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HUNTSVILLE, AL, United States

Meyer A.,University of Hamburg | Betzel C.,University of Hamburg | Pusey M.,Ixpressgenes, Inc.
Acta Crystallographica Section F:Structural Biology Communications

Successful protein crystallization screening experiments are dependent upon the experimenter being able to identify positive outcomes. The introduction of fluorescence techniques has brought a powerful and versatile tool to the aid of the crystal grower. Trace fluorescent labeling, in which a fluorescent probe is covalently bound to a subpopulation (<0.5%) of the protein, enables the use of visible fluorescence. Alternatively, one can avoid covalent modification and use UV fluorescence, exploiting the intrinsic fluorescent amino acids present in most proteins. By the use of these techniques, crystals that had previously been obscured in the crystallization drop can readily be identified and distinguished from amorphous precipitate or salt crystals. Additionally, lead conditions that may not have been obvious as such under white-light illumination can be identified. In all cases review of the screening plate is considerably accelerated, as the eye can quickly note objects of increased intensity. © 2015 International Union of Crystallography. Source

Judge R.A.,Abbott Laboratories | Forsythe E.L.,Nektar Therapeutics | Pusey M.L.,Ixpressgenes, Inc.
Crystal Growth and Design

Likemany smallmoleculematerials, tetragonal lysozyme crystals exhibit growth rate dispersion. To investigate this phenomenon further, the growth rate dispersion of the (110) and (101) crystal faces was determined as a function of sodium chloride concentration, temperature, and solution pH. Under the conditions investigated, the growth rate dispersion follows the constant crystal growthmodel, in which each individual crystal is assumed to have a unique, constant growth rate. While the growth rate dispersion of the (110) face seems independent of the solution conditions, for the (101) face it was observed to vary systematically with temperature and pH. The greater susceptibility of the (101) face to the causes of growth rate dispersion was interpreted in light of a model proposed to explain the differing growth mechanisms of each face. Overall, the magnitude of crystal growth rate dispersion observed for lysozyme is similar to that reported for some small organic molecules. © 2010 American Chemical Society. Source

Sigdel M.,University of Alabama in Huntsville | Pusey M.L.,Ixpressgenes, Inc. | Aygun R.S.,University of Alabama in Huntsville
Crystal Growth and Design

In this paper, we describe the design and implementation of a stand-alone real-time system for protein crystallization image acquisition and classification with a goal to assist crystallographers in scoring crystallization trials. An in-house assembled fluorescence microscopy system is built for image acquisition. The images are classified into three categories as noncrystals, likely leads, and crystals. Image classification consists of two main steps - image feature extraction and application of classification based on multilayer perceptron (MLP) neural networks. Our feature extraction involves applying multiple thresholding techniques, identifying high intensity regions (blobs), and generating intensity and blob features to obtain a 45-dimensional feature vector per image. To reduce the risk of missing crystals, we introduce a max-class ensemble classifier which applies multiple classifiers and chooses the highest score (or class). We performed our experiments on 2250 images consisting of 67% noncrystal, 18% likely leads, and 15% clear crystal images and tested our results using 10-fold cross validation. Our results demonstrate that the method is very efficient (<3 s to process and classify an image) and has comparatively high accuracy. Our system only misses 1.2% of the crystals (classified as noncrystals) most likely due to low illumination or out of focus image capture and has an overall accuracy of 88%. © 2013 American Chemical Society. Source

Current macromolecule crystallization screening methods rely on the random testing of crystallization conditions, in the hope that one or more will yield positive results, crystals. Most plate outcomes are either clear or precipitated solutions, in which the results are routinely discarded by the experimenter. However, many of these may in fact be close to crystallization conditions, a fact which is obscured by the nature of the apparent outcome. We are developing a fluorescence-based approach to the determination of crystallization conditions, an approach which can also be used to assess conditions that may be close to those that would give crystals. The method uses measurements of fluorescence anisotropy and intensity. The method was first tested using model proteins, with likely outcomes as determined by fluorescence measurements where the plate data showed either clear or precipitated solutions being subjected to optimization screening. The results showed a ∼83% increase in the number of crystallization conditions. The method was then tried as the sole screening method with a number of test proteins. In every case, at least one or more crystallization conditions were found, and it is estimated that ∼53% of these would not have been found using a plate screen.(Figure Presented) © 2011 American Chemical Society. Source

Sigdel M.,University of Alabama in Huntsville | Pusey M.L.,Ixpressgenes, Inc. | Aygun R.S.,University of Alabama in Huntsville
Crystal Growth and Design

Thousands of experiments corresponding to different combinations of conditions are set up to determine the relevant conditions for successful protein crystallization. In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization progress. Images are collected multiple times during the course of an experiment. A huge number of collected images make manual review of images tedious and discouraging. In this work, utilizing trace fluorescence labeling, we describe an automated system called CrystPro for monitoring the protein crystal growth in crystallization trial images by analyzing the time sequence images. Given the sets of image sequences, the objective is to develop an efficient and reliable system to detect crystal growth changes such as new crystal formation and increase of crystal size. CrystPro consists of three major steps-identification of crystallization trials proper for spatiotemporal analysis, spatiotemporal analysis of identified trials, and crystal growth analysis. We evaluated the performance of our system on three crystallization image data sets (PCP-ILopt-11, PCP-ILopt-12, and PCP-ILopt-13) and compared our results with expert scores. Our results indicate (a) 98.3% accuracy and 0.896 sensitivity on identification of trials for spatiotemporal analysis, (b) 77.4% accuracy and 0.986 sensitivity of identifying crystal pairs with new crystal formation, and (c) 85.8% accuracy and 0.667 sensitivity on crystal size increase detection. The results show that our method is reliable and efficient for tracking growth of crystals and determining useful image sequences for further review by the crystallographers. © 2015 American Chemical Society. Source

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