Determination and application of empirically derived detergent phase boundaries to effectively crystallize membrane proteins.

Elucidating the structures of membrane proteins is essential to our understanding of disease states and a critical component in the rational design of drugs. Structural characterization of a membrane protein begins with its detergent solubilization from the lipid bilayer and its purification within a functionally stable protein-detergent complex (PDC). Crystallization of the PDC typically occurs by changing the solution environment to decrease solubility and promote interactions between exposed hydrophilic surface residues. As membrane proteins have been observed to form crystals close to the phase separation boundaries of the detergent used to form the PDC, knowledge of these boundaries under different chemical conditions provides a foundation to rationally design crystallization screens. We have carried out dye-based detergent phase partitioning studies using different combinations of 10 polyethylene glycols (PEG), 11 salts, and 11 detergents to generate a significant amount of chemically diverse phase boundary data. The resulting curves were used to guide the formulation of a 1536-cocktail crystallization screen for membrane proteins. We are making both the experimentally derived phase boundary data and the 1536 membrane screen available through the high-throughput crystallization facility located at the Hauptman-Woodward Institute. The phase boundary data have been packaged into an interactive Excel spreadsheet that allows investigators to formulate grid screens near a given phase boundary for a particular detergent. The 1536 membrane screen has been applied to 12 membrane proteins of unknown structures supplied by the structural genomics and structural biology communities, with crystallization leads for 10/12 samples and verification of one crystal using X-ray diffraction.

Establishing a training set through the visual analysis of crystallization trials. Part I: approximately 150,000 images.

Structural crystallography aims to provide a three-dimensional representation of macromolecules. Many parts of the multistep process to produce the three-dimensional structural model have been automated, especially through various structural genomics projects. A key step is the production of crystals for diffraction. The target macromolecule is combined with a large and chemically diverse set of cocktails with some leading ideally, but infrequently, to crystallization. A variety of outcomes will be observed during these screening experiments that typically require human interpretation for classification. Human interpretation is neither scalable nor objective, highlighting the need to develop an automatic computer-based image classification. As a first step towards automated image classification, 147,456 images representing crystallization experiments from 96 different macromolecular samples were manually classified. Each image was classified by three experts into seven predefined categories or their combinations. The resulting data where all three observers are in agreement provides one component of a truth set for the development and rigorous testing of automated image-classification systems and provides information about the chemical cocktails used for crystallization. In this paper, the details of this study are presented.

Efficient optimization of crystallization conditions by manipulation of drop volume ratio and temperature.

An efficient optimization method for the crystallization of biological macromolecules has been developed and tested. This builds on a successful high-throughput technique for the determination of initial crystallization conditions. The optimization method takes an initial condition identified through screening and then varies the concentration of the macromolecule, precipitant, and the growth temperature in a systematic manner. The amount of sample and number of steps is minimized and no biochemical reformulation is required. In the current application a robotic liquid handling system enables high-throughput use, but the technique can easily be adapted in a nonautomated setting. This method has been applied successfully for the rapid optimization of crystallization conditions in nine representative cases.

Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates.

A technique for automatically evaluating microbatch (400 nl) protein-crystallization trials is described. This method addresses analysis problems introduced at the sub-microlitre scale, including non-uniform lighting and irregular droplet boundaries. The droplet is segmented from the well using a loopy probabilistic graphical model with a two-layered grid topology. A vector of 23 features is extracted from the droplet image using the Radon transform for straight-edge features and a bank of correlation filters for microcrystalline features. Image classification is achieved by linear discriminant analysis of its feature vector. The results of the automatic method are compared with those of a human expert on 32 1536-well plates. Using the human-labeled images as ground truth, this method classifies images with 85% accuracy and a ROC score of 0.84. This result compares well with the experimental repeatability rate, assessed at 87%. Images falsely classified as crystal-positive variously contain speckled precipitate resembling microcrystals, skin effects or genuine crystals falsely labeled by the human expert. Many images falsely classified as crystal-negative variously contain very fine crystal features or dendrites lacking straight edges. Characterization of these misclassifications suggests directions for improving the method.

A deliberate approach to screening for initial crystallization conditions of biological macromolecules.

A method to rationally predict crystallization conditions for a previously uncrystallized macromolecule has not yet been developed. One way around this problem is to determine initial crystallization conditions by casting a wide net, surveying a large number of chemical and physical conditions to locate crystallization leads. A facility that executes the rapid survey of crystallization lead conditions is described in detail. Results and guidelines for the initial screening of crystallization conditions, applicable to both manual and robotic setups, are discussed.