Measuring multiprotein assemblies that drive biological signals

描述（由申请人提供）：蛋白质彼此相互作用形成复合物，并且这些复合物可以是动态的，并且在它们传递生物信号时可以互换。然而，尽管它们对生物学和疾病至关重要，但蛋白质复合物可能难以可视化和评估。当蛋白质复合物来源于非基因工程人类细胞时，例如那些将在临床患者样本中提供的蛋白质复合物，分析它们甚至存在更多的技术障碍。目前，由于生理蛋白质复合物概况几乎是不可获得的，临床实践不能使用它们来帮助人类健康的努力。我们建议通过安装一个新的分析平台，MIF，在线分析生理，人类蛋白质复合物的配置文件，以推进一个新的战略。我们已经学会了如何克服和控制技术障碍，这使我们通过靶向21种可以参与231种蛋白质间缔合的人类蛋白质来启动MIF（具体目标1）。MIF将允许分析小样本和高通量格式化，有利于其适用于源自临床患者的原始人类样本。数据分析将涉及生成生物信息学策略（具体目标2），重点关注蛋白质复合物的三个独特参数：蛋白质丰度，共享复合物中的蛋白质多样性（通常被认为是蛋白质多聚/聚集）和异型蛋白质关联。我们将通过将其应用于人类蛋白质复合物的分析来现场测试MIF，这些蛋白质复合物可能与伴随普遍致命的脑癌胶质母细胞瘤的免疫抑制有关（具体目标3）。总之，MIF及其独特的分析将使获得生理，人类复杂的配置文件。我们提出胶质母细胞瘤衍生的MIF数据将为分析这些复合物提供一种新的策略，并说明其对许多研究领域和疾病类别的普遍适用性。

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.