BUILDING AND VALIDATING LOCATION PROTEOMICS DATABASES

构建和验证位置蛋白质组数据库

• 批准号: 7813483
• 负责人: Robert F Murphy
• 金额: $ 51.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7813483
• 关键词: Active Learning; Address; American; Automation; Behavior; Biological Assay; Biological Markers; Cell Culture Techniques; Cell Line; Cells; Coin; Databases; Dependency; Disease; Drug Delivery Systems; Employment; Fill-It; Fluorescence Microscopy; Funding; Genes; Goals; Grant; Heart; Image; Image Analysis; Knowledge; Learning; Location; Machine Learning; Measures; Methods; Microscopy; Modeling; Molecular Bank; Mutation; NIH 3T3 Cells; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Principal Investigator; Proteins; Proteome; Proteomics; Recovery; Resolution; Robotics; Running; Screening procedure; Statistical Models; Systems Biology; Techniques; Testing; Time; United States National Institutes of Health; Work; abstracting; base; cell behavior; drug discovery; predictive modeling; programs; protein function; public health relevance; research study; response; two-dimensional

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.

该建议的主要目标是收集有关哺乳动物细胞中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。它基于通过Co-Pis和软件系统开发蛋白质标记方法的广泛工作，用于通过PI对荧光显微镜图像中亚细胞模式的自动分析。 25,000个独立的表达GFP蛋白融合的独立细胞系将在NIH 3T3细胞中使用高通量CD标记（蛋白质捕获）方法创建。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，并且在每种细胞系中标记的基因和蛋白质将通过高通量分子分析方法确定。这些图像将受到自动化计算机图像分析，以分组具有统计上无法区分的模式的蛋白质。将每种蛋白质的确定位置与蛋白质数据库，期刊文章和位置预测因子可获得的任何信息进行比较。每个分配的位置将伴随着结合这些来源的置信度估计。此外，每个蛋白质组的图像将用于构建可以合成新蛋白质分布在统计上等效原始图像的生成模型。合成分布的能力将为正常和疾病状态的细胞行为进行系统生物学建模提供重要的结构框架。