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 major objectives of the proposed effort are two fold. First is the implementation of a low cost epifluorescence and transmission microscopy system for the automated documentation of macromolecule crystallization plate outcomes. Second is the development of software for the rapid scoring of the images of the crystallization screen outcomes obtained by the microscopy system. The goals of the scoring process in this work are the rapid identification of likely crystals based upon the image pixel intensity due to the fluorescence from trace fluorescently labeled macromolecules (<0.5% of the molecules labeled with fluorescent probe), and the graduated scoring of precipitation outcomes that reflects the experimentally determined propensity of those outcomes to be optimized to crystallization conditions. Experimentally, trace fluorescently labeled protein will be subjected to crystallization screens and the outcomes periodically imaged. Intensity-based image analysis will be carried out using the software developed for this effort. Precipitated conditions which show high scores based on the image analysis will be subjected to optimization screening, and we propose there will be 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 probe, being covalently attached, serves as a reporter to the protein's response to the solution conditions. Some precipitates showed 'bright spots'of fluorescence, and these conditions could subsequently be optimized to crystallization conditions (Pusey et al., 2008). It is further proposed that intensity-based scoring of precipitation outcomes may be used to better discriminate between non-productive and potentially productive precipitation results. Preliminary tests indicate that fluorescence intensity-based crystallization screen scoring should be very fast, with processing times likely to be 5 seconds per image. The capabilities to be developed are necessary for the subsequent introduction and sale of crystallization screening kits that have a balanced incomplete factorial (IF) approach to searching 'precipitation space'during the screening process. This relies upon accurate scoring of the outcomes, which is time consuming and expensive in labor if done by hand. Larger laboratories or research groups can afford the efforts and equipment needed to develop automated crystallization plate image documentation and scoring systems. Smaller groups cannot. Affordable IF implementation is seen as advantageous to improved crystallization and to our business long range development efforts.
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 expand the data returned during protein crystallization process and the information that can be derived from it, by putting a powerful but affordable results documentation and analysis tool into the hands of crystallization laboratories.
| Dinc, Imren; Dinc, Semih; Sigdel, Madhav et al. (2017) Super-Thresholding: Supervised Thresholding of Protein Crystal Images. IEEE/ACM Trans Comput Biol Bioinform 14:986-998 |
| Sigdel, Madhav; Dinc, Imren; Sigdel, Madhu S et al. (2017) Feature analysis for classification of trace fluorescent labeled protein crystallization images. BioData Min 10:14 |
| Sigdel, Madhu S; Sigdel, Madhav; Dinç, Semih et al. (2016) FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure. IEEE/ACM Trans Comput Biol Bioinform 13:326-40 |
| Dinc, Imren; Pusey, Marc L; Aygun, Ramazan S (2016) Optimizing Associative Experimental Design for Protein Crystallization Screening. IEEE Trans Nanobioscience 15:101-12 |
| Meyer, Arne; Betzel, Christian; Pusey, Marc (2015) Latest methods of fluorescence-based protein crystal identification. Acta Crystallogr F Struct Biol Commun 71:121-31 |
| Sigdel, Madhav; Pusey, Marc L; Aygun, Ramazan S (2015) CrystPro: Spatiotemporal Analysis of Protein Crystallization Images. Cryst Growth Des 15:5254-5262 |
| Pusey, Marc; Barcena, Jorge; Morris, Michelle et al. (2015) Trace fluorescent labeling for protein crystallization. Acta Crystallogr F Struct Biol Commun 71:806-14 |
| Dinç, ?mren; Sigdel, Madhav; Dinç, Semih et al. (2014) Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images. Proc IEEE Southeastcon 2014: |
| Sigdel, Madhu S; Sigdel, Madhav; Dinç, Semih et al. (2014) Autofocusing for Microscopic Images using Harris Corner Response Measure. Proc IEEE Southeastcon 2014: |
| Sigdel, Madhav; Dinç, ?mren; Dinç, Semih et al. (2014) Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery. Proc IEEE Southeastcon 2014: |
Showing the most recent 10 out of 11 publications
