Laue-DIALS: open-source software for polychromatic X-ray diffraction data.

Most X-ray sources are inherently polychromatic. Polychromatic ("pink") X-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure-ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most X-ray facilities to discard > 99% of X-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron.

DJ-1 (PARK7) is an intensively studied protein whose cytoprotective activities are dysregulated in multiple diseases. DJ-1 has been reported as having two distinct enzymatic activities in defense against reactive carbonyl species that are difficult to distinguish in conventional biochemical experiments. Here, we establish the mechanism of DJ-1 using a synchrotron-compatible version of mix-and-inject-serial crystallography (MISC), which was previously performed only at XFELs, to directly observe DJ-1 catalysis. We designed and used new diffusive mixers to collect time-resolved Laue diffraction data of DJ-1 catalysis at a pink beam synchrotron beamline. Analysis of structurally similar methylglyoxal-derived intermediates formed through the DJ-1 catalytic cycle shows that the enzyme catalyzes nearly two turnovers in the crystal and defines key aspects of its glyoxalase mechanism. In addition, DJ-1 shows allosteric communication between a distal site at the dimer interface and the active site that changes during catalysis. Our results rule out the widely cited deglycase mechanism for DJ-1 action and provide an explanation for how DJ-1 produces L-lactate with high chiral purity.

Perturbative diffraction methods resolve a conformational switch that facilitates a two-step enzymatic mechanism.

Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to poor experimental access. This shortcoming is evident with Escherichia coli dihydrofolate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we describe ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments to map the conformational dynamics of the Michaelis complex of DHFR. We resolve coupled global and local motions and find that these motions are engaged by the protonated substrate to promote efficient catalysis. This result suggests a fundamental design principle for multistep enzymes in which pre-existing dynamics enable intermediates to drive rapid electrostatic reorganization to facilitate subsequent chemical steps.

Correcting systematic errors in diffraction data with modern scaling algorithms.

X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods.

Resolving conformational changes that mediate a two-step catalytic mechanism in a model enzyme.

Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to lack of experimental access. This shortcoming is evident with E. coli dihydrofolate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we present ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments that enable identification of coupled conformational changes in DHFR. We identify a global hinge motion and local networks of structural rearrangements that are engaged by substrate protonation to regulate solvent access and promote efficient catalysis. The resulting mechanism shows that DHFR's two-step catalytic mechanism is guided by a dynamic free energy landscape responsive to the state of the substrate.

A unifying Bayesian framework for merging X-ray diffraction data.

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

Native SAD phasing at room temperature.

Single-wavelength anomalous diffraction (SAD) is a routine method for overcoming the phase problem when solving macromolecular structures. This technique requires the accurate measurement of intensities to determine differences between Bijvoet pairs. Although SAD experiments are commonly conducted at cryogenic temperatures to mitigate the effects of radiation damage, such temperatures can alter the conformational ensemble of the protein and may impede the merging of data from multiple crystals due to non-uniform freezing. Here, a strategy is presented to obtain high-quality data from room-temperature, single-crystal experiments. To illustrate the strengths of this approach, native SAD phasing at 6.55 keV was used to solve four structures of three model systems at 295 K. The resulting data sets allow automatic phasing and model building, and reveal alternate conformations that reflect the structure of proteins at room temperature.

reciprocalspaceship: a Python library for crystallographic data analysis.

Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges - many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used pandas library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.

The dynamic conformational cycle of the group I chaperonin C-termini revealed via molecular dynamics simulation.

Chaperonins are large ring shaped oligomers that facilitate protein folding by encapsulation within a central cavity. All chaperonins possess flexible C-termini which protrude from the equatorial domain of each subunit into the central cavity. Biochemical evidence suggests that the termini play an important role in the allosteric regulation of the ATPase cycle, in substrate folding and in complex assembly and stability. Despite the tremendous wealth of structural data available for numerous orthologous chaperonins, little structural information is available regarding the residues within the C-terminus. Herein, molecular dynamics simulations are presented which localize the termini throughout the nucleotide cycle of the group I chaperonin, GroE, from Escherichia coli. The simulation results predict that the termini undergo a heretofore unappreciated conformational cycle which is coupled to the nucleotide state of the enzyme. As such, these results have profound implications for the mechanism by which GroE utilizes nucleotide and folds client proteins.

A conserved mode of protein recognition and binding in a ParD-ParE toxin-antitoxin complex.

Toxin-antitoxin (TA) systems form a ubiquitous class of prokaryotic proteins with functional roles in plasmid inheritance, environmental stress response, and cell development. ParDE family TA systems are broadly conserved on plasmids and bacterial chromosomes and have been well characterized as genetic elements that promote stable plasmid inheritance. We present a crystal structure of a chromosomally encoded ParD-ParE complex from Caulobacter crescentus at 2.6 A resolution. This TA system forms an alpha(2)beta(2) heterotetramer in the crystal and in solution. The toxin-antitoxin binding interface reveals extensive polar and hydrophobic contacts of ParD antitoxin helices with a conserved recognition and binding groove on the ParE toxin. A cross-species comparison of this complex structure with related toxin structures identified an antitoxin recognition and binding subdomain that is conserved between distantly related members of the RelE/ParE toxin superfamily despite a low level of overall primary sequence identity. We further demonstrate that ParD antitoxin is dimeric, stably folded, and largely helical when not bound to ParE toxin. Thus, the paradigmatic model in which antitoxin undergoes a disorder-to-order transition upon toxin binding does not apply to this chromosomal ParD-ParE TA system.