Measuring multiprotein assemblies that drive biological signals

描述（由申请人提供）：蛋白质相互作用形成复合物，这些复合物在传递生物信号时可以是动态和交换的。然而，尽管蛋白质复合物对生物学和疾病至关重要，但它们很难可视化和评估。当蛋白质复合物来自非基因工程的人类细胞时，比如那些将在临床患者样本中提供的细胞，分析蛋白质复合物的技术障碍就更大了。目前，由于生理蛋白质复合物图谱几乎无法获得，临床实践无法使用它们来帮助人类健康努力。我们建议通过建立一个新的分析平台MIF来推进一种新的策略，以在线分析生理，人类蛋白质复合物概况。我们已经学会了如何克服和控制技术障碍，使我们通过靶向21种人类蛋白质来启动MIF，这些蛋白质可以参与231种蛋白质间关联（Specific Aim 1）。MIF将允许分析小样本和高通量格式化，有利于其对来自临床患者的主要人类样本的采用。数据分析将涉及生成生物信息学策略（Specific Aim 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元化/聚集）和异型蛋白质关联。我们将实地测试MIF，将其应用于分析可能与普遍致命的脑癌胶质母细胞瘤伴随的免疫抑制相关的人类蛋白质复合物（Specific Aim 3）。总之，MIF及其独特的分析将使生理、人类复杂概况的获取成为可能。我们提出，胶质母细胞瘤衍生的MIF数据将举例说明分析这些复合物的新策略，并说明其在许多研究领域和疾病类别中的普遍适用性。

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.