Mapping Protein Social Network Dynamics with Photoproximity Profiling Platforms

使用 Photoproximity 分析平台绘制蛋白质社交网络动态

• 批准号: 10707896
• 负责人: Raymond E Moellering
• 金额: $ 31.74万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707896
• 关键词: Aging; Antioxidants; Architecture; Benchmarking; Biological; Biological Assay; Biology; Cells; Cellular Stress; Cellular Structures; Cementation; Characteristics; Chemicals; Communities; Data; Data Set; Degenerative Disorder; Development; Disease; Environment; Event; Future; Genetic Transcription; Goals; Grant; Homeostasis; In Situ; In Vitro; Intervention; Investments; Kinetics; Label; Light; Macromolecular Complexes; Malignant Neoplasms; Maps; Mediating; Metabolic; Metabolic stress; Metabolism; Methods; Molecular; Nature; Neurodegenerative Disorders; Organism; Oxidation-Reduction; Pathway interactions; Performance; Positioning Attribute; Property; Protein Dynamics; Proteins; Proteomics; Publishing; Radial; Regulation; Research; Resolution; Signal Pathway; Signal Transduction; Social Network; Stress; System; Testing; Therapeutic Intervention; Translating; Vertebral column; Work; analysis pipeline; biological adaptation to stress; interest; member; nanoscale; next generation; novel; preference; programs; protein complex; response; sensor; technology platform; temporal measurement; tool; transcription factor

Project Summary The ability to sense dynamic changes in the cellular environment and translate that information into rewired biomolecular interactions forms the backbone of cellular signal transduction. Despite significant interest and investment in methods capable of detecting and quantifying protein-protein and other protein-biomolecule interactions, the most commonly employed methods solely map interactions in non-physiologic environments outside of cells where many important factors contributing to the interactions under study are lost. These methods are particularly poorly suited to study signaling events in cells that rely on the cellular architecture and chemical environment in order to form and function. Therefore, new methods are needed to quantitatively map protein “social networks” inside of living systems. Here we propose to develop and validate several complementary light- dependent proximity profiling platforms capable of detecting protein interaction dynamics in live cells with high spatial and temporal resolution, as well as minimal perturbation to the cellular environment. We will accomplish this goal through three interconnected aims that are supported by preliminary data and our previously published work with an intracellular photoproximity profiling platform. First, we will synthesize and test tunable photoproximity chemical probes to map protein complexes at nanometer scale inside of cells. In parallel, we propose to test potentially more efficient catalytic photoproximity profiling platforms for increased resolution of low abundance macromolecular complexes inside of cells. Finally, we propose to apply these platforms to study the dynamic sensing of altered metabolic and redox stress inside cells through the integrated antioxidant and unfolded protein response pathways. These proximity profiles will enable drafting of the first quantitative, comprehensive maps of the integrated stress response in cells, which will identify points of intervention for diseases such as cancer, aging and neurogenerative disorders. Furthermore, the methods and proximity profiles developed herein will also be widely useful to the biological community for application to diverse questions in intracellular signal transduction.

项目摘要 感知细胞环境中动态变化并将这些信息转化为重新连接的能力 生物分子相互作用形成了细胞信号转导的主干。尽管很感兴趣，并且 投资能够检测和量化蛋白质 - 蛋白质和其他蛋白质生物分子的方法 相互作用，最常用的方法仅在非生理环境中绘制相互作用 在许多重要因素导致研究相互作用的细胞之外。这些方法 特别适合研究依赖细胞结构和化学的细胞中的信号事件 环境以形成和功能。因此，需要新的方法来定量绘制蛋白质 生活系统内部的“社交网络”。在这里，我们建议开发和验证几种完整的光 依赖性分析平台，能够检测高活细胞中蛋白质相互作用动力学 空间和临时分辨率，以及对细胞环境的最小扰动。我们将完成 通过三个相互联系的目标，这些目标得到了初步数据的支持和我们先前发布的 使用细胞内光耐力分析平台。首先，我们将合成并测试可调节 在细胞内部的纳米尺度上绘制蛋白质复合物的光耐化学问题。并行，我们 提议测试潜在的更有效的催化光耐谱分析平台，以增加分辨率的分辨率 细胞内部的低抽象大分子复合物。最后，我们建议将这些平台应用于研究 通过综合抗氧化剂和 展开的蛋白质反应途径。这些接近度概况将使第一定量的起草， 细胞中综合应力反应的综合图，该图将确定干预点 癌症，衰老和神经源性疾病等疾病。此外，方法和接近度概况 本文开发的也将对生物学界广泛有用，以应用于各种问题 细胞内信号转导。