 µm, frame rates of up to 3000 Hz and a dead time as low as 3.8 µs. An EIGER 1M and EIGER 16M were tested on Swiss Light Source beamlines X10SA and X06SA for their application in macromolecular crystallography. The combination of fast frame rates and a very short dead time allows high-quality data acquisition in a shorter time. The ultrafine ϕ-slicing data-collection method is introduced and validated and its application in finding the optimal rotation angle, a suitable rotation speed and a sufficient X-ray dose are presented. An improvement of the data quality up to slicing at one tenth of the mosaicity has been observed, which is much finer than expected based on previous findings. The influence of key data-collection parameters on data quality is discussed.

During, or even after, data collection the presence and effects of radiation damage in macromolecular crystallography may not always be immediately obvious. Despite this, radiation damage is almost always present, with site-specific damage occurring on very short time (dose) scales well before global damage becomes apparent. A result of both site-specific radiation damage and derivatization is a change in the relative intensity of reflections. The size and approximate rate of onset of X-ray-induced transformations is compared with the changes expected from derivatization, and strategies for minimizing radiation damage are discussed.

A Monte Carlo-type approach for low- and medium-resolution phasing of single-particle diffraction data is suggested. Firstly, the single-particle phase problem is substituted with the phase problem for an imaginary crystal. A unit cell of this crystal contains a single isolated particle surrounded by a large volume of bulk solvent. The developed phasing procedure then generates a large number of connected and finite molecular masks, calculates their Fourier coefficients, selects the sets with magnitudes that are highly correlated with the experimental values and finally aligns the selected phase sets and calculates the averaged phase values. A test with the known structure of monomeric photosystem II resulted in phases that have 97% correlation with the exact phases in the full 25 Å resolution shell (1054 structure factors) and correlations of 99, 94, 81 and 79% for the resolution shells ∞-60, 60-40, 40-30 and 30-25 Å, respectively. The same procedure may be used for crystallographic ab initio phasing.