The video details a protocol for analyzing a thawed sample using centrifugation, microscopy, and imaging techniques over six weeks. It focuses on identifying crystal growth and precipitation through automated plate imaging, utilizing brightfield, sonic, and UV-TPEF imaging to characterize the samples' properties and monitor changes.

protein sample setup and crystallization

Hochdurchsatz-Screening für die Proteinkristallisation

Even in an age of tremendous progress in structural biology methods, X-ray crystallography continues to be a dependable and popular method for generating high-quality structural models of macromolecules. Over 85% of all three-dimensional structural models deposited to the Protein Data Bank (PDB) are from crystal-based structural methods (as of January, 2023).1 Furthermore, X-ray crystallography remains indispensable for solving protein-ligand structures, a crucial component of the drug discovery and development process2. Despite protein crystallization having remained the dominant structural biology technique for over half a century, methods to predict crystallization likelihood based on physical properties3 or sequence4,5 are still in their infancy.
The prediction of crystallization conditions is even more obscure; limited progress has been made to predict likely crystallization conditions even for model proteins6,7. Other studies have attempted to identify crystallization conditions based on protein homology and conditions mined from the PDB8,9,10. The predictive power to be found in the PDB is limited, however, as only the final, successful crystallization conditions are deposited, which, by necessity, misses the often extensive optimization experiments required to fine-tune crystal growth. Further, many PDB entries lack metadata containing these details, including the cocktail formulas, crystallization format, temperature, and time to crystallize11,12. Therefore, for many proteins of interest, the most accessible way to determine the crystallization conditions is experimentally, using as many conditions as possible across a wide range of chemical possibilities.
Several approaches to make crystallization screening as fruitful and thorough as possible have been explored to great effect, including sparse matrices13, incomplete factorial screening14, additives15,16, seeding17, and nucleating agents18. The National HTX Center at Hauptman-Woodward Medical Research Institute (HWI) has developed an efficient pipeline for crystallization screening using the microbatch-under-oil approach19, which utilizes automated liquid handling and imaging modalities to streamline the identification of initial crystallization conditions using comparatively minimal sample and cocktail volumes (Figure 1). The set of 1,536 unique cocktails are based on conditions previously determined to be conducive to protein crystal growth and are designed to be chemically diverse in order to sample a large range of possible crystallization conditions20,21,22. The broad sampling of crystallization conditions increases the likelihood of observing one or more crystallization leads.
Few formal analyses of how many conditions are needed for screening have appeared in the literature. One study focused on the sampling layout of different screens and found that the random sampling of components (similar to an incomplete factorial) represented the most thorough and efficient sampling method23. Another study of screening noted that there have been numerous instances when the very thorough 1,536 screen has yielded only a single crystal hit24, and a very recent study highlighted that most commercial screens undersample the crystallization space known to be associated with screening hits25. Not all crystallization leads will yield a diffraction quality crystal suitable for data collection due to inherent disorder within the crystal, diffraction limitations, or crystal flaws; therefore, casting a wider net for conditions has the additional benefit of providing alternative crystal forms for optimization.
The format of protein crystallization experiments also has an impact on the success of the screen. Vapor diffusion is the most commonly used setup for high-throughput crystallization applications and is utilized at state-of-the art crystallization centers, including the EMBL Hamburg and Institut Pasteur high-throughput screening centers26,27,28. The HTX Center uses the microbatch-under-oil method; while less commonly used, it is a robust method that minimizes the consumption of sample and crystallization cocktails20,21,22. One advantage of the microbatch-under-oil method, particularly when using a high-viscosity paraffin oil, is that only slight evaporation occurs within the drop during the experiment, meaning that the equilibrium concentration is achieved upon drop mixing. If positive crystallization results are observed in the microbatch-under-oil method, the reproduction of these conditions is typically more straightforward than in vapor diffusion setups, in which crystallization occurs at some undefined point during the equilibration between the crystallization drop and the reservoir. The reproducibility of hits is desirable for high-throughput crystallization approaches, which produce prohibitively tiny protein crystals that typically need to be optimized for single-crystal X-ray experiments.
The high-throughput crystallization screen for soluble proteins is made up of cocktails that are prepared in-house, ready-made commercial screens, and in-house-modified commercial screens22. The cocktails were initially developed using the incomplete factorial strategy using previously successful crystallization cocktails20. The reagents in the screen that are commercially available include arrays of polymers, crystallization salts, PEG, and ion combinations and screens that utilize sparse matrix and incomplete factorial approaches. There are also reagents that are modified before inclusion in the screen: an additive screen, a pH and buffer screen, an ionic liquid additive screen, and a polymer screen.
The power of known crystallization conditions and strategies has been leveraged in the 1,536 crystallization cocktails, along with the benefits of the microbatch-under-oil system to generate a pipeline that employs automated liquid handling, automated brightfield imaging, and second order nonlinear imaging of chiral crystals (SONICC). The automation of both the liquid handling and imaging provides the benefits of fewer wet lab hours and higher reproducibility. The high-throughput nature of automated crystallization screening necessitates the automation of the process of monitoring for crystal growth. These advances are achieved with state-of-the-art imaging technologies to assist in the identification of positive crystal hits. Both standard brightfield imaging of plates, as well as multi-photon methods for enhanced detection, are used via a crystal imaging system with SONICC (Figure 2). SONICC combines second harmonic generation (SHG)29 microscopy and ultraviolet two-photon excited fluorescence (UV-TPEF)30 microscopy to detect very small crystals, as well as those obscured by precipitate. The SONICC imaging informs on whether the wells contain protein (via UV-TPEF) and crystals (via SHG). Beyond the positive identification of protein crystals, additional information can also be obtained using state-of-the-art imaging methods. Cocktail-only imaging prior to sample addition serves as a negative control; these images can identify the well appearance prior to sample addition, including in terms of salt crystals and debris. Additionally, SHG and UV-TPEF imaging help differentiate protein crystals from salt crystals and can be used for visualizing protein-nucleic acid complexed material31.
High-throughput crystallization experiments undergoing repeated monitoring via imaging result in a very large volume of images needing examination. Automated crystal scoring methods have been developed to reduce the burden on the user and increase the probability of identifying positive crystal hits. The HTX Center partcipated in the development of the MAchine Recognition of Crystallization Outcomes (MARCO) scoring algorithm, a trained deep convolutional neural network architecture developed by a consortium of academic, non-profit, government, and industry partners to classify brightfield well images32. The algorithm was trained on nearly half a million brightfield images from crystallization experiments from multiple institutions using different crystallization methods and different imagers. The algorithm outputs a probabilistic score indicating whether a given image falls into four possible image classes: &#34;crystal&#34;, &#34;clear&#34;, &#34;precipitate&#34;, and &#34;other&#34;. MARCO has a reported classification accuracy of 94.5%. Crystal detection is further enhanced with software that implements the algorithm and provides a graphical user interface (GUI) for accessible and simple image viewing, enabled with the AI-enabled scoring capabilities32,33. The MARCO Polo GUI is designed to work seamlessly with the setup of the imaging and data management system in the HTX Center to identify hits in the 1,536-well screen, with human engagement to examine the output of sorted lists. Additionally, as open-source software available on GitHub, the GUI is readily available for modification to reflect the specific needs of other laboratory groups.
Here, the process of setting up a high-throughput microbatch-under-oil experiment using robotic liquid handling to deliver both the cocktail and protein is described. The HTX Center has a unique array of instrumentation and resources that are not found at other institutions, with the goal of providing screening services and educational resources to interested users. Demonstrating the methods and capabilities of robotics-enabled high-throughput techniques will enable the community to have knowledge of available technologies and make decisions for their own structure determination efforts.

Autor im Rampenlicht: Hochdurchsatz-Screening zur Gewinnung von Kristalltreffern für die Proteinkristallographie  

Hochdurchsatz-Screening zur Gewinnung von Kristalltreffern für die Proteinkristallographie

Watch this Scientific Journal Video about Protein Sample Setup and Crystallization at JoVE.com

Protein Sample Setup and Crystallization  

Aufbau und Kristallisation von Proteinproben

Zentrifugieren Sie die unverpackte aufgetaute Probe bei 10.000 g für zwei Minuten bei Raumtemperatur. Beobachten Sie die Probe, um Niederschlag, Farbe und Zustand zu erkennen. Erwärmen Sie die vorbereitete 1.536-Well-Platte auf 23 Grad Celsius und zentrifugieren Sie sie dann fünf Minuten lang bei 150 g.Machen Sie mit einem Hellfeldmikroskop Bilder von der Platte, die den Cocktail enthält, nur um sie als Negativkontrolle zu verwenden. Geben Sie 200 Nanoliter der Probe mit einem Liquid-Handling-Roboter in jede Vertiefung. Nachdem Sie die Platte bei 150 g zentrifugiert haben, inkubieren Sie sie sechs Wochen lang bei der gewünschten Temperatur.Nehmen Sie Hellfeldbilder der Platten mit Proben in wöchentlichen Abständen von Tag 1 bis Woche 6 auf. Führen Sie Schallbildgebung mit dem SHG und UV-TPEF durch. Die automatisierte Plattenbildgebung während der gesamten Experimentdauer ermöglichte die Identifizierung von schnell und langsam wachsenden Kristallen.Die UV-TPEF-Bildgebung zeigte die proteinartige Natur an, während die SHG-Bildgebung die kristalline Natur der Probe bestätigte. Die Schallbildgebung ermöglichte die Identifizierung von Kristallen, die durch Ausfällung oder Film verdeckt sind und kein SHG-Signal erzeugen, oder von Mikrokristallen, die fälschlicherweise als Niederschlag angesehen werden können. Nicht-Proteinkristalle, denen das UV-TPEF-Signal fehlte, die aber starke Hellfeld- und SHG-Signale aufwiesen, wurden ebenfalls identifiziert.

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Protein Sample Setup and Crystallization

Unit 1

Introduction to Separation Methods

SCIENTISTS IN ACTION

553 Aufrufe. Zentrifugieren Sie die unverpackte aufgetaute Probe bei 10.000 g für zwei Minuten bei Raumtemperatur. Beobachten Sie die Probe, um Niederschlag, Farbe und Zustand zu erkennen. Erwärmen Sie die vorbereitete 1.536-Well-Platte auf 23 Grad Celsius und zentrifugieren Sie sie dann fünf Minuten lang bei 150 g.Machen Sie mit einem Hellfeldmikroskop Bilder von der Platte, die den Cocktail enthält, nur um sie als Negativkontrolle zu verwenden. Geben Sie 200 Nanoliter der Probe mit einem Liqu...

Serie: Protein Sample Setup and Crystallization  

High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

X-ray crystallography is the most commonly employed technique to discern macromolecular structures, but the crucial step of crystallizing a protein into an ordered lattice amenable to diffraction remains challenging. The crystallization of biomolecules is largely experimentally defined, and this process can be labor-intensive and prohibitive to researchers at resource-limited institutions. At the National High-Throughput Crystallization (HTX) Center, highly reproducible methods have been implemented to facilitate crystal growth, including an automated high-throughput 1,536-well microbatch-under-oil plate setup designed to sample a wide breadth of crystallization parameters. Plates are monitored using state-of-the-art imaging modalities over the course of 6 weeks to provide insight into crystal growth, as well as to accurately distinguish valuable crystal hits. Furthermore, the implementation of a trained artificial intelligence scoring algorithm for identifying crystal hits, coupled with an open-source, user-friendly interface for viewing experimental images, streamlines the process of analyzing crystal growth images. Here, the key procedures and instrumentation are described for the preparation of the cocktails and crystallization plates, imaging the plates, and identifying hits in a way that ensures reproducibility and increases the likelihood of successful crystallization.

This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.

X-ray crystallography is the most commonly employed technique to discern macromolecular structures, but the crucial step of crystallizing a protein into ...