Measuring multiprotein assemblies that drive biological signals

测量驱动生物信号的多蛋白组装体

• 批准号: 8528873
• 负责人: Adam G. Schrum
• 金额: $ 31.39万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8528873
• 关键词: Binding; Biochemical; Biochemical Process; Biocompatible Materials; Bioinformatics; Biological; Biological Assay; Biology; Cells; Chimeric Proteins; Clinical; Co-Immunoprecipitations; Collection; Complex; Data; Data Analyses; Data Set; Disease; Drug Targeting; Flow Cytometry; Generations; Glioblastoma; Health; Homo; Human; Human Engineering; Imagery; Immune; Immunosuppression; In Vitro; Learning; Malignant Neoplasms; Malignant neoplasm of brain; Maps; Measurement; Measures; Methodology; Methods; Microspheres; Mouse Protein; Mus; Pathway interactions; Patients; Physiological; Predictive Value; Proteins; Publishing; Sample Size; Sampling; Signal Transduction; Source; Systems Analysis; T-Lymphocyte; Testing; Therapeutic; Yeasts; base; biosignature; clinical practice; clinically relevant; design; dimer; field study; innovation; interest; meetings; monomer; novel; outcome forecast; protein complex; protein protein interaction; public health relevance; research study; response; styrofoam; trafficking; yeast two hybrid system; yellow-1

DESCRIPTION (provided by applicant): Proteins interact with each other to form complexes, and these complexes can be dynamic and interchanging as they relay biological signals. However, despite their central importance to biology and disease, protein complexes can be difficult to visualize and assess. There are even more technological barriers to analyzing protein complexes when they originate from non-genetically engineered human cells, such as those that would be provided in clinical patient samples. Currently, because physiologic protein complex profiles are virtually unobtainable, clinical practice cannot use them to assist in human health endeavors. We propose to advance a new strategy by mounting a new assay platform, MIF, to bring the analysis of physiologic, human protein complex profiles online. We have already learned how to overcome and control for the technical hurdles, leading us to launch MIF by targeting 21 human proteins that can participate in 231 inter-protein associations (Specific Aim 1). MIF will allow for analysis of small samples and high-throughput formatting, favoring its adoptability for primary human samples originating from clinical patients. Data analysis will involve the generation of Bioinformatics strategies (Specific Aim 2) to focus on three unique parameters of protein complexes: protein abundance, protein multiplicity in shared complexes (commonly thought of as protein plurization/aggregation), and heterotypic protein associations. We will field-test MIF by applying it to the analysis of human protein complexes that may be associated with the immune suppression that accompanies the universally lethal brain cancer, glioblastoma (Specific Aim 3). Together, MIF and its unique analysis will make available the acquisition of physiologic, human complex profiles. We propose that the glioblastoma- derived MIF data will exemplify a new strategy for analyzing these complexes, and illustrate its general applicability to many fields of study and classes of disease.

描述(申请人提供)：蛋白质相互作用形成复合体，这些复合体在传递生物信号时可以是动态的和互换的。然而，尽管蛋白质复合体对生物学和疾病至关重要，但它们可能很难被可视化和评估。当蛋白质复合体来自非基因工程的人类细胞时，分析它们甚至面临更多的技术障碍，比如那些将在临床患者样本中提供的蛋白质复合体。目前，由于生理蛋白质复合体的图谱几乎无法获得，临床实践无法使用它们来帮助人类的健康努力。我们建议通过安装一个新的分析平台MIF来推进一项新的战略，使生理的、人类蛋白质复合体的分析在线。我们已经学会了如何克服和控制技术障碍，导致我们启动了MIF，针对能够参与231个蛋白质间联系的21个人类蛋白质(具体目标1)。MIF将允许分析小样本和高通量格式化，有利于其适用于来自临床患者的初级人类样本。数据分析将涉及制定生物信息学战略(具体目标2)，以侧重于蛋白质复合体的三个独特参数：蛋白质丰度、共享复合体中的蛋白质多样性(通常被认为是蛋白质多元化/聚集)和异型蛋白质相关性。我们将通过将MIF应用于人类蛋白质复合体的分析来进行现场测试，这些蛋白质复合体可能与伴随着普遍致命的脑癌-胶质母细胞瘤(特定目标3)的免疫抑制有关。总之，MIF及其独特的分析将使获取生理的、人体的复杂轮廓成为可能。我们认为，来自胶质母细胞瘤的MIF数据将例证一种分析这些复合体的新策略，并说明其在许多研究领域和疾病类别中的普遍适用性。