Virginia Apostolopoulou

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 display 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 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 (RT‐TRX), 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.

Advances in structural biology increasingly focus on uncovering protein dynamics and transient macromolecular complexes. Such studies require modeling of low-occupancy species like time-evolving intermediates and bound ligands. In protein crystallography, difference maps that compare paired perturbed and reference datasets are a powerful way to identify and aid modeling of low-occupancy species. Current methods to generate difference maps, however, rely on manually tuned parameters and, when signals are weak due to low occupancy, can fail to extract clear, chemically interpretable signals. We address these issues, first by showing that negentropy – a measure of how different a signal looks from anticipated Gaussian noise – is an effective metric to assess difference map quality and can therefore be used to automatically determine difference map calculation parameters. Leveraging this, we apply total variation denoising, an image restoration technique that requires a choice of regularization parameter, to crystallographic difference maps. We show that total variation denoising improves map signal-to-noise and enables us to estimate the latent phase contribution of low-occupancy states. We implement this technology in an open-source Python package, METEOR. METEOR opens new possibilities, for time-resolved and ligand-screening crystallography especially, allowing detection of low-occupancy states that could not previously be resolved.

Advances in structural biology increasingly focus on uncovering protein dynamics and transient or weak macromolecular complexes. Such studies require modeling of low-occupancy species, for instance time-evolving intermediates and bound ligands. In protein crystallography, difference maps that compare paired perturbed and reference datasets are a powerful way to identify and aid modeling of low-occupancy species. Current methods to generate difference maps, however, rely on manually tuned parameters and, in cases of weak signal due to low occupancy, can fail to extract clear, chemically interpretable signals. We address these issues, first by showing negentropy is an effective metric to assess difference map quality and can therefore be used to automatically determine parameters needed during difference map calculation. Leveraging this, we apply total variation denoising, an image restoration technique that requires a choice of regularization parameter, to crystallographic difference maps. We show that total variation denoising improves map signal-to-noise and enables us to estimate the latent phase contribution of low-occupancy states. This technology opens new possibilities for time-resolved and ligand-screening crystallography in particular, allowing the detection of states that previously could not be resolved due to their inherently low occupancy.

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.