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NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database

机译：使用Uniprot数据库对大豆种子进行NanoUPLC-MSE蛋白质组学数据评估

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Background Recombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database. Results The NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and beta-conglycinin chains. The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified. Conclusions NanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.
机译：背景技术在过去的十年中，重组DNA技术已广泛用于从转基因生物（GMO）生成各种产品，并且能够分析这些产品的技术的开发对于理解基因表达模式至关重要。液相色谱与质谱联用是分析GMO中蛋白质含量和可能的表达修饰的强大工具。具体来说，NanoUPLC-MS E 技术提供了复杂混合物的快速蛋白质分析，并提供了支持的步骤，以实现高样品通量，使用少量样品进行鉴定和定量，并具有出色的重复性。在这里，我们使用NanoUPLC-MS E 评估大豆种子EMBRAPA BR16品种的肽和蛋白质鉴定及定量，并与理论胰蛋白酶进行比较从Uniprot数据库中消化大豆序列。结果NanoUPLC-MS E 肽分析结果鉴定出3,400种肽，其中58％被鉴定为无误解。实验表明，有13％的肽进行了源内裂解，而82％的肽以小于5 ppm的质量测量精度被鉴定。超过75％的鉴定蛋白质具有至少10个匹配的肽，88％的鉴定蛋白质具有大于30％的覆盖率，而87％的鉴定蛋白质均出现在所有四个重复样本中。 78％的鉴定蛋白质对应于所有大豆球蛋白和β-伴大豆球蛋白链。理论上的Uniprot肽数据库有723,749个条目，而548,336个肽的分子量大于500 Da。种子蛋白质占蛋白质数据库条目的0.86％。在肽水平上，胰蛋白酶消化的种子蛋白仅占理论Uniprot肽数据库的0.3％。所有数据库肽中总共22％的pI值小于5，其中25％的pI值在5至8之间。根据典型的NanoUPLC-MS的检测范围， upup class =“ a-plus -plus“> E 实验，即500至5000 Da，将无法鉴定64种蛋白质。结论NanoUPLC-MS E 实验在5 ppm的肽段误差范围内提供了良好的蛋白质覆盖，分子量检测范围从500到5000 Da。根据要分析的组织或蛋白质，应使用第二种消化酶。对于种子组织，胰蛋白酶蛋白的消化结果可提供良好的数据库覆盖率。 Uniprot数据库中有许多重复的条目，使用NanoUPLC-MS E 分析时，可能会导致错误的蛋白质同源物关联。相对于其他工作中报道的转基因大豆，EMBRAPA BR-16种子的蛋白质组学特征缺少某些描述的蛋白质。

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