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。它建立在广泛的先前工作的开发蛋白质标记方法的co-PI和软件系统的开发，自动分析的荧光显微镜图像中的亚细胞模式的PI。将使用高通量CD标记（蛋白捕获）方法在NIH 3 T3细胞中创建25，000个表达GFP蛋白融合的独立细胞系。随着细胞系的建立，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。将对图像进行自动化、计算机化图像分析，以将具有统计学上不可区分的模式的蛋白质分组。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中可用的任何信息进行比较。每个指定的位置将伴随着从组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学上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.