A S. cerevisiae high-coverage high-quality protein-protein binary interactome map

描述（由申请人提供）：在寻求生成预测性“系统”模型的过程中，具有增加的质量和灵敏度的蛋白质-蛋白质相互作用网络（相互作用组）模型的生成是必要的，尽管不是充分的。酵母S. cerevisiae是验证这一概念的极好模型。产生互补高质量（HQ）数据的两种主要方法用于实验性地映射相互作用组：i）二元物理相互作用映射;和ii）和蛋白质复合物分析。这两种方法都需要获得一个完整的视图的生物体的相互作用。 我们最近开发了一个概念框架，以评估相互作用组模型的基础上，对参考集的筛选和验证测定的定量基准。然后，该框架结合了完整性，检测灵敏度，采样灵敏度和精度的具体测量，以估计网络模型的整体灵敏度和质量。由此，我们已经表明，早期酵母相互作用组数据覆盖约10%，我们的第二代图谱覆盖约20%的具有HQ相互作用的酵母二元相互作用组。 为了日益标准化和改进的质量控制，我们开发了一种实验方法来为个体交互分配置信度分数。该方法使用相互作用测定的“工具箱”，每个测定以共同的参考集为基准。工具包检测中每个相互作用的实验验证能够整合基准和验证数据，以计算个体置信度分数。 在这里，我们建议继续对S的二元相互作用组进行绘图工作。啤酒。利用新技术，我们的目标是将整体灵敏度扩展到50%，这在技术上是可行的，并且可以预期能够深刻地改善对相互作用组网络及其如何介导基因型与表型关系的理解。对于质量控制，将在多个标准化二元相互作用试验中验证所有相互作用，并计算每个单独相互作用的置信度评分。因此，我们将产生和分析第三代的二进制相互作用组图的S。啤酒。我们的具体目标是：i）扩展S.酿酒酵母从~20到~50%的灵敏度，ii）用基于高度受控和基准化的测定工具-试剂盒的新型置信度评分策略，通过实验验证特异性目标1中发现的所有二元相互作用。（3）扩大S.酿酒酵母二元相互作用组网络使用新的高质量的地图中获得的具体目标2。

DESCRIPTION (provided by applicant): The generation of protein-protein interaction network (interactome) models with increasing quality and sensitivity is a necessary, although not sufficient, aspect in the quest of generating predictive "systems" models. The yeast S. cerevisiae is an excellent model to validate this concept. Two major approaches, yielding complementary high-quality (HQ) data are used to experimentally map interactomes: i) binary physical interaction mapping; and ii) and protein complex analysis. Both approaches are required to obtain a complete view of the interactome of an organism. We recently developed a conceptual framework to assess interactome models based on quantitative benchmarking of screening and validation assays against reference sets. The framework then combines specific measurements of completeness, assay sensitivity, sampling sensitivity and precision to estimate the overall sensitivity and quality of a network model. With this we have shown that early yeast interactome data cover ~10%, our second generation map ~20%, of the yeast binary interactome with HQ interactions. For increasingly standardized and improved quality control, we developed an experimental method to assign confidence scores to individual interactions. This method uses a "tool-kit" of interaction assays each benchmarked against common reference sets. Experimental validation of every interaction in the tool-kit assays enables integration of benchmark and validation data to calculate individual confidence scores. Here we propose to continue the mapping efforts for the binary interactome of S. cerevisiae. Utilizing novel technologies we aim to extend the overall sensitivity to 50%, which is both technically feasible and can be expected to enable a profoundly improved understanding of the interactome network and how it mediates genotype-to-phenotype relationships. For quality control all interactions will be validated in multiple standardized binary interaction assays and a confidence score for each individual interaction will be calculated. We will thus generate and analyze a third generation binary interactome map of S. cerevisiae. Our specific aims are: i) to expand the binary interactome map of S. cerevisiae from ~20 to ~50% sensitivity, ii) to validate experimentally all binary interactions found in Specific Aim 1 with a novel confidence scoring strategy based on a highly-controlled and benchmarked assay tool-kit. iii) To expand the global analysis of the S. cerevisiae binary interactome network using the new high- quality map obtained in Specific Aim 2.