A Mixed-Bait-Strategy for Protein Interactome Mapping

蛋白质相互作用组图谱的混合诱饵策略

• 批准号: 7143465
• 负责人: JING HUANG
• 金额: $ 14.21万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7143465
• 关键词: library; protein protein interaction; proteins; yeasts

DESCRIPTION (provided by applicant): Understanding protein function on a genome-wide scale is a main goal of biology. An important step towards achieving this goal is through unraveling protein interactome networks. Despite advances in high- throughput technologies, library-against-library screening with proteome-wide coverage remains a daunting task. This proposal presents a novel pooling and deconvolution strategy (MBS) that is generally applicable to maximize screening efficiency in a wide variety of situations. MBS has three key components: imaginary tagging (binary coding) of baits, combinatorial mix-bait screening, and built-in prey-bait tracking and cross- validation. MBS reaches significantly higher coverage than conventional single-bait strategy under a wide range of experimental errors. MBS has the potential to greatly accelerate interactome mapping from yeast to human and is useful for many other library-against-library screens (including drug screening). The key advantages of MBS include better accuracy, coverage, and efficiency. Its versatility lies in imaginary tagging, which is universally applicable regardless of the nature of the query (molecules, cells, organisms, etc). Protein-protein interactions (PPIs) underlie a wide range of physiological and disease processes. Despite advances in high-throughput technologies, large-scale PPI mapping remains a daunting task due to the huge demand of time and resources. We introduce a novel strategy (called MBS) that will greatly reduces the number of screens needed while simultaneously increasing accuracy and coverage. MBS-guided community efforts should greatly accelerate interactome mapping from yeast to human, and find many other biomedical applications.

描述(由申请人提供)：在全基因组范围内了解蛋白质功能是生物学的主要目标。实现这一目标的重要一步是通过解开蛋白质相互作用组网络。尽管高通量技术取得了进步，但蛋白质组覆盖的文库对文库筛选仍然是一项艰巨的任务。这一建议提出了一种新的合并和去卷积策略(MBS)，该策略通常适用于在各种情况下最大限度地提高筛选效率。MBS有三个关键组件：假想的诱饵标记(二进制编码)、组合混合诱饵筛选以及内置的猎物诱饵跟踪和交叉验证。在广泛的实验误差范围内，MBS的覆盖率明显高于传统的单诱饵策略。MBS有可能极大地加速从酵母到人类的相互作用组图，并可用于许多其他文库对文库的筛选(包括药物筛选)。MBS的主要优势包括更好的准确性、覆盖率和效率。它的多功能性在于想象的标签，无论查询的性质(分子、细胞、生物体等)如何，它都是普遍适用的。蛋白质-蛋白质相互作用(PPI)是广泛的生理和疾病过程的基础。尽管高通量技术取得了进步，但由于时间和资源的巨大需求，大规模PPI测绘仍然是一项艰巨的任务。我们引入了一种新的策略(称为MBS)，它将大大减少所需的屏幕数量，同时提高准确性和覆盖率。MBS引导的社区努力将极大地加速从酵母到人类的相互作用组图，并找到许多其他生物医学应用。