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Measuring multiprotein assemblies that drive biological signals

测量驱动生物信号的多蛋白组装体

基本信息

批准号：
9020977
负责人：
Adam G. Schrum
金额：
$ 30.4万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
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/9020977
关键词：
Alopecia Areata Autoimmune Diseases Award 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 Epitopes Flow Cytometry Generations Grant Health Homo Human Human Engineering Imagery Immune In Vitro Learning Maps Measurement Measures Methodology Methods Microspheres Mouse Protein Mus Pathway interactions Patients Physiological Predictive Value Principal Investigator Proteins Publishing Sample Size Sampling Signal Transduction Signaling Protein Source Surface Systems Analysis T-Lymphocyte Therapeutic Yeasts abstracting 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 styrofoam trafficking treatment response yeast two hybrid system

项目摘要

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.

授权号：1R01GM103841-01A1 首席研究员：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通过电子邮件发送至：researchadmin@mayo.edu 项目期间：2013年1月4日 – 2018年3月31日当前日期：2015 年 4 月 15 日修改后的项目摘要/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，尽管蛋白质复合物对生物学和疾病至关重要，但它们可能很难可视化和评估。当蛋白质复合物源自非基因工程人类细胞（例如临床患者样本中提供的细胞）时，分析蛋白质复合物会面临更多技术障碍。目前，由于生理蛋白质复合物谱实际上无法获得，临床实践无法利用它们来协助人类健康事业。我们建议通过安装新的检测平台 MIF 来推进一项新策略，以在线分析人类蛋白质复合物的生理谱。我们已经学会了如何克服和控制技术障碍，因此我们通过靶向可参与 231 个蛋白质间关联的 21 种人类蛋白质来推出 MIF（具体目标 1）。 MIF 将允许分析小样本和高通量格式化，有利于其适用于来自临床患者的原始人类样本。数据分析将涉及生物信息学策略的生成（具体目标 2），重点关注由暴露的表面表位检测到的共享复合物中蛋白质的多样性，以评估网络蛋白质关联。我们将通过将 MIF 应用于分析可能与自身免疫性疾病斑秃相关的人类蛋白质复合物来对 MIF 进行现场测试（具体目标 3）。 MIF 及其独特的分析将共同实现人体复杂的生理特征的采集。我们建议，源自患者的 MIF 数据将举例说明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。