Macromolecular Crystallography Research Articles

Identifying and avoiding radiation damage in macromolecular crystallography.

Radiation damage remains one of the major impediments to accurate structure solution in macromolecular crystallography. The artefacts of radiation damage can manifest as structural changes that result in incorrect biological interpretations being drawn from a model, they can reduce the resolution to which data can be collected and they can even prevent structure solution entirely. Inthis article, we discuss how to identify and mitigate against the effects of radiation damage at each stage in the macromolecular crystal structure-solution pipeline.

Neutron Macromolecular Crystallography for Biological Samples—Current State and Future Perspectives

Knowledge of hydrogen locations and protonation states is critical for a fundamental understanding of biological macromolecular function/interactions, and neutron macromolecular crystallography (NMX) is uniquely suited among the experimental structural-determination methods to provide this information. However, despite its potential, NMX remains a relatively niche technique, due to substantial limitations. This review explores NMX’s role amongst the evolving landscape of structural biology, comparing and contrasting it to the historical gold standard of X-ray macromolecular crystallography (X-ray MX) and the increasingly prevalent electron-based methods—i.e., electron microscopy (EM) and electron diffraction (ED). Forthcoming developments (e.g., the European Spallation Source in Lund, Sweden, coming online) are expected to substantially address current limitations and ensure NMX will remain relevant in the coming decades.

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

Ray-tracing analytical absorption correction for X-ray crystallography based on tomographic reconstructions

Processing of single-crystal X-ray diffraction data from area detectors can be separated into two steps. First, raw intensities are obtained by integration of the diffraction images, and then data correction and reduction are performed to determine structure-factor amplitudes and their uncertainties. The second step considers the diffraction geometry, sample illumination, decay, absorption and other effects. While absorption is only a minor effect in standard macromolecular crystallography (MX), it can become the largest source of uncertainty for experiments performed at long wavelengths. Current software packages for MX typically employ empirical models to correct for the effects of absorption, with the corrections determined through the procedure of minimizing the differences in intensities between symmetry-equivalent reflections; these models are well suited to capturing smoothly varying experimental effects. However, for very long wavelengths, empirical methods become an unreliable approach to model strong absorption effects with high fidelity. This problem is particularly acute when data multiplicity is low. This paper presents an analytical absorption correction strategy (implemented in new software AnACor) based on a volumetric model of the sample derived from X-ray tomography. Individual path lengths through the different sample materials for all reflections are determined by a ray-tracing method. Several approaches for absorption corrections (spherical harmonics correction, analytical absorption correction and a combination of the two) are compared for two samples, the membrane protein OmpK36 GD, measured at a wavelength of λ = 3.54 Å, and chlorite dismutase, measured at λ = 4.13 Å. Data set statistics, the peak heights in the anomalous difference Fourier maps and the success of experimental phasing are used to compare the results from the different absorption correction approaches. The strategies using the new analytical absorption correction are shown to be superior to the standard spherical harmonics corrections. While the improvements are modest in the 3.54 Å data, the analytical absorption correction outperforms spherical harmonics in the longer-wavelength data (λ = 4.13 Å), which is also reflected in the reduced amount of data being required for successful experimental phasing.

Scaling and merging macromolecular diffuse scattering with mdx2.

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 todetermine 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.

DSAS: a new macromolecular substructure solution program based on the modified phase-retrieval algorithm

Abstract Considering the pivotal role of single-wavelength anomalous diffraction (SAD) in macromolecular crystallography, our objective was to introduce DSAS, a novel program designed for efficient anomalous scattering substructure determination. DSAS stands out with its core components: a modified phase-retrieval algorithm and automated parameter tuning. The software boasts an intuitive graphical user interface (GUI), facilitating seamless input of essential data and real-time monitoring. Extensive testing on DSAS has involved diverse datasets, encompassing proteins, nucleic acids, and various anomalous scatters such as sulfur (S), selenium (Se), metals, and halogens. The results confirm DSAS's exceptional performance in accurately determining heavy atom positions, making it a highly effective tool in the field.

FLEXR GUI: a graphical user interface for multi-conformer modeling of proteins.

Proteins are well known 'shapeshifters' which change conformation to function. In crystallography, multiple conformational states are often present within the crystal and the resulting electron-density map. Yet, explicitly incorporating alternative states into models to disentangle multi-conformer ensembles is challenging. We previously reported the tool FLEXR, which, within a few minutes, automatically separates conformational signal from noise and builds the corresponding, often missing, structural features into a multi-conformer model. To make the method widely accessible for routine multi-conformer building as part of the computational toolkit for macromolecular crystallography, we present a graphical user interface (GUI) for FLEXR, designed as a plugin for Coot 1. The GUI implementation seamlessly connects FLEXR models with the existing suite of validation and modeling tools available in Coot. We envision that FLEXR will aid crystallographers by increasing access to a multi-conformer modeling method that will ultimately lead to a better representation of protein conformational heterogeneity in the Protein Data Bank. In turn, deeper insights into the protein conformational landscape may inform biology or provide new opportunities for ligand design. The code is open source and freely available on GitHub at https://github.com/TheFischerLab/FLEXR-GUI.

Comparative Analysis of Room Temperature Structures Determined by Macromolecular and Serial Crystallography

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.

AlphaFold-assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography.

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

The time revolution in macromolecular crystallography.

Macromolecular crystallography has historically provided the atomic structures of proteins fundamental to cellular functions. However, the advent of cryo-electron microscopy for structure determination of large and increasingly smaller and flexible proteins signaled a paradigm shift in structural biology. The extensive structural and sequence data from crystallography and advanced sequencing techniques have been pivotal for training computational models for accurate structure prediction, unveiling the general fold of most proteins. Here, we present a perspective on the rise of time-resolved crystallography as the new frontier of macromolecular structure determination. We trace the evolution from the pioneering time-resolved crystallography methods to modern serial crystallography, highlighting the synergy between rapid detection technologies and state-of-the-art x-ray sources. These innovations are redefining our exploration of protein dynamics, with high-resolution crystallography uniquely positioned to elucidate rapid dynamic processes at ambient temperatures, thus deepening our understanding of protein functionality. We propose that the integration of dynamic structural data with machine learning advancements will unlock predictive capabilities for protein kinetics, revolutionizing dynamics like macromolecular crystallography revolutionized structural biology.

Advanced exploitation of unmerged reflection data during processing and refinement with autoPROC and BUSTER.

The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.

Crystallographic Data Collection Using a Multilayer Monochromator on an Undulator Beamline at the Shanghai Synchrotron Radiation Facility

To resolve photons hungry for weak diffraction samples by the crystallographic method, a double-multilayer monochromator (DMM) was employed on an undulator beamline (BL17UM) at the Shanghai Synchrotron Radiation Facility (SSRF) to provide a focused sub-micron beam with high brightness for macromolecular crystallography experiments. High-quality crystallographic datasets from model protein crystal samples were collected and processed by an existing crystallographic program for structure solution and refinement. The data quality was compared with datasets from a normal silicon crystal monochromator to evaluate the bandwidth of the DMM effect on these crystallographic data. This experiment demonstrates that multilayer optics on an undulator beamline may play a valuable role in satisfying the demands of structure-related research, which requires novel methods.

Accounting for nonuniformity of bulk-solvent: A mosaic model.

A flat mask-based model is almost universally used in macromolecular crystallography to account for disordered (bulk) solvent. This model assumes any voxel of the crystal unit cell that is not occupied by the atomic model is occupied by the solvent. The properties of this solvent are assumed to be exactly the same across the whole volume of the unit cell. While this is a reasonable approximation in practice, there are a number of scenarios where this model becomes suboptimal. In this work, we enumerate several of these scenarios and describe a new generalized approach to modeling the bulk-solvent which we refer to as mosaic bulk-solvent model. The mosaic bulk-solvent model allows nonuniform features of the solvent in the crystal to be accounted for in a computationally efficient way. It is implemented in the computational crystallography toolbox and the Phenix software.

From femtoseconds to minutes: time-resolved macromolecular crystallography at XFELs and synchrotrons.

Over the last decade, the development of time-resolved serial crystallography (TR-SX) at X-ray free-electron lasers (XFELs) and synchrotrons has allowed researchers to study phenomena occurring in proteins on the femtosecond-to-minute timescale, taking advantage of many technical and methodological breakthroughs. Protein crystals of various sizes are presented to the X-ray beam in either a static or a moving medium. Photoactive proteins were naturally the initial systems to be studied in TR-SX experiments using pump-probe schemes, where the pump is a pulse of visible light. Other reaction initiations through small-molecule diffusion are gaining momentum. Here, selected examples of XFEL and synchrotron time-resolved crystallography studies will be used to highlight the specificities of the various instruments and methods with respect to time resolution, and are compared with cryo-trapping studies.

Finback: a web-based data collection system at SSRF biological macromolecular crystallography beamlines.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with auser-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

CPMU Development at Diamond Light Source

Over the last three years (2020-2022) Diamond Light Source has installed four in-house designed, built, and measured Cryogenic Permanent Magnet Undulators (CPMUs). All four are 2 m long with a 17.6 mm period and have a minimum operating gap of 4 mm. These have replaced existing 2 m long in-vacuum Pure Permanent Magnet (PPM) devices to improve the flux to several of Diamond’s MX (Macromolecular Crystallography) beamlines by a factor of 2-4. In this paper we present the mechanical and cryogenic design considerations, and the shimming procedures and tools developed to produce these devices. The performance of the CPMUs compared to their PPM counterparts will also be reviewed.

Deep residual networks for crystallography trained on synthetic data.

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.

Raw diffraction data and reproducibility.

In recent years, there has been a major expansion in digital storage capability for hosting raw diffraction datasets. Naturally, the question has now arisen as to the benefits and costs for the preservation of such raw, i.e., experimental diffraction datasets. We describe the consultations made of the global structural chemistry, i.e., chemical crystallography community from the points of view of the International Union of Crystallography (IUCr) Committee on Data, of which JRH was the Chair until very recently, and the IUCrData Raw Data Letters initiative, for which LKB is the Main Editor. The monitoring by the CCDC of CSD depositions which cite the digital object identifiers of raw diffraction datasets provides interesting statistics by probe (x-ray, neutron, or electron) and by home lab vs central facility. Clearly, a better understanding of the reproducibility of current analysis procedures is at hand. Policies for publication requiring raw data have been updated in IUCr Journals for macromolecular crystallography, namely, that raw data should be made available for a new crystal structure or a new method as well as the wwPDB deposition. For chemical crystallography, such a step requiring raw data archiving has not yet been recommended by the IUCr Commission on Structural Chemistry.

The TR-icOS setup at the ESRF: time-resolved microsecond UV-Vis absorption spectroscopy on protein crystals.

The technique of time-resolved macromolecular crystallography (TR-MX) has recently been rejuvenated at synchrotrons, resulting in the design of dedicated beamlines. Using pump-probe schemes, this should make the mechanistic study of photoactive proteins and other suitable systems possible with time resolutions down to microseconds. In order to identify relevant time delays, time-resolved spectroscopic experiments directly performed on protein crystals are often desirable. To this end, an instrument has been built at the icOS Lab (in crystallo Optical Spectroscopy Laboratory) at the European Synchrotron Radiation Facility using reflective focusing objectives with a tuneable nanosecond laser as a pump and a microsecond xenon flash lamp as a probe, called the TR-icOS (time-resolved icOS) setup. Using this instrument, pump-probe spectra can rapidly be recorded from single crystals with time delays ranging from a few microseconds to seconds and beyond. This can be repeated at various laser pulse energies to track the potential presence of artefacts arising from two-photon absorption, which amounts to a power titration of a photoreaction. This approach has been applied to monitor the rise and decay of the M state in the photocycle of crystallized bacteriorhodopsin and showed that the photocycle is increasingly altered with laser pulses of peak fluence greater than 100 mJ cm-2, providing experimental laser and delay parameters for a successful TR-MX experiment.

Assembly Requirements for the Construction of Large-Scale Binary Protein Structures.

The precise assembly of multiple biomacromolecules into well-defined structures and materials is of great importance for various biomedical and nanobiotechnological applications. In this study, we investigate the assembly requirements for two-component materials using charged protein nanocages as building blocks. To achieve this, we designed several variants of ferritin nanocages to determine the surface characteristics necessary for the formation of large-scale binary three-dimensional (3D) assemblies. These nanocage variants were employed in protein crystallization experiments and macromolecular crystallography analyses, complemented by computational methods. Through the screening of nanocage variant combinations at various ionic strengths, we identified three essential features for successful assembly: (1) the presence of a favored crystal contact region, (2) the presence of a charged patch not involved in crystal contacts, and (3) sufficient distinctiveness between the nanocages. Surprisingly, the absence of noncrystal contact mediating patches had a detrimental effect on the assemblies, highlighting their unexpected importance. Intriguingly, we observed the formation of not only binary structures but also both negatively and positively charged unitary structures under previously exclusively binary conditions. Overall, our findings will inform future design strategies by providing some design rules, showcasing the utility of supercharging symmetric building blocks in facilitating the assembly of biomacromolecules into large-scale binary 3D assemblies.