HUNTSVILLE, AL, United States
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Sigdel M.,University of Alabama in Huntsville | Pusey M.L.,Ixpressgenes, Inc. | Aygun R.S.,University of Alabama in Huntsville
Crystal Growth and Design | Year: 2013

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.


Pusey M.L.,Ixpressgenes, Inc.
Crystal Growth and Design | Year: 2011

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.


Judge R.A.,Abbott Laboratories | Forsythe E.L.,Nektar Therapeutics | Pusey M.L.,Ixpressgenes, Inc.
Crystal Growth and Design | Year: 2010

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.


Meyer A.,University of Hamburg | Betzel C.,University of Hamburg | Pusey M.,Ixpressgenes, Inc.
Acta Crystallographica Section F:Structural Biology Communications | Year: 2015

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.


Pusey M.,Ixpressgenes, Inc. | Barcena J.,Ixpressgenes, Inc. | Morris M.,Ixpressgenes, Inc. | Singhal A.,Ixpressgenes, Inc. | And 2 more authors.
Acta Crystallographica Section:F Structural Biology Communications | Year: 2015

Fluorescence can be a powerful tool to aid in the crystallization of proteins. In the trace-labeling approach, the protein is covalently derivatized with a high-quantum-yield visible-wavelength fluorescent probe. The final probe concentration typically labels ≤0.20% of the protein molecules, which has been shown to not affect the crystal nucleation or diffraction quality. The labeled protein is then used in a plate-screening experiment in the usual manner. As the most densely packed state of the protein is the crystalline form, then crystals show as the brightest objects in the well under fluorescent illumination. A study has been carried out on the effects of trace fluorescent labeling on the screening results obtained compared with nonlabeled protein, and it was found that considering the stochastic nature of the crystal nucleation process the presence of the probe did not affect the outcomes obtained. Other effects are realised when using fluorescence. Crystals are clearly seen even when buried in precipitate. This approach also finds 'hidden' leads, in the form of bright spots, with ∼30% of the leads found being optimized to crystals in a single-pass optimization trial. The use of visible fluorescence also enables the selection of colors that bypass interfering substances, and the screening materials do not have to be UV-transparent. © 2015 International Union of Crystallography.


Sigdel M.,University of Alabama in Huntsville | Pusey M.L.,Ixpressgenes, Inc. | Aygun R.S.,University of Alabama in Huntsville
Crystal Growth and Design | Year: 2015

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.


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: STTR | Phase: Phase II | Award Amount: 489.77K | Year: 2016

Project Summary Crystallization followed by subsequent structure determination is a major step in understanding the structure function relationship of macromolecules Understanding macromolecule structure has become a key part in the development of new pharmaceuticals and is a major area of NIH research Crystallization however is also the rate limiting step despite technological efforts to automate the set up and crystallization data acquisition processes Macromolecule crystallization conditions are arrived at by screening experiments where the target material is typically subjected to hundreds or even thousands of different chemical cocktails In most cases screening experiments fail as they do not result in a crystal We propose that screening experiments contain useful information about the target proteins behavior in response to the tested solution conditions No screen or group of screens can systematically cover the combinatorial chemical space for protein crystallization and we hypothesize that in the absence of clear positive hits scored results can be analyzed to determine these factors The analysis method developed is called the Associated Experimental Design AED approach The analysis identified the most significant factors and a condition screen based on those factors is prepared for each protein and set up In the ongoing Phase I effort the AED software is being progressively evolved adding functions for aiding in prioritizing the screen factors employed for likely success in crystallization The software is written to not duplicate input conditions for a given protein in the output i e all output conditions are new combinations of high probability factors as determined from the analysis The software has been tested with proteins to date Of the proteins that did not give crystals upon initial screening gave crystals from screens developed on the basis of the AED analysis Of the remaining proteins gave as many or more crystals in the single AED based screen than were obtained in the x condition screens One of these proteins was the RrP RrP archaeal exosome catalytic core complex Based on the Phase I results the AED method shows considerable promise A major advantage of this approach is that it fits into existing practice making use of existing materials methods and data routinely generated in crystallization screening The AED software can be used with any imaging system that gives a scored assessment of the results for each trial including manual scoring by a user with a simple low power microscope The Phase I results also showed that it can be used with a reduced more granular scoring scale Success with this approach will increase the number of hits generated and greatly reduce the time and effort required for macromolecule crystallization The proposed Phase II effort is to build upon the successful approach developed in Phase I and further develop the analytical methods employed Successful crystallization and X ray data analysis provides important three dimensional information on the macromolecules structure function relationship important to the design of pharmaceuticals Screening experiments to identify crystallization conditions typically return non crystalline results This proposal is to further develop and test software for the analysis of screening data scores to identify likely significant crystallization factors providing an analytical basis for subsequent experiments and thereby increasing the chances of success


Grant
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase II | Award Amount: 743.51K | Year: 2013

DESCRIPTION (provided by applicant): Crystallization, followed by subsequent structure determination, is a major step in understanding the structure- function relationship of macromolecules. Understanding macromolecule structure has become a key part in the development of new pharmaceuticals, and is a major area of NIH research. Crystallization however is also the rate limiting step, despite technological efforts to automate the set-up and crystallization data acquisition processes. The Phase I effort successfully demonstrated that a low cost epifluorescence type crystallization plate imaging system could be assembled, and that lead crystallization conditions could be obtained from apparently failed outcomes by intensity analysis of the fluorescence images.The method is based upon trace covalent labeling, defined as lt 0.5% of the molecules being labeled, of the protein using a fluorescent probe that excites and emits in the visible spectrum. The major objectives of this proposal are to expand upon the Phase I results. First is the improvement of the software for the rapid scoring of the crystallization screen images, to expand that capability to include scoring for different crystallization outcomes (needle, plate, 3D crystal), and to further improve the scoring success rate. Second is the implementation of a multicolor fluorescence capability to make the instrument more suitable for the crystallization of macromolecule complexes, followed by the testing and demonstration of that capability. Third is to develop a labeling methodology suitable for use with integral membrane proteins. Fourth is to further refine the instrument and methods by continued use and testing in our laboratory. Experimentally, trace fluorescently labeled protein will be subjected to crystallization screens and the outcomes periodically imaged. Intensity-based image analysis, using the evolving software as it is developed during the proposal period, will be carried out. Precipitated conditions which show suitable scores based on the image analysis will be subjected to optimization screening, and based upon the Phase I effort results we expect a correlation between the scores obtained and subsequent crystallization. Previous research has shown that fluorescence can be a powerful aid in finding and identifying crystals in screening plates (Judge et al., 2005; Forsythe et al., 2006; Groves et al., 2007; Pusey et al., 2008). Crystallization gives the most densely packed state for a protein, and therefore trace fluorescently labeled protein will have the greatest fluorescence intensity relative to clear or precipitated outcomes. The covalently bound probe serves as a reporter to the protein's response to the solution conditions. Some precipitates showed 'bright spots' of fluorescence, and manyof these outcomes were subsequently be optimized to crystallization conditions (Pusey et al., 2008; Phase I results). Thus intensity-based scoring of precipitation outcomes may be used to discriminate between non-productive and potentially productive precipitation results. Fluorescence intensity-based crystallization screen scoring is found to be fast, with image processing times currently 3 seconds. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Successful crystallization and X-ray data analysis provides important three-dimensional information on the macromolecules structure-function relationship. Many proteins that are potential drug targets or key components in diseases are only available in trace quantities, or are difficult to obtain. Thisproposal is to increase the data returned fro the protein crystallization process, and thereby increase the chances of success, by putting a powerful but affordable screening plate imaging and analysis tool into the hands of crystallization laboratories.


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

DESCRIPTION provided by applicant Crystallization followed by subsequent structure determination is a major step in understanding the structure function relationship of macromolecules Understanding macromolecule structure has become a key part in the development of new pharmaceuticals and is a major area of NIH research Crystallization however is also the rate limiting step despite technological efforts to automate the set up and crystallization data acquisition processes Macromolecule crystallization conditions are arrived at by screening experiments where the target material is subjected to typically hundreds to even thousands of different chemical cocktails In most cases screening experiments fail as they do not result in a crystal We propose that the experiments contain useful information about the targets behavior in response to the imposed conditions and that the results can be analyzed to extract the relevant parameters for guiding subsequent crystallization trials No screen or group of screens can systematically cover the chemical space for protein crystallization and we hypothesize that in the absence of clear positive hits scored results can be analyzed to determine these factors As a test of this a preliminary screening results analysis program was written and tested using three hyperthermophile proteins one each a very easy moderate and a very difficult crystallizer Characterizations were on the basis of each proteins behavior in a single crystallization screen The analysis is called the Associated Experimental Design AED approach The analysis identified the most significant factors and a condition screen based on those factors was prepared for each protein and set up Crystals were obtained for all three proteins and none of the crystallization conditions duplicated those in the original screen Our Phase I goal is to further improve the preliminary AED software The Phase I improvements are to include up to three or four screens in the analysis function to treat salt anions and cations separately in the analysis to output a condition screen for each protein composed of the found significant factors for that protein and to test a revised scoring scheme For simplicity th same screens will be used throughout the Phase I effort A pool of test proteins will be employed each being classified as easy moderately difficult and difficult crystallizers based upon their performance in screening trials Each iteration of the software will be used with the test proteins original scoring data and optimization screens set up The optimization results will be fed back to further guide the AED software development Proteins classified as difficult are anticipated to require two or more optimization rounds Based on the preliminary results the AED method shows considerable promise A major advantage of this approach is that it will fit into existing practice making use of the data already generated in crystallization screening Success with this approach will increase the number of hits generated and greatly reduce the time and effort required for macromolecule crystallization PUBLIC HEALTH RELEVANCE Successful crystallization and X ray data analysis provides important three dimensional information on the macromolecules structure function relationship important to the design of pharmaceuticals Screening experiments to identify crystallization conditions typically return non crystalline results This proposal is to develop software for the analysis of screening data scores to identify likely significant crystallization factors providingan analytical basis for subsequent experiments and thereby increasing the chances of success


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 744.43K | Year: 2010

DESCRIPTION (provided by applicant): Current practice is to set up trial crystallization screens and periodically review the results to see if a crystal or promising crystal-like precipitate has appeared a process that often takes weeks or months. Most outcomes are precipitated protein or clear drops, and the conditions that led to those results are removed from further consideration. We are developing an alternative screening approach, the self-association behavior of the target macromolecule as measured by fluorescence anisotropy as a diagnostic for the likelihood of crystallization under the test conditions. Dilute solution properties are known to be a diagnostic for crystallization (George and Wilson, 1994; George et al., 1997; Wilson et al., 1993; Wilson et al, 1996; Tessier et al., 2002; Tessier et al., 2003; Garcia et al., 2003a; Garcia et al., 2003b; Bloustine et al., 2003). Concentration vs. anisotropy data for a macromolecule-precipitant combination was originally proposed for determining the likelihood of that solution producing crystals, although based on Phase I results intensity vs. concentration data is also a strong indicator. Data acquired in Phase I shows that this approach can find lead crystallization conditions from solutions that give clear drops or precipitate in screening assays. The applications of the instrument and methodology to be developed will be to rapidly conduct crystallization screens within 2-24 hrs, using a minimum amount of protein ( 0.03 mg), with a higher probability of finding lead conditions. Higher success rates will greatly facilitate structure-based drug design, particularly for target proteins that are difficult to obtain, and contribute to the understanding and treatment human disease. The Phase II proposal's objectives are to substantially improve the present instrument and methodology, progressing from the currently required 4.5 mg to 0.03 mg of protein/96 condition screen, and validate this method with extensive testing. This will be the basis for a macromolecule crystallization business operated on a fee-for-service basis. Experience with the Phase I instrument has indicated where improvements can be made in the plate set-up process, data collection optics, and electronics and the initial Phase II work will be to implement those improvements. Subsequent testing will first be with model proteins. For each model protein the concentration vs. anisotropy and intensity data obtained will be compared with crystallization screens set up in parallel, to define the signature curves indicating crystallization or potential crystallization outcomes and the extended data range over which crystallization conditions can be recovered. All anisotropy-derived leads will be tested with optimization screens. Subsequent testing will be to challenge the methodology using previously uncrystallized soluble and membrane proteins from the same source. The model and test protein data will be used in the subsequent development of software for data analysis. Projected Phase III efforts include developing incomplete factorial screens that can make full use of the quantitative data obtained by this method. PUBLIC HEALTH RELEVANCE: Successful crystallization and X-ray data analysis provides important three-dimensional information on the macromolecules structure-function relationship. Many proteins that are potential drug targets or key components in diseases are only available in trace quantities, or are difficult to obtain. This proposal is to continue development of a new approach to macromolecule crystallization, using a minimum amount of protein, and giving quantitative data that can subsequently be analyzed to determine those conditions which will give crystals and those that can be brought to crystallization conditions, thus giving a higher success rate.

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