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

High-Throughput Screening for Protein Crystallization

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.

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography  

High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Getting a crystal to perform diffraction experiments remains a challenge because it's difficult to predict what parameters will influence crystallization. We favor the experimenter by screening 1536 crystallization conditions in a single plate and use advanced imaging technologies to identify even the smallest crystal hits. Structural biology techniques are developing rapidly and the field has been revolutionized by computational structure prediction.This has caused the field to become more integrative with several experimental or computational approaches more commonly being combined to generate a more detailed and accurate representation of biological mechanisms. Generating crystal hits is a key step to performing single crystal x-ray diffraction experiments and to developing techniques like micro ED and serial crystallography. Using advanced imaging methods, UVTBF and SHG along with the power of the Marco algorithm helps us identify useful crystals at all size scales.Our high throughput crystallization methods have generated a large amount of data for probing questions regarding efficiency of crystallization cocktail components. The specialized imaging we do reveal how non-linear optical methods can be used to detect vanishingly small crystals. Finding crystallization conditions is critical for crystal based structural methods, which account for 90%of all structural models in the protein data bank.In 2021, close to 2 million files were downloaded per day from the PDB emphasizing the impact structures have in paving the way for further scientific investigations.

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

Protein Sample Setup and Crystallization  

Centrifuge the unpacked thawed sample at 10, 000g for two minutes at room temperature. Observe the sample to identify any precipitation, color, and condition. Warm the prepared 1, 536 well plate to 23 degrees Celsius, and then centrifuge it at 150g for five minutes.Using a brightfield microscope, take pictures of the plate containing the cocktail only to be used as a negative control. Dispense 200 nanoliters of the sample to each well using a liquid handling robot. after centrifuging the plate at 150g, incubate it at the desired temperature for six weeks.Capture brightfield images of the plates with samples at weekly intervals from day 1 to week 6. Perform sonic imaging with the SHG and UV-TPEF. Automated plate imaging throughout the experiment duration allowed for the brightfield identification of rapidly and slow-growing crystals.The UV-TPEF imaging indicated the proteinaceous nature, while the SHG imaging confirmed the crystalline nature of the sample. Sonic imaging enabled the identification of crystals obscured by precipitation or film which do not produce an SHG signal, or microcrystals that may be mistaken as precipitate. Nonprotein crystals lacking UV-TPEF signal but displaying strong brightfield and SHG signals were also identified.

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567 vistas.  Hauptman-Woodward Medical Research Institute. Centrifugar la muestra descongelada sin empaquetar a 10, 000 g durante dos minutos a temperatura ambiente. Observe la muestra para identificar cualquier precipitación, color y condición. Caliente la placa preparada de 1.536 pocillos a 23 grados centígrados, y luego centrifugarla a 150 g durante cinco minutos.Usando un microscopio de campo claro, tome fotografías del plato que contiene el cóctel solo para usarlo como control negativo. Dispensar 200 nanolitros de la muestra a cada p...

Video: Protein Sample Setup and Crystallization  

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 ...

﻿Oil and Crystallization Cocktail Well Plate Preparation

Oil and Crystallization Cocktail Well Plate Preparation  

To begin, use a robotic liquid handling system for delivering five microliters of paraffin oil to the wells of a 1536 well plate. Invert the thawed cocktail containing 384 well plates to mix the solutions. Incubate the plates at 30 degrees celsius to dissolve any persistent precipitates.Dispense equal measures of the buffered PEG 3350, and the additive screen components from the 96 well plate into the 384 well plate to a final volume of 50 microliters. Stamp four 384 well plates into a 1536 well plate filling all four quadrants. Using a liquid handling robot equipped with a 384-syringe head, deliver 200 nanoliters of cocktail solution into each well of the 1536 well plate.

Image Analysis to Identify Protein Crystals

To analyze the crystallization images, first open the AI-enabled open-source graphical user interface. Next, click on Import, select Images from the dropdown menu, and then choose From Rar Archive/Directory. Click on Browse For Folder in the popup window and then navigate to the folder containing the images.Choose the desired files and import them into the interface by clicking Open. Once the selected files appear in the selected paths window, select one or more to download into the interface and then click on Import Runs. To view the image of the first well, click on the greater than symbol to the left of the sample name on the window of the slideshow viewer, then double-click on the appropriate read.Resize the whole window to enlarge the image. The Image Details box includes information about the image and scoring. The Cocktail Details box contains metadata about the cocktail components.To move to the next well, click on the Next button in the Navigation panel or press the right arrow key on the keyboard. A specific well can be navigated by entering the well number in the By Well Number window. All reads can be viewed by checking the Show All Dates box.All spectra can be viewed by checking the Show All Spectrum box. Each individual spectrum image can be viewed by clicking on the Swap Spectrum button. To score the crystal images using the MARCO algorithm, first select a specific run from the list on the left side of the window, then click on Classify Selected Run and view the MARCO scoring information in the Image Details window once all 1, 536 wells have been scored.To view a subset of the scored images, for example, the images classified as crystals by MARCO, in the Image Filtering panel, tick the crystals and MARCO boxes and then click on the Submit Filters button. To generate a manual human-scored set, assign scores to each well by clicking the appropriate button, located in the Classification panel at the bottom of the window, or use the keyboard number pad to assign a score.