Analysis of force developed by a AAA ATPase
Analysis of force developed by a AAA ATPase
批准号:
8536863
负责人:
SANFORD M SIMON
金额:
$29.73万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-30 至 2015-08-31
关键词:
ATP HydrolysisATP phosphohydrolaseAddressAffectAmino AcidsApoptosisBacteriaBindingBiogenesisBiological ClocksBiologyCell CycleCell divisionCell physiologyClinical TreatmentClinical TrialsCommitDecision TreesDevicesDigestionFailureFamilyGene Expression RegulationGenerationsGenetic TranscriptionGoalsHalf-LifeHandHealthHumanIndividualLengthMalignant NeoplasmsMeasurementMeasuresMethodsMitochondriaModelingMolecular MachinesMovementMultiple MyelomaMultivesicular BodyMutateNervous System TraumaOutcomePathologyPerformancePropertyProteasome InhibitorProteinsProteolysisRecurrenceRegulationRoleSideSignal Transduction PathwaySiteStatistical DistributionsStressSurfaceSystemTechniquesTestingTherapeutic InterventionTimeTransport Protein GeneWarWorkantigen processingdesensitizationhuman diseaseinsightmembermulticatalytic endopeptidase complexpolypeptideprotein degradationreceptorsingle molecule
中文摘要
描述(申请人提供):AAA ATPase家族包括其作用包括但远不限于,线粒体和多囊泡体的生物发生,参与基因调控和蛋白质运输的蛋白质。这个项目将集中在单分子水平上研究这些机器的活动。模型蛋白质将是ClpX--它是蛋白质小体的一部分,蛋白质降解的关键作用是展开蛋白质,为降解做好准备。。细胞蛋白质的易损性差异很大,半衰期从几分钟到几天不等。通过允许细胞蛋白质水平的快速变化来调节降解,有助于控制信号转导途径、细胞周期、转录、凋亡、抗原处理、生物钟控制、分化和表面受体脱敏。需要解决的问题是:如何在可选结果之间分配工作?系统可以执行的最大工作量是多少?有哪些因素限制了它的工作效率?这些问题对健康有影响:人类的病理状况与降解系统的故障有关,它的调节提供了治疗干预的可能性。此外,一种蛋白酶体催化活性的抑制剂正在用于治疗复发性多发性骨髓瘤,蛋白酶体抑制剂正在进行临床试验,用于治疗广泛的人类恶性肿瘤。因此,了解蛋白质半衰期的调节应该为细胞生理学和病理学提供关键的见解。对异常蛋白质的不当处理在整个生物学中都会招致惩罚:受到压力的细菌的生存取决于处理错误折叠和结构异常蛋白质的系统的有效性能-要么正确折叠它们,要么摧毁它们。需要解决的具体问题是:设备的拉力能多大程度上导致展开?需要多少拉力才能不可逆转地承诺?拉力的极限是多少,拉力的统计分布是什么?对这些问题的回答将不仅揭示这些机器的功能,还将揭示描述结果如何控制以及何时可能超出机器能力的决策树。我们不仅想知道机器是如何工作的,而且想知道它的决策树是如何产生不同的结果的。
英文摘要
DESCRIPTION (provided by applicant): The AAA ATPases family includes molecules whose roles include, but are far from limited to, biogenesis of mitochondria and multivesicular bodies, proteins in involved in gene regulation and protein transport. This project will focus on studying the activity of these machines at the single molecule level. The model protein will be ClpX - it is the part of the proteosome, a key effect of protein degradation that unfolds the proteins to prepare them for degradation. . Cellular proteins differ widely in their liability, from half-lives of minutes to days. Regulated degradation, by allowing rapid changes in the levels of cellular proteins, helps control signal transduction pathways, the cell- cycle, transcription, apoptosis, antigen processing, biological clock control, differentiation and surface receptor desensitization. The questions to be addressed are: How is work partitioned between alternative outcomes? What is the maximum work that can be performed by the system? What factors limit its efficiency in performing work? These questions have health implications: human pathological conditions are associated with failures of the degradation system and its regulation offers the potential for therapeutic intervention. Furthermore, an inhibitor of proteasome catalytic activity is in use for treatment of recurrent multiple myeloma, and proteasome inhibitors are in clinical trial for treatment of a broad spectrum of human malignancies. Thus, understanding the regulation of the half-life of proteins should provide critical insights into cell physiology and pathology. The mishandling of aberrant proteins incurs penalties throughout biology: the survival of bacteria subjected to stress depends on the effective performance of systems which deal with misfolded and structurally aberrant proteins- to either fold them properly or destroy them. The specific questions to be addressed are: How hard can the device pull to cause unfolding? How many pulls are needed to commit irreversibly? What is the limit of pulling power, and the statistical distribution of pulling power? Answers to these questions will begin to reveal not just what these machines do but the decision tree that describes how outcomes are controlled and when machine capacity may be exceeded. We want to know not just how the machine works, but how its decision tree yields alternative outcomes.
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