Data reduction in protein serial crystallography.

Serial crystallography (SX) has become an established technique for protein structure determination, especially when dealing with small or radiation-sensitive crystals and investigating fast or irreversible protein dynamics. The advent of newly developed multi-megapixel X-ray area detectors, capable of capturing over 1000 images per second, has brought about substantial benefits. However, this advancement also entails a notable increase in the volume of collected data. Today, up to 2 PB of data per experiment could be easily obtained under efficient operating conditions. The combined costs associated with storing data from multiple experiments provide a compelling incentive to develop strategies that effectively reduce the amount of data stored on disk while maintaining the quality of scientific outcomes. Lossless data-compression methods are designed to preserve the information content of the data but often struggle to achieve a high compression ratio when applied to experimental data that contain noise. Conversely, lossy compression methods offer the potential to greatly reduce the data volume. Nonetheless, it is vital to thoroughly assess the impact of data quality and scientific outcomes when employing lossy compression, as it inherently involves discarding information. The evaluation of lossy compression effects on data requires proper data quality metrics. In our research, we assess various approaches for both lossless and lossy compression techniques applied to SX data, and equally importantly, we describe metrics suitable for evaluating SX data quality.

Time-resolved crystallography captures light-driven DNA repair.

Photolyase is an enzyme that uses light to catalyze DNA repair. To capture the reaction intermediates involved in the enzyme's catalytic cycle, we conducted a time-resolved crystallography experiment. We found that photolyase traps the excited state of the active cofactor, flavin adenine dinucleotide (FAD), in a highly bent geometry. This excited state performs electron transfer to damaged DNA, inducing repair. We show that the repair reaction, which involves the lysis of two covalent bonds, occurs through a single-bond intermediate. The transformation of the substrate into product crowds the active site and disrupts hydrogen bonds with the enzyme, resulting in stepwise product release, with the 3' thymine ejected first, followed by the 5' base.

Structure of the Lysinibacillus sphaericus Tpp49Aa1 pesticidal protein elucidated from natural crystals using MHz-SFX.

The Lysinibacillus sphaericus proteins Tpp49Aa1 and Cry48Aa1 can together act as a toxin toward the mosquito Culex quinquefasciatus and have potential use in biocontrol. Given that proteins with sequence homology to the individual proteins can have activity alone against other insect species, the structure of Tpp49Aa1 was solved in order to understand this protein more fully and inform the design of improved biopesticides. Tpp49Aa1 is naturally expressed as a crystalline inclusion within the host bacterium, and MHz serial femtosecond crystallography using the novel nanofocus option at an X-ray free electron laser allowed rapid and high-quality data collection to determine the structure of Tpp49Aa1 at 1.62 Å resolution. This revealed the packing of Tpp49Aa1 within these natural nanocrystals as a homodimer with a large intermolecular interface. Complementary experiments conducted at varied pH also enabled investigation of the early structural events leading up to the dissolution of natural Tpp49Aa1 crystals-a crucial step in its mechanism of action. To better understand the cooperation between the two proteins, assays were performed on a range of different mosquito cell lines using both individual proteins and mixtures of the two. Finally, bioassays demonstrated Tpp49Aa1/Cry48Aa1 susceptibility of Anopheles stephensi, Aedes albopictus, and Culex tarsalis larvae-substantially increasing the potential use of this binary toxin in mosquito control.

JINXED: just in time crystallization for easy structure determination of biological macromolecules.

Macromolecular crystallography is a well established method in the field of structural biology and has led to the majority of known protein structures to date. After focusing on static structures, the method is now under development towards the investigation of protein dynamics through time-resolved methods. These experiments often require multiple handling steps of the sensitive protein crystals, e.g. for ligand-soaking and cryo-protection. These handling steps can cause significant crystal damage, and hence reduce data quality. Furthermore, in time-resolved experiments based on serial crystallography, which use micrometre-sized crystals for short diffusion times of ligands, certain crystal morphologies with small solvent channels can prevent sufficient ligand diffusion. Described here is a method that combines protein crystallization and data collection in a novel one-step process. Corresponding experiments were successfully performed as a proof-of-principle using hen egg-white lysozyme and crystallization times of only a few seconds. This method, called JINXED (Just IN time Crystallization for Easy structure Determination), promises high-quality data due to the avoidance of crystal handling and has the potential to enable time-resolved experiments with crystals containing small solvent channels by adding potential ligands to the crystallization buffer, simulating traditional co-crystallization approaches.

Automatic bad-pixel mask maker for X-ray pixel detectors with application to serial crystallography.

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

Antiviral activity of natural phenolic compounds in complex at an allosteric site of SARS-CoV-2 papain-like protease.

SARS-CoV-2 papain-like protease (PLpro) covers multiple functions. Beside the cysteine-protease activity, facilitating cleavage of the viral polypeptide chain, PLpro has the additional and vital function of removing ubiquitin and ISG15 (Interferon-stimulated gene 15) from host-cell proteins to support coronaviruses in evading the host's innate immune responses. We identified three phenolic compounds bound to PLpro, preventing essential molecular interactions to ISG15 by screening a natural compound library. The compounds identified by X-ray screening and complexed to PLpro demonstrate clear inhibition of PLpro in a deISGylation activity assay. Two compounds exhibit distinct antiviral activity in Vero cell line assays and one inhibited a cytopathic effect in non-cytotoxic concentration ranges. In the context of increasing PLpro mutations in the evolving new variants of SARS-CoV-2, the natural compounds we identified may also reinstate the antiviral immune response processes of the host that are down-regulated in COVID-19 infections.

Data reduction for serial crystallography using a robust peak finder.

A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of 'robust statistics' has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (i.e. the background noise) and another group comprising outliers (i.e. Bragg peaks). Our robust statistics algorithm has two key advantages, which are demonstrated through testing using multiple SX data sets. First, it is relatively insensitive to the exact value of the input parameters and hence requires minimal optimization. This is critical for the algorithm to be able to run unsupervised, allowing for automated selection or 'vetoing' of SX diffraction data. Secondly, the processing of individual diffraction patterns can be easily parallelized. This means that it can analyse data from multiple detector modules simultaneously, making it ideally suited to real-time data processing. These characteristics mean that the robust peak finder (RPF) algorithm will be particularly beneficial for the new class of MHz X-ray free-electron laser sources, which generate large amounts of data in a short period of time.

X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease.

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput x-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (Mpro), which is essential for viral replication. In contrast to commonly applied x-ray fragment screening experiments with molecules of low complexity, our screen tested already-approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to Mpro In subsequent cell-based viral reduction assays, one peptidomimetic and six nonpeptidic compounds showed antiviral activity at nontoxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2.