Building and Validating Location Proteomics Databases

描述（由申请人提供）：本提案的主要目标是收集哺乳动物细胞内蛋白质分布的高分辨率信息，并将其与核苷酸和蛋白质序列联系起来。它建立在广泛的前期工作的基础上，这些工作是由合作PI开发的蛋白质标记方法，以及PI开发的用于荧光显微镜图像中亚细胞模式自动分析的软件系统。将在NIH 3T3细胞中使用高通量cd标记（蛋白质捕获）方法创建25,000个表达GFP蛋白融合的独立细胞系。随着细胞系的创建，将使用荧光显微镜收集高分辨率荧光显微镜图像，并通过高通量分子分析方法确定每个细胞系中标记的基因和蛋白质。这些图像将经过自动的计算机图像分析，以统计上无法区分的模式对蛋白质进行分组。每种蛋白质的确定位置将与蛋白质数据库、期刊文章和位置预测器提供的任何信息进行比较。每个指定地点都将附有综合这些来源得出的置信度估计。此外，每个蛋白质组的图像将用于构建生成模型，该模型可以合成与原始图像统计等效的新蛋白质分布。合成分布的能力将为正常和疾病状态下细胞行为的系统生物学建模提供重要的结构框架。

DESCRIPTION (provided by applicant): The primary goal of this proposal is to collect high-resolution information on the distribution of proteins within mammalian cells and to link it to nucleotide and protein sequences. It builds on extensive prior work on development of protein tagging methods by the co-PIs and on development of software systems for automated analysis of subcellular patterns in fluorescence microscope images by the PI. 25,000 independent cell lines expressing GFP protein fusions will be created in NIH 3T3 cells using high-throughput CD-tagging (protein-trapping) methods. As the cell lines are created, high-resolution fluorescence microscope images will be collected using fluorescence microscopy and the gene and protein tagged in each cell line will be determined by high-throughput molecular analysis methods. The images will be subjected to automated, computerized image analysis to group proteins with statistically indistinguishable patterns. The determined location for each protein will be compared to whatever information is available from protein databases, journal articles and location predictors. Each assigned location will be accompanied by a confidence estimate derived from combining these sources. In addition, the images for each protein group will be used to build generative models that can synthesize new protein distributions statistically equivalent to the original images. The ability to synthesize distributions will provide an important structural framework for systems biology modeling of cell behavior in normal and disease states.