Measuring multiprotein assemblies that drive biological signals

授权编号：1 R 01 GM 103841 - 01 A1 主要研究者：Adam G. Schrum博士 项目名称：测量驱动生物信号的多蛋白组装体 奖项电邮至：researchadmin@mayo.edu 项目时间：2013年1月4日至2018年3月31日 当前日期：2015年4月15日 修改项目摘要/摘要部分 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的和互换的。 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），重点关注通过暴露的表面表位检测到的共享复合物中的蛋白质多样性，以评估网络蛋白质关联。 我们将通过将其应用于可能与自身免疫性疾病斑秃（特异性目标3）相关的人类蛋白质复合物的分析来现场测试MIF。 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。 我们建议，患者衍生的MIF数据将成为分析这些复合物的新策略，并说明其对许多研究领域和疾病类别的普遍适用性。

Grant Number: 1R01GM103841-01A1 Principal Investigator(s): Adam G. Schrum, PHD Project Title: Measuring multiprotein assemblies that drive biological signals Award e-mailed to: researchadmin@mayo.edu Project Period: 04/01/2013 – 03/31/2018 Current Date: April 15, 2015 Modified Project Summary/Abstract Section 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 multiplicity of proteins in shared complexes detected by exposed surface epitopes, to assess network protein associations. We will field-test MIF by applying it to the analysis of human protein complexes that may be associated with the autoimmune disease, Alopecia Areata (Specific Aim 3). Together, MIF and its unique analysis will make available the acquisition of physiologic, human complex profiles. We propose that the patient-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.