A simple and versatile microfluidic device for efficient biomacromolecule crystallization and structural analysis by serial crystallography.

Determining optimal conditions for the production of well diffracting crystals is a key step in every biocrystallography project. Here, a microfluidic device is described that enables the production of crystals by counter-diffusion and their direct on-chip analysis by serial crystallography at room temperature. Nine 'non-model' and diverse biomacromolecules, including seven soluble proteins, a membrane protein and an RNA duplex, were crystallized and treated on-chip with a variety of standard techniques including micro-seeding, crystal soaking with ligands and crystal detection by fluorescence. Furthermore, the crystal structures of four proteins and an RNA were determined based on serial data collected on four synchrotron beamlines, demonstrating the general applicability of this multipurpose chip concept.

Combining crystallogenesis methods to produce diffraction-quality crystals of a psychrophilic tRNA-maturation enzyme.

The determination of conditions for the reproducible growth of well diffracting crystals is a critical step in every biocrystallographic study. On the occasion of a new structural biology project, several advanced crystallogenesis approaches were tested in order to increase the success rate of crystallization. These methods included screening by microseed matrix screening, optimization by counter-diffusion and crystal detection by trace fluorescent labeling, and are easily accessible to any laboratory. Their combination proved to be particularly efficient in the case of the target, a 48 kDa CCA-adding enzyme from the psychrophilic bacterium Planococcus halocryophilus. A workflow summarizes the overall strategy, which led to the production of crystals that diffracted to better than 2 Šresolution and may be of general interest for a variety of applications.

Feature analysis for classification of trace fluorescent labeled protein crystallization images.

BACKGROUND: Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated. RESULTS: Features are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu's, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu's thresholding, region features using green percentile G90 thresholding, region features using green percentile G99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier. CONTRIBUTIONS: The feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis.

A low-cost method for visible fluorescence imaging.

A wide variety of crystallization solutions are screened to establish conditions that promote the growth of a diffraction-quality crystal. Screening these conditions requires the assessment of many crystallization plates for the presence of crystals. Automated systems for screening and imaging are very expensive. A simple approach to imaging trace fluorescently labeled protein crystals in crystallization plates has been devised, and can be implemented at a cost as low as $50. The proteins ß-lactoglobulin B, trypsin and purified concanavalin A (ConA) were trace fluorescently labeled using three different fluorescent probes: Cascade Yellow (CY), Carboxyrhodamine 6G (CR) and Pacific Blue (PB). A crystallization screening plate was set up using ß-lactoglobulin B labeled with CR, trypsin labeled with CY, ConA labeled with each probe, and a mixture consisting of 50% PB-labeled ConA and 50% CR-labeled ConA. The wells of these plates were imaged using a commercially available macro-imaging lens attachment for smart devices that have a camera. Several types of macro lens attachments were tested with smartphones and tablets. Images with the highest quality were obtained with an iPhone 6S and an AUKEY Ora 10× macro lens. Depending upon the fluorescent probe employed and its Stokes shift, a light-emitting diode or a laser diode was used for excitation. An emission filter was used for the imaging of protein crystals labeled with CR and crystals with two-color fluorescence. This approach can also be used with microscopy systems commonly used to observe crystallization plates.