Fluorescence Intensity-Based Scoring of Macromolecule Crystallization Plates

描述（由申请人提供）：结晶以及随后的结构测定是理解大分子结构-功能关系的主要步骤。了解大分子结构已成为新药开发的关键部分，也是NIH研究的一个主要领域。然而，结晶也是速率限制步骤，尽管技术上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。首先是实施低成本的落射荧光和透射显微镜系统的自动化文件的高分子结晶板的结果。第二是开发软件，用于对显微镜系统获得的结晶筛结果的图像进行快速评分。这项工作中评分过程的目标是基于来自痕量荧光标记的大分子（<0.5%的用荧光探针标记的分子）的荧光的图像像素强度快速识别可能的晶体，以及沉淀结果的分级评分，其反映了实验确定的那些结果对结晶条件进行优化的倾向。实验上，痕量荧光标记的蛋白质将经受结晶筛选，并且结果定期成像。将使用为此开发的软件进行基于强度的图像分析。基于图像分析显示高分数的结晶条件将进行优化筛选，并且我们提出所获得的分数与随后的结晶之间将存在相关性。 先前的研究已经表明，荧光可以是在筛选板中发现和识别晶体的有力辅助（Judge等人，2005; Forsythe等人，2006;格罗夫斯等人，2007; Pusey等人，2008年）。结晶为蛋白质提供了最密集的堆积状态，因此痕量荧光标记的蛋白质相对于澄清或沉淀的结果将具有最大的荧光强度。共价连接的探针充当蛋白质对溶液条件的响应的报告者。一些沉淀物显示出荧光的“亮点”，并且这些条件随后可以被优化为结晶条件（Pusey等人，2008年）。它进一步提出，基于强度的评分的降水结果可以用来更好地区分非生产性和潜在的生产性降水结果。初步测试表明，基于荧光强度的结晶屏幕评分应该非常快，处理时间可能为每个图像5秒。 要开发的能力是必要的，随后推出和销售的结晶筛选试剂盒，有一个平衡的不完全因子（IF）的方法来搜索“沉淀空间”在筛选过程中。这依赖于对结果的准确评分，如果手工完成，这是耗时且昂贵的劳动力。较大的实验室或研究小组可以负担开发自动化结晶板图像记录和评分系统所需的努力和设备。小团体不能。经济实惠的IF实施被视为有利于改善结晶和我们的业务长期发展努力。 公共卫生相关性：成功的结晶和X射线数据分析提供了重要的三维信息的大分子结构与功能的关系。许多蛋白质是潜在的药物靶点或疾病的关键成分，它们只能以微量获得，或难以获得。该提案旨在扩大蛋白质结晶过程中返回的数据以及从中获得的信息，将功能强大但价格实惠的结果记录和分析工具交给结晶实验室。

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.