TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets
TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets
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DOI：
10.1038/s41587-019-0067-5
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发表时间：
2019-04-01
影响因子：
46.9
通讯作者：
Elias, Joshua E.
中科院分区：
文献类型：
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作者：
Devabhaktuni, Arun;Lin, Sarah;Elias, Joshua E.
虽然质谱法非常适合于鉴定数千种潜在的蛋白质翻译后修饰（PTM），但它在历史上只偏向于少数几种。为了测量不同蛋白质组中的整个PTM集，软件必须克服覆盖巨大搜索空间和区分正确与不正确的光谱解释的双重挑战。在这里，我们描述了TagGraph，一个计算工具，克服了这两个挑战，一个不受限制的基于字符串的搜索方法，比现有的方法快350倍，和一个概率验证模型，我们优化了PTM分配。我们将TagGraph应用于已发表的2500万个质谱的人类蛋白质组数据集，并将其原始分析的置信度提高了两倍。我们在蛋白质组的近100万个位点上鉴定了数千种修饰类型。我们展示了高度丰富但研究不足的PTM的替代背景，如脯氨酸羟基化，及其与癌症突变的意想不到的关联。通过对PTM进行广泛的表征，TagGraph可以告知它们的功能和调节如何交叉。
Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.