Chaturvedi Palak, Doerfler Hannes, Jegadeesan Sridharan, Ghatak Arindam, Pressman Etan, Castillejo Maria Angeles, Wienkoop Stefanie, Egelhofer Volker, Firon Nurit, Weckwerth Wolfram
Department of Ecogenomics and Systems Biology, Faculty of Sciences, University of Vienna , Althanstrasse 14, A-1090 Vienna, Austria.
Department of Vegetable Research, Institute of Plant Sciences, The Volcani Centre, Agricultural Research Organization , Bet Dagan, 50250, Israel.
J Proteome Res. 2015 Nov 6;14(11):4463-71. doi: 10.1021/pr501240n. Epub 2015 Oct 21.
Recently, we have developed a quantitative shotgun proteomics strategy called mass accuracy precursor alignment (MAPA). The MAPA algorithm uses high mass accuracy to bin mass-to-charge (m/z) ratios of precursor ions from LC-MS analyses, determines their intensities, and extracts a quantitative sample versus m/z ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm that allows the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to indistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages, post-meiotic and mature. Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role in fruit setting and yield. By LC-MS-based shotgun proteomics, we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data-processing strategy, 51 unique proteins were identified as heat-treatment-responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.
最近，我们开发了一种名为质量精度前体对齐（MAPA）的定量鸟枪法蛋白质组学策略。MAPA算法利用高质量精度对液相色谱-质谱（LC-MS）分析中前体离子的质荷比（m/z）进行分箱，确定其强度，并从大量样本中提取定量样本与m/z比数据对齐矩阵。在此，我们介绍该算法的一个新特性，它允许从复杂的鸟枪法蛋白质组学数据中提取和对齐蛋白质型肽前体离子或任何其他目标肽，以准确量化独特的蛋白质。这种策略规避了典型的鸟枪法蛋白质组学方法因无法区分蛋白质异构体而导致蛋白质定量混淆问题。我们将此策略应用于减数分裂后和成熟这两个发育阶段的对照和热处理番茄花粉粒的比较。花粉是一种对温度敏感的组织，参与植物的生殖周期，在坐果和产量中起主要作用。通过基于LC-MS的鸟枪法蛋白质组学，我们总共在所有不同组织中鉴定出2000多种蛋白质。通过应用靶向MAPA数据处理策略，鉴定出51种独特的蛋白质作为热处理响应蛋白候选物。讨论了所鉴定候选物在特定发育阶段的潜在功能。
