Fluorescence Intensity-based Scoring of Macromolecule Crystallization Plates

描述（由申请人提供）：结晶，随后的结构确定，是理解大分子结构-功能关系的重要步骤。了解大分子结构已成为新药开发的关键部分，也是NIH研究的一个重点领域。然而，结晶也是限制速率的步骤，尽管技术努力使设置和结晶数据采集过程自动化。第一阶段的工作成功地证明了可以组装低成本的表观荧光型结晶板成像系统，并且可以通过荧光图像的强度分析从明显失败的结果中获得铅结晶条件。该方法基于痕量共价标记，定义为被标记分子的< 0.5%，使用荧光探针在可见光谱中激发和发射蛋白质。这项建议的主要目标是扩大第一阶段的成果。首先是对结晶屏幕图像快速评分软件的改进，将该功能扩展到包括对不同结晶结果（针、板、3D晶体）的评分，并进一步提高评分成功率。其次是实现多色荧光能力，使仪器更适合大分子复合物的结晶，然后是该能力的测试和演示。第三是开发一种适用于整体膜蛋白的标记方法。四是通过我们实验室的持续使用和测试，进一步完善仪器和方法。实验中，痕量荧光标记的蛋白质将受到结晶筛选和结果定期成像。将使用在建议期间开发的不断发展的软件进行基于强度的图像分析。基于图像分析显示合适分数的沉淀条件将进行优化筛选，根据第一阶段的努力结果，我们期望获得的分数与后续结晶之间存在相关性。先前的研究表明，荧光可以在筛选板中发现和识别晶体（Judge等人，2005年；Forsythe等人，2006年；Groves等人，2007年；Pusey等人，2008年）。结晶为蛋白质提供了最密集的包装状态，因此痕量荧光标记的蛋白质相对于清晰或沉淀的结果将具有最大的荧光强度。共价结合探针作为报告蛋白对溶液条件的反应。一些沉淀物显示出荧光“亮点”，其中许多结果随后被优化为结晶条件（Pusey et al., 2008； I期结果）。因此，基于强度的降水结果评分可用于区分非生产性和潜在生产性降水结果。发现基于荧光强度的结晶筛分速度快，图像处理时间目前为3秒。

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 < 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 many of 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.