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The global COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has wreaked unprecedented havoc on global society, in terms of a huge loss of life and burden of morbidity, economic upheaval and social disruption. Yet the sheer magnitude and uniqueness of this event has also spawned a massive mobilization of effort in the scientific community to investigate the virus, to develop therapeutics and vaccines, and to understand the public health impacts. Structural biology has been at the center of these efforts, and so it is advantageous to take an opportunity to reflect on the status of structural science vis-à-vis its role in the fight against COVID-19, to register the unprecedented response and to contemplate the role of structural biology in addressing future outbreak threats. As the one-year anniversary of the World Health Organization declaration that COVID-19 is a pandemic has just passed, over 1000 structures of SARS-CoV-2 biomolecules have been deposited in the Worldwide Protein Data Bank (PDB). It is rare to obtain a snapshot of such intense effort in the structural biology arena and is of special interest as the 50th anniversary of the PDB is celebrated in 2021. It is additionally timely as it overlaps with a period that has been termed the `resolution revolution' in cryoelectron microscopy (CryoEM). CryoEM has recently become capable of producing biomolecular structures at similar resolutions to those traditionally associated with macromolecular X-ray crystallography. Examining SARS-CoV-2 protein structures that have been deposited in the PDB since the virus was first identified allows a unique window into the power of structural biology and a snapshot of the advantages of the different techniques available, as well as insight into the complementarity of the structural methods.

Transforming growth factor β-1 (TGFβ-1) is a secreted signalling protein that directs many cellular processes and is an attractive target for the treatment of several diseases. The primary endogenous activity regulatory mechanism for TGFβ-1 is sequestration by its pro-peptide, latency-associated peptide (LAP), which sterically prohibits receptor binding by caging TGFβ-1. As such, recombinant LAP is promising as a protein-based therapeutic for modulating TGFβ-1 activity; however, the mechanism of binding is incompletely understood. Comparison of the crystal structure of unbound LAP (solved here to 3.5 Å resolution) with that of the bound complex shows that LAP is in a more open and extended conformation when unbound to TGFβ-1. Analysis suggests a mechanism of binding TGFβ-1 through a large-scale conformational change that includes contraction of the inter-monomer interface and caging by the `straight-jacket' domain that may occur in partnership through a loop-to-helix transition in the core jelly-roll fold. This conformational change does not appear to include a repositioning of the integrin-binding motif as previously proposed. X-ray scattering-based modelling supports this mechanism and reveals possible orientations and ensembles in solution. Although native LAP is heavily glycosylated, solution scattering experiments show that the overall folding and flexibility of unbound LAP are not influenced by glycan modification. The combination of crystallography, solution scattering and biochemical experiments reported here provide insight into the mechanism of LAP sequestration of TGFβ-1 that is of fundamental importance for therapeutic development.

The characterization of biomacromolecule structural vibrations has been impeded by a broad continuous vibrational density of states obscuring molecule specific vibrations. A terahertz microscopy system using polarization control produces signatures to dynamically fingerprint proteins and a RNA G-quadruplex.

Identifying and then optimizing initial crystallization conditions is a prerequisite for macromolecular structure determination by crystallography. Improved technologies enable data collection on crystals that are difficult if not impossible to detect using visible imaging. The application of second-order nonlinear imaging of chiral crystals and ultraviolet two-photon excited fluorescence detection is shown to be applicable in a high-throughput manner to rapidly verify the presence of nanocrystals in crystallization screening conditions. It is noted that the nanocrystals are rarely seen without also producing microcrystals from other chemical conditions. A crystal volume optimization method is described and associated with a phase diagram for crystallization.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

A significant problem in biological X-ray crystallography is the radiation chemistry caused by the incident X-ray beam. This produces both global and site-specific damage. Site specific damage can misdirect the biological interpretation of the structural models produced. Cryo-cooling crystals has been successful in mitigating damage but not eliminating it altogether; however, cryo-cooling can be difficult in some cases and has also been shown to limit functionally relevant protein conformations. The doses used for X-ray crystallography are typically in the kilo-gray to mega-gray range. While disulfide bonds are among the most significantly affected species in proteins in the crystalline state at both cryogenic and higher temperatures, there is limited information on their response to low X-ray doses in solution, the details of which might inform biomedical applications of X-rays. In this work we engineered a protein that dimerizes through a susceptible disulfide bond to relate the radiation damage processes seen in cryo-cooled crystals to those closer to physiologic conditions. This approach enables a low-resolution technique, small angle X-ray scattering (SAXS), to detect and monitor a residue specific process. A dose dependent fragmentation of the engineered protein was seen that can be explained by a dimer to monomer transition through disulfide bond cleavage. This supports the crystallographically derived mechanism and demonstrates that results obtained crystallographically can be usefully extrapolated to physiologic conditions. Fragmentation was influenced by pH and the conformation of the dimer, providing information on mechanism and pointing to future routes for investigation and potential mitigation. The novel engineered protein approach to generate a large-scale change through a site-specific interaction represents a promising tool for advancing radiation damage studies under solution conditions.

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields.

The heterotrimeric protein complex containing the integrin linked kinase (ILK), parvin, and PINCH proteins, termed the IPP complex, is an essential component of focal adhesions, where it interacts with many proteins to mediate signaling from integrin adhesion receptors. Here we conduct a biochemical and structural analysis of the minimal IPP complex, comprising full-length human ILK, the LIM1 domain of PINCH1, and the CH2 domain of α-parvin. We provide a detailed purification protocol for IPP and show that the purified IPP complex is stable and monodisperse in solution. Using small-angle X-ray scattering (SAXS), we also conduct the first structural characterization of IPP, which reveals an elongated shape with dimensions 120×60×40 Å. Flexibility analysis using the ensemble optimization method (EOM) is consistent with an IPP complex structure with limited flexibility, raising the possibility that inter-domain interactions exist. However, our studies suggest that the inter-domain linker in ILK is accessible and we detect no inter-domain contacts by gel filtration analysis. This study provides a structural foundation to understand the conformational restraints that govern the IPP complex.