Novel Quantitative Proteomic Methods to Discover and Localize Endogenous Protein Complexes

项目负责人：Daniel Szymanski（普渡大学）项目负责人：Mark Hall， Daisuke Kihara, Jun Xie（普渡大学）迫切需要系统级数据集，以便更有效地将基础知识转化为改良的作物性状。关于蛋白质复合体组成的信息就是这样一个例子，但是关于农作物的资源缺乏。本项目提出了一种基于质谱分析的大豆内源蛋白复合物的新方法。研究结果将作为一种主要作物反向遗传分析的假设生成机器。该方法将细胞质萃取物的多重色谱分离与柱组分的定量无标记质谱分离相结合。整个纯化方案中蛋白质的相对丰度将用于将共纯化蛋白质聚类成组，以揭示蛋白质复合物的组成。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替代技术、大规模基因克隆或靶向纯化策略。为了确定该方法的有效性，研究人员将追求三个研究目标：1)预测和验证从叶细胞质中分离的100个内源性蛋白质复合物的组成；2)利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)确定细胞质蛋白复合物的组成是否在甘氨酸max中保守。尽管质谱法在蛋白质组学研究中得到了广泛的应用，但据研究者所知，大规模蛋白质复合物预测的拟议策略尚未完成。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，以揭示内源性蛋白质复合物的组成。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，以提高作物生产力。普渡大学植物蛋白相互作用倡议（PIPPI）将通过为本科生、研究生和博士后提供研究机会等活动，为不同的受众提供广泛的影响；还有一个为研究人员提供植物蛋白复合物数据的网站。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培养领导和组织能力。通过与各种校园多元化项目和办公室合作，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上向公众开放。预计这个新项目将有助于培养下一代植物遗传学家，利用不同类型的数据来推动作物改良策略。

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.