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1. Instrumentation and methods for efficient time-resolved X-ray crystallography of biomolecular systems with sub-10 ms time resolution

   https://doi.org/10.1107/S205225252500288X  

   Indergaard, John A; Mahmood, Kashfia; Gabriel, Leo; Zhong, Gary; Lastovka, Adam; McLeod, Matthew J; Thorne, Robert E (May 2025, IUCrJ)

   None (Ed.)

   Time-resolved X-ray crystallography has great promise to illuminate structure–function relations and key steps of enzymatic reactions with atomic resolution. The dominant methods for chemically-initiated reactions require complex instrumentation at the X-ray beamline, significant effort to operate and maintain this instrumentation, and enormous numbers (∼10⁵–10⁹) of crystals per time point. We describe instrumentation and methods that enable high-throughput time-resolved study of biomolecular systems using standard crystallography sample supports and mail-in X-ray data collection at standard high-throughput cryocrystallography synchrotron beamlines. The instrumentation allows rapid reaction initiation by mixing of crystals and substrate/ligand solution, rapid capture of structural states via thermal quenching with no pre-cooling perturbations, and yields time resolutions in the single-millisecond range, comparable to the best achieved by any non-photo-initiated method in both crystallography and cryo-electron microscopy. Our approach to reaction initiation has the advantages of simplicity, robustness, low cost, adaptability to diverse ligand solutions and small minimum volume requirements, making it well suited to routine laboratory use and to high-throughput screening. We report the detailed characterization of instrument performance, present structures of binding ofN-acetylglucosamine to lysozyme at time points from 8 ms to 2 s determined using only one crystal per time point, and discuss additional improvements that will push time resolution toward 1 ms. 

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2. Polo : an open-source graphical user interface for crystallization screening

   https://doi.org/10.1107/S1600576721000108  

   Holleman, Ethan T.; Duguid, Erica; Keefe, Lisa J.; Bowman, Sarah E. (April 2021, Journal of Applied Crystallography)

   null (Ed.)

   Polo is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. Polo aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. Polo provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize Polo 's basic functionality. Polo is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0. 

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3. Entropic colloidal crystallization pathways via fluid–fluid transitions and multidimensional prenucleation motifs

   https://doi.org/10.1073/pnas.1905929116  

   Lee, Sangmin; Teich, Erin G.; Engel, Michael; Glotzer, Sharon C. (July 2019, Proceedings of the National Academy of Sciences)

   Complex crystallization pathways are common in protein crystallization, tetrahedrally coordinated systems, and biomineralization, where single or multiple precursors temporarily appear before the formation of the crystal. The emergence of precursors is often explained by a unique property of the system, such as short-range attraction, directional bonding, or ion association. But, structural characteristics of the prenucleation phases found in multistep crystallization remain unclear, and models are needed for testing and expanding the understanding of fluid-to-solid ordering pathways. Here, we report 3 instances of 2-step crystallization of hard-particle fluids. Crystallization in these systems proceeds via a high-density precursor fluid phase with prenucleation motifs in the form of clusters, fibers and layers, and networks, respectively. The density and diffusivity change across the fluid–fluid phase transition increases with motif dimension. We observe crystal nucleation to be catalyzed by the interface between the 2 fluid phases. The crystals that form are complex, including, notably, a crystal with 432 particles in the cubic unit cell. Our results establish the existence of complex crystallization pathways in entropic systems and reveal prenucleation motifs of various dimensions. 

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4. 3D diffractive imaging of nanoparticle ensembles using an x-ray laser

   https://doi.org/10.1364/OPTICA.410851  

   Ayyer, Kartik; Xavier, P_Lourdu; Bielecki, Johan; Shen, Zhou; Daurer, Benedikt_J; Samanta, Amit_K; Awel, Salah; Bean, Richard; Barty, Anton; Bergemann, Martin; et al (December 2020, Optica)

   Single particle imaging at x-ray free electron lasers (XFELs) has the potential to determine the structure and dynamics of single biomolecules at room temperature. Two major hurdles have prevented this potential from being reached, namely, the collection of sufficient high-quality diffraction patterns and robust computational purification to overcome structural heterogeneity. We report the breaking of both of these barriers using gold nanoparticle test samples, recording around 10 million diffraction patterns at the European XFEL and structurally and orientationally sorting the patterns to obtain better than 3-nm-resolution 3D reconstructions for each of four samples. With these new developments, integrating advancements in x-ray sources, fast-framing detectors, efficient sample delivery, and data analysis algorithms, we illuminate the path towards sub-nanometer biomolecular imaging. The methods developed here can also be extended to characterize ensembles that are inherently diverse to obtain their full structural landscape. 

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5. Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

   https://doi.org/10.1038/s41467-020-16892-4  

   Freitas, Rodrigo; Reed, Evan J. (June 2020, Nature Communications)

   Abstract The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence. 

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