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.

项目概要 感知细胞环境动态变化并将该信息转化为重新连接的能力 生物分子相互作用构成了细胞信号转导的支柱。尽管有很大的兴趣和 投资能够检测和定量蛋白质-蛋白质和其他蛋白质-生物分子的方法 相互作用，最常用的方法仅绘制非生理环境中的相互作用 在细胞外，许多有助于所研究的相互作用的重要因素都丢失了。这些方法 特别不适合研究依赖于细胞结构和化学物质的细胞中的信号传导事件 环境才能形成和发挥作用。因此，需要新的方法来定量绘制蛋白质图谱 生命系统内部的“社交网络”。在这里，我们建议开发和验证几种互补光 依赖邻近分析平台能够检测活细胞中蛋白质相互作用动态 空间和时间分辨率，以及对细胞环境的最小扰动。我们将完成 这一目标通过三个相互关联的目标实现，这些目标得到了初步数据和我们之前发布的支持 使用细胞内光邻近分析平台。首先，我们将综合并测试可调参数 光邻近化学探针可在细胞内以纳米尺度绘制蛋白质复合物。与此同时，我们 建议测试潜在更有效的催化光接近分析平台，以提高 细胞内低丰度的大分子复合物。最后，我们建议应用这些平台来学习 通过集成的抗氧化剂动态感知细胞内代谢和氧化还原应激的变化 未折叠的蛋白质反应途径。这些邻近概况将能够起草第一个定量的、 细胞内综合应激反应的综合图，这将确定干预点 癌症、衰老和神经再生障碍等疾病。此外，方法和接近度概况 本文开发的也将广泛用于生物界，应用于解决各种问题 细胞内信号转导。