Acta Crystallographica Section D Structural Biology

Density modification is a standard step to provide a route for routine structure solution by any experimental phasing method -with SAD and MAD being the most popular ones- as well as to extend fragments or incomplete models into a full solution. The effect of density modification on the starting maps from either source is illustrated in the case of SHELXE. The different modes in which the program can run are reviewed; these include less well-known uses such as reading external phase values and weights or phase distributions encoded in Hendrickson-Lattman coefficients. Typically in SHELXE, initial phases are calculated from experimental data, from a partial model or map, or from a combination of both sources. The initial phase set is improved and extended by density modification and, if the resolution of the data and the type of structure permits, poly-alanine tracing. The trace now includes an extension into the gamma position or hydrophobic and aromatic side chains if a sequence is provided, which is performed in every tracing cycle. Once a correlation coefficient over 30% between the structure factors calculated from such a trace and the native data indicates that the structure has been solved, in all model building cycles sequence is docked and side chains are fitted if the map supports it. The extensions to the tracing algorithm brought in to provide a complete model are discussed. The improvement in phasing performance is assessed using a set of tests. SynopsisSide chain tracing now completes model building in SHELXE to enhance density modification. All alternative SHELXE modes, using single or combined sources of starting phase information, are described. O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD

Many double-stranded DNA (dsDNA) viruses undergo a capsid maturation process during assembly of infectious virus particles, which involves transformation of a metastable capsid precursor called procapsid into a stable, DNA-filled capsid usually with a larger size and a more angular shape. Sf6 is a tailed dsDNA bacteriophage that infects Shigella flexneri. The phage Sf6 capsid protein gp5 was heterologously expressed and purified. Electron microscopy showed that the gp5 spontaneously assembled into spherical, procapsid-like particles. We also observed tube-like and cone-shaped particles reminiscent of human immunodeficiency virus. The gp5 procapsid-like particles were crystallized and crystals diffracted beyond 4.3 [A] resolution. X-ray data at 5.9 [A] resolution were collected with a completeness of 31.1% and an overall Rmerge of 15.0%. The crystals belong to the space group C2 with unit cell dimensions of a=973.326 [A], b=568.234 [A], c=565.567 [A], and {beta}=120.540{degrees}. Self-rotation function showed the 532 symmetry, confirming formation of icosahedral particles. The particle was situated at the origin of the crystal unit cell with the icosahedral 2-fold axis coinciding with the crystallographic b axis, and there is a half of the icosahedral particle in the crystallographic asymmetric unit.

Analysis of crystallographic diffraction data before phasing gives the crystallographer a first look at the nature of the problem and the context in which the structure determination will be performed. We here report the development of Xtricorder, an application that targets analysis of crystallographic data specifically for likelihood-based phasing. As well as porting many of the analyses previously available but relatively inaccessible in our Phaser codebase, Xtricorder offers a likelihood-enhanced self-rotation function. A novel and intuitive graphical representation of the self-rotation function presents the results for user inspection, and has the added advantage that, in an adapted form, is appropriate for training a convolutional neural network to enhance the standard Matthews analysis and more accurately predict the number of copies in the asymmetric unit. We investigate the usefulness of the likelihood-enhanced self-rotation function in first look analyses, exploring the circumstances under which the self-rotation function results are useful, and discuss the application to AI-generated structure prediction. Synopsis Xtricorder is a new tool for analysing crystallographic data prior to phasing, featuring a likelihood-enhanced self-rotation function and graphical output that aids both user interpretation and machine learning-based prediction of asymmetric unit content.

In more and more drug discovery projects, crystallographic fragment screening (CFS) is employed as an early screening method. Here, we demonstrate that choosing the right crystal form has a profound influence on the hit rates and hence success and speed of downstream lead generation. Two CFS campaigns with the same fragment library and an almost identical experimental setup were carried out against the two crystal forms of the SARS-CoV-2 main protease.While both crystal forms exhibit similar diffraction properties, the observed hit rates in the two campaigns were vastly different. For the monoclinic crystals a hit rate of 3% was determined, while a hit rate of 16% was observed for the orthorhombic crystals. These findings align with the more open molecular packing in the orthorhombic crystals where the solvent channels leading to the active sites are about twice larger than in the monoclinic crystal form. Our results highlight the critical importance of the crystal system in a crystallographic fragment-screening campaign and identify this parameter as one of the most important ones to be optimized during preparation of a campaign.

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

One of the most common resistance mechanisms against antibiotics employed by Gram-negative bacteria involves the production of {beta}-lactamases, resulting in rapid hydrolysis of the antibiotic. Extensive use of the early generation cephalosporins led to the rise of extended-spectrum {beta}-lactamases (ESBLs) like CTX-Ms. Cefdinir is an extended-spectrum third-generation cephalosporin administered since the late 90s; despite this, there is no reported 3D-structure of the antibiotic bound to any {beta}-lactamase or Penicillin-Binding-Protein (PBP) in the PDB. Here we report the X-ray crystallographic structure of Cefdinir-bound CTX-M-14 E166A mutant obtained via serial cryo-crystallography (cryo-SSX). SynopsisSerial cryo-crystallography reveals the structure of the extended spectrum {beta}-lactamase CTX-M-14, in complex with the third-generation cephalosporin antibiotic Cefdinir.

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

The determination of three dimensional structures of macromolecules is one of the actual challenge in biology with the ultimate objective of understanding their function. So far, X-ray crystallography is the most popular method to solve structure, but this technique relies on the generation of diffracting crystals. Once a correct data set has been obtained, the calculation of electron density maps requires to solve the so-called <<phase problem >> using different approaches. The most frequently used technique is molecular replacement, which relies on the availability of the structure of a protein sharing strong structural similarity with the studied protein. Its success rate is directly correlated with the quality of the models used for the molecular replacement trials. The availability of models as accurate as possible is then definitely critical. Very recently, a breakthrough step has been made in the field of protein structure prediction thanks to the use of machine learning approaches as implemented in the AlphaFold or RoseTTAFold structure prediction programs. Here, we describe how these recent improvements helped us to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway (NMD), an mRNA quality control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons.

Obtaining atomic structures of large protein complexes from medium-resolution cryogenic electron-microscopy (cryo-EM) density maps is a critical bottleneck in the cryo-EM workflow. CryoJAM aims to automate this process by using a 3D Convolutional Neural Network model within a U-Net architecture. This model is trained on a novel loss function that leverages Fourier-Shell Correlation (FSC), as a proxy for quality of fit, and Root Mean Squared Error (RMSE) to help optimize fits within real space. Capitalizing on the gold-standard status of FSC in cryo-EM, this method introduces an innovative implementation of FSC into cryo-EM model fitting software, enhancing the precision and efficiency of structural analysis. After 25 epochs, CryoJAM successfully reduced the RMSE in 21 out of 26 of the test cases, effectively fitting homologous protein structures into medium-resolution cryo-EM densities.

Microcrystal electron diffraction (MicroED) has the potential to considerably impact the field of structural biology. Indeed, the method can solve atomic structures of a wide range of molecules, beyond the reach of single particle cryo-electron microscopy, exploiting crystals too small for X-ray diffraction (XRD) even using X-ray free-electron lasers. However, until the first unknown protein structure - a R2-like ligand-binding oxidase from Sulfolobus acidocaldarius (SaR2lox) - was recently solved at 3.0 [A] resolution, MicroED had only been used to study known protein structures previously obtained by XRD. Here, after adapting sample preparation protocols, the structure of the SaR2lox protein originally solved by MicroED was redetermined by XRD at 2.1 [A] resolution. In light of the higher resolution XRD data and taking into account experimental differences of the methods, the quality of the MicroED structure is examined. The analysis demonstrates that MicroED provided an overall accurate model, revealing biologically relevant information specific to SaR2lox, such as the absence of an ether cross-link, but did not allow to detect the presence of a ligand visible by XRD in the protein binding pocket. Furthermore, strengths and weaknesses of MicroED compared to XRD are discussed in the perspective of this real-life protein example. The study provides fundaments to help MicroED become a method of choice for solving novel protein structures. SynopsisThe first unknown protein structure solved by microcrystal electron diffraction (MicroED) was recently published. The redetermination by X-ray diffraction of this protein structure provides new insights into the strengths and weaknesses of the promising MicroED method.

Calculation of density maps from atomic models is essential for structural studies using crystallography and electron cryo-microscopy (cryoEM). These maps serve various purposes, including atomic model building, refinement, visualization, and validation. However, accurately comparing model-calculated maps to experimental data poses challenges, particularly because the resolution of cryoEM experimental maps varies across the map. Traditional crystallography methods generate finite-resolution maps with uniform resolution throughout the unit cell volume, while most modern software in cryoEM employ Gaussian-like functions to generate these maps, which does not adequately account for atomic model parameters and resolution. Recent work by Urzhumtsev & Lunin (2022, IUCr Journal, 9, 728-734) introduces a novel method for computing atomic model maps that incorporate local resolution and can be expressed as analytically differentiable functions of all atomic parameters. This approach enhances the accuracy of matching atomic models to experimental maps. In this paper, we detail the implementation of this method in CCTBX and Phenix. SynopsisNew tools implemented in CCTBX and Phenix allow the calculation of variable-resolution maps through a sum of atomic images expressed as analytic functions of all atomic parameters, along with their associated local resolution.

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

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

Cryo-EM has made extraordinary headway towards becoming a semi-automated, high-throughput structure determination technique. In the general workflow, high-to-medium population states are grouped into two- and three-dimensional classes, from which structures can be obtained with near-atomic resolution and subsequently analyzed to interpret function. However, low population states, which are also functionally important, are often discarded. Here, we describe a technique whereby low population states can be efficiently identified with minimal human effort via a deep convolutional neural network classifier. We use this deep learning classifier to describe a transient, low population state of bacteriophage A511 in the midst of infecting its bacterial host. This method can be used to further automate data collection and identify other functionally important low population states.

In structural biology, most figures of macromolecules are aimed at those well-versed in structure, requiring prior familiarity with scales and commonly used color schemes. Yet, as structural biology becomes democratized with the increasing pace of structure determination, the accessibility of structural data is paramount. Here, we identify three keys, and have written accompanying software plugins, for structural biologists to create figures truer to the hard-won data and clearer across different modes of color vision and to non-expert readers. O_LIUse perceptually uniform colormaps C_LIO_LIConsider readers with different modes of color vision C_LIO_LIBe explicit about scales and color usage C_LI

KAMO and Blend provide particularly effective tools to manage automatically the merging of large numbers of datasets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options such as use of more accurate cell distance metrics and use of reflection-intensity correlation coefficients to infer "distances" among sets of reflec- tions. This increases the sensitivity to differences in unit cell parameters and allows for clustering to assemble nearly complete datasets on the basis of intensity or ampli- tude differences. If datasets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step we use either the simple cell vector distance of the original Blend, or we use the more sensi- tive NCDist. This step tends to find clusters of sufficient size so 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 the reflections having a common hkl to merge clusters in a way sensitive to structural differences that may not have perturbed the 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 cate- gories 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 from one another, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy is to use unit-cell clustering until merged data is sufficiently complete then to use intensity-based clustering. We have demonstrated that, using this strategy, we are able to accurately cluster datasets from crystals that have subtle differences.

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

Capturing meta-stable conformations of enzymes and ligand complexes demands structural snapshots beyond static crystal structures. While time-resolved serial crystallography at room temperature, offers a time-resolution down to the femto-second domain it requires large amounts of micro crystals, specialized beamlines and considerable experience. Moreover, as the majority of enzymes displays turnover-times in the millisecond domain or slower, simpler methods can provide meaningful structural insight into enzyme catalysis. Vitrification of protein crystals can trap reaction intermediates by rapid cooling to {inverted exclamation} 100 K, and has traditionally been used to gain insight into long lived reaction intermediates such as product complexes. However, manual vitrification procedures are limited to long delay times of at least several seconds and heavily suffer from operator variability. A solution to this problem is provided by automatic crystal plunging devices, such as the Spitrobot, that plunge loop-mounted protein crystals into liquid nitrogen within millisecond time-scales. Versatile means of reaction initiation can be achieved either by micro dispensing a ligand droplet, or via optical excitation of light-sensitive proteins, or via the photoactivation of caged compounds. In addition to the conceptual simplicity, another benefit of cryo-trapping is that data can be collected at conventional synchrotron beamlines, exploiting their robust high-throughput capabilities. Thus, compared to room-temperature time-resolved crystallography, users not only benefit from uncoupling sample-preparation and data-collection, but also from a reduction in the required technical expertise and ready access to radiation sources. However, as cryo-trapping crystallography explores dynamic structural changes that become only visible by the comparison of several samples, experiments have to be carefully planned to carry out the necessary controls and to avoid mis- or over-interpretation of the results. Here we describe a detailed protocol for cryo-trapping time-resolved crystallography using automated crystal-plungers that enables researchers to map enzymatic reaction coordinate pathways within the millisecond domain.

We have examined how the refined B-factor changes as a function of Z (the atomic number of a scatterer) at the sulfur site of the [4Fe:4S] cluster of the nitrogenase iron protein by refinement. A simple model is developed that quantitatively captures the observed relationship between Z and B, based on a Gaussian electron density distribution with a constant electron density at the position of the scatterer. From this analysis, the fractional changes in B and Z are found to be similar. The utility of B-factor refinement to potentially distinguish atom types reflects the Z dependence of X-ray atomic scattering factors; the weaker dependence of electron atomic scattering factors on Z implies that distinctions between refined values of B in an electron scattering structure will be less sensitive to the atomic identity of a scatterer than for the case with X-ray-diffraction. This behavior provides an example of the complementary information that can be extracted from different types of scattering studies.

Mating type (MAT) loci are the most important and significant regulators of sexual reproduction and development in ascomycetous fungi. Usually, they encode two transcription factors (TFs), named MAT1-1-1 or MAT1-2-1. Mating-type strains carry only one of the two TF genes, which control expression of pheromone and pheromone receptor genes, involved in the cell-cell recognition process. The present work presents the crystallization for the alpha1 (1) domain of MAT1-1-1 from the human pathogenic fungus Aspergillus fumigatus (AfMAT1-1-1). Crystals were obtained for the complex between a polypeptide containing the 1 domain and DNA carrying the AfMAT1-1-1 recognition sequence. A streak seeding technique was applied to improve native crystal quality, resulting in diffraction data to 3.2 [A] resolution. Further, highly redundant data sets were collected from the crystals of selenomethionine-substituted AfMAT1-1-1 with a maximum resolution of 3.2 [A]. This is the first report of structural studies on the 1 domain MAT regulator involved in the mating of ascomycetes. SynopsisAn optimized purification and crystallization protocol together with initial X-ray datasets are described for this mating type transcription factor from human pathogenic fungus Aspergillus fumigatus.