Mapping Protein Social Network Dynamics with Photoproximity Profiling Platforms
Mapping Protein Social Network Dynamics with Photoproximity Profiling Platforms
批准号:
10707896
负责人:
Raymond E Moellering
金额:
$31.74万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-21 至 2026-08-31
关键词:
AgingAntioxidantsArchitectureBenchmarkingBiologicalBiological AssayBiologyCellsCellular StressCellular StructuresCementationCharacteristicsChemicalsCommunitiesDataData SetDegenerative DisorderDevelopmentDiseaseEnvironmentEventFutureGenetic TranscriptionGoalsGrantHomeostasisIn SituIn VitroInterventionInvestmentsKineticsLabelLightMacromolecular ComplexesMalignant NeoplasmsMapsMediatingMetabolicMetabolic stressMetabolismMethodsMolecularNatureNeurodegenerative DisordersOrganismOxidation-ReductionPathway interactionsPerformancePositioning AttributePropertyProtein DynamicsProteinsProteomicsPublishingRadialRegulationResearchResolutionSignal PathwaySignal TransductionSocial NetworkStressSystemTestingTherapeutic InterventionTranslatingVertebral columnWorkanalysis pipelinebiological adaptation to stressinterestmembernanoscalenext generationnovelpreferenceprogramsprotein complexresponsesensortechnology platformtemporal measurementtooltranscription factor
中文摘要
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英文摘要
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.
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依托单位:
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依托单位:
海外基金