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Automated Selected ReactionMonitoring Software for Accurate Label-Free Protein Quantification

机译:自动选择反应监测软件可实现准确的无标记蛋白质定量

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摘要

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5–19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.
机译:选择性反应监测(SRM)是一种质谱分析方法,具有使用标记的参考肽准确,可重复地定量蛋白质的能力。然而,如果靶向大量肽并且当选择肽时需要高柔韧性时,使用标记的参考肽变得不切实际。我们开发了无标签定量SRM工作流程,该流程依赖于新的自动算法Anubis进行准确的峰检测。 Anubis有效地从污染肽中去除干扰信号,以估算目标肽的真实信号。我们在已发布的多站点数据集上评估了该算法,并获得了与手动数据分析一致的结果。在化脓性链球菌整个蛋白质组消化物中的复杂肽混合物中,我们在整个蛋白质组丰度范围内(6.5-19.2%)实现了技术变异,这大大低于生物学样本的总变异。我们的结果表明,具有自动数据分析功能的无标签SRM工作流程对于大规模生物学研究是可行的,从而为定量蛋白质组学和系统生物学开辟了新的可能性。

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