Novel Quantitative Proteomic Methods to Discover and Localize Endogenous Protein Complexes

发现和定位内源蛋白质复合物的新定量蛋白质组学方法

• 批准号: 1127027
• 负责人: Daniel Szymanski
• 金额: $ 60.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-15 至 2017-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1127027&HistoricalAwards=false
• 关键词: Novel; Quantitative; Proteomic; Methods; Discover

PI: Daniel Szymanski (Purdue University) CoPIs: Mark Hall, Daisuke Kihara, Jun Xie (Purdue University) There is a strong need for systems-level data sets that enable a more efficient translation of basic knowledge into improved crop traits. Information on protein complex composition is one such example, but resources for crop plants are lacking. This project proposes a novel mass spectrometry-based method to solve endogenous protein complexes in soybean. The results will serve as hypothesis-generating machines for the reverse-genetic analysis of a major crop. The approach couples multiple chromatography separations of cytosolic extracts with quantitative label-free mass spectrometry of the column fractions. The relative abundance of the proteins across the purification scheme will be used to cluster co-purifying proteins into groups that reveal protein complex composition. Unlike other large-scale, protein interaction techniques, this new approach is simple to experimentally validate using existing mutant collections. This inexpensive technique does not require gene replacement technology, large-scale gene cloning, or targeted purification strategies. To establish the effectiveness of this method, the investigators will pursue three research objectives: 1) to predict and validate the composition of 100 endogenous protein complexes isolated from leaf cytosol; 2) to use bioinformatics and existing "omics" data sets to predict and evaluate models for protein complex composition; and 3) to determine if the compositions of cytosolic protein complexes are conserved in Glycine max. Although mass spectrometry has been used extensively in proteomics research, to the investigators' knowledge the proposed strategy for large-scale protein complex prediction has not been done. Successful completion of this work will provide broadly useful results, databases, and reagents that will reveal the composition of endogenous protein complexes. This deep analysis of the cytosolic proteome will provide an important data set that enables crop scientists to further modify proteins and pathways with the goal to improve crop productivity. The Purdue Initiative on Plant Protein Interactions (PIPPI) will provide broad impact by reaching diverse audiences through such activities as research opportunities for undergraduate and graduate students and postdoctoral fellows; and a website providing data on plant protein complexes for researchers. Students will gain skills collaborating in diverse university research environments, be exposed to cutting-edge research in plant proteomics, and develop leadership and organizational abilities by assisting with project management, research design, and data release. By partnering with various campus diversity programs and offices, all aspects of the project will involve underrepresented undergraduate and graduate students in STEM disciplines. Proteomic data sets will be available to the public at https://proteomecommons.org. It is expected that this novel project will help prepare the next generation of plant geneticists that utilize diverse types of data to drive crop improvement strategies.

主要研究者：丹尼尔Szymanski（普渡大学）CoPIs：马克霍尔，Daisuke Kihara，Jun Xie（普渡大学）有一个系统级的数据集，使基础知识更有效地转化为改善作物性状的强烈需求。蛋白质复合物组成的信息就是这样一个例子，但缺乏作物的资源。本项目提出了一种新的基于质谱的方法来解决大豆内源蛋白质复合物。这些结果将作为一种主要作物反向遗传分析的假设生成机器。该方法将细胞溶质提取物的多重色谱分离与柱级分的定量无标记质谱联用。跨纯化方案的蛋白质的相对丰度将用于将共纯化蛋白质聚类成揭示蛋白质复合物组成的组。与其他大规模的蛋白质相互作用技术不同，这种新方法很容易使用现有的突变体集合进行实验验证。这种廉价的技术不需要基因置换技术，大规模基因克隆或靶向纯化策略。为了建立这种方法的有效性，研究人员将追求三个研究目标：1）预测和验证从叶胞质溶胶中分离的100种内源性蛋白质复合物的组成; 2）使用生物信息学和现有的“组学”数据集来预测和评估蛋白质复合物组成的模型; 3）确定胞质蛋白质复合物的组成是否在大豆中保守。 尽管质谱技术已被广泛应用于蛋白质组学研究，但据研究人员所知，所提出的大规模蛋白质复合物预测策略尚未完成。这项工作的成功完成将提供广泛有用的结果，数据库和试剂，将揭示内源性蛋白质复合物的组成。这种对胞质蛋白质组的深入分析将提供一个重要的数据集，使作物科学家能够进一步修改蛋白质和途径，以提高作物产量。普渡大学植物蛋白质相互作用倡议（PIPPI）将通过为本科生、研究生和博士后研究员提供研究机会等活动接触不同的受众，产生广泛的影响;并建立一个网站，为研究人员提供植物蛋白质复合物的数据。学生将获得在不同的大学研究环境中合作的技能，接触植物蛋白质组学的前沿研究，并通过协助项目管理，研究设计和数据发布来培养领导和组织能力。通过与各种校园多样性计划和办公室合作，该项目的各个方面都将涉及STEM学科中代表性不足的本科生和研究生。蛋白质组学数据集将在https://proteomecommons.org上向公众提供。预计这一新项目将有助于培养下一代植物遗传学家，他们将利用各种类型的数据来推动作物改良策略。