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。拟议努力的主要目标有两个方面。首先是实现低成本的荧光和透射显微镜系统，用于自动记录大分子结晶板结果。其次是软件的开发，用于对显微镜系统获得的结晶筛选结果图像进行快速评分。在这项工作中，评分过程的目标是基于痕量荧光标记的大分子（荧光探针标记的分子<0.5%）的荧光所产生的图像像素强度，快速识别可能的晶体，并对沉淀结果进行分级评分，该评分反映了实验确定的这些结果对结晶条件进行优化的倾向。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。基于强度的图像分析将使用为此开发的软件进行。根据图像分析得出高分的沉淀条件将进行优化筛选，我们提出所得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。探针，共价连接，作为报告蛋白对溶液条件的反应。一些沉淀物显示出荧光的“亮点”，这些条件随后可以优化为结晶条件（Pusey et al., 2008）。进一步提出，基于强度的降水结果评分可用于更好地区分非生产性和潜在生产性降水结果。初步测试表明，基于荧光强度的结晶屏幕评分应该非常快，处理时间可能为每张图像5秒。需要开发的能力对于随后引入和销售结晶筛选试剂盒是必要的，该试剂盒具有在筛选过程中搜索“沉淀空间”的平衡不完全析因（IF）方法。这依赖于对结果的准确评分，如果手工完成，这既耗时又昂贵。较大的实验室或研究小组可以负担得起开发自动结晶板图像记录和评分系统所需的努力和设备。较小的团体则不能。负担得起的IF实现被视为有利于改进结晶和我们的业务长期发展努力。

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 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. 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 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.