Novel Quantitative Proteomic Methods to Discover and Localize Endogenous Protein Complexes

Pi：Daniel Szymanski(普渡大学)Copis：Mark Hall，Daisuke Kihara，Jun Xie(普渡大学)对系统级数据集的强烈需求，使基础知识能够更有效地转化为改良的作物性状。有关蛋白质复合体组成的信息就是这样一个例子，但作物资源匮乏。本项目提出了一种基于质谱学的新方法来解决大豆内源蛋白质复合体的问题。研究结果将作为一种主要作物的反向遗传分析的假设生成机器。该方法将胞浆提取物的多重层析分离与柱级分的定量无标记质谱分析结合在一起。整个纯化方案中蛋白质的相对丰度将被用来将共同纯化的蛋白质聚集到揭示蛋白质复杂组成的组中。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集合进行实验验证。这种廉价的技术不需要基因替换技术、大规模的基因克隆或有针对性的纯化策略。为了确定这种方法的有效性，研究人员将追求三个研究目标：1)预测和验证从叶片细胞质中分离出的100个内源蛋白质复合体的组成；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体组成的模型；以及3)确定胞质蛋白质复合体的组成是否在大豆中保守。虽然质谱学在蛋白质组学研究中已经得到了广泛的应用，但据研究人员所知，大规模蛋白质复合体预测的策略还没有提出。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，揭示内源性蛋白质复合体的组成。这种对细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。普渡大学植物蛋白相互作用倡议(PIPPI)将产生广泛的影响，通过本科生和研究生以及博士后研究员的研究机会等活动接触到不同的受众；以及一个为研究人员提供植物蛋白质复合体数据的网站。学生将获得在不同的大学研究环境中合作的技能，接触到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力和组织能力。通过与各种校园多元化计划和办公室合作，该项目的所有方面都将涉及STEM学科中代表性不足的本科生和研究生。蛋白质组数据集将在https://proteomecommons.org.向公众开放预计这个新颖的项目将有助于培养下一代植物遗传学家，他们利用不同类型的数据来推动作物改良战略。

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.