Automatic validation of phosphopeptide identifications from tandem mass spectra
Automatic validation of phosphopeptide identifications from tandem mass spectra
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DOI：
10.1021/ac061334v
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发表时间：
2007-02-15
影响因子：
7.4
通讯作者：
Yates, John, III
中科院分区：
文献类型：
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作者：
Lu, Bingwen;Ruse, Cristian;Yates, John, III
我们开发和比较了两种方法的自动验证磷酸肽串联质谱确定使用数据库搜索算法。通过SEQUEST搜索附加有其诱饵（反向序列）的蛋白质数据库获得磷酸肽鉴定。采用统计评估和迭代搜索来创建高质量的磷酸肽数据集。搜索后验证的自动化是通过两种不同的策略来实现的。通过使用统计多重检验，我们计算每个试验性肽磷酸化的p值。在第二种方法中，我们使用支持向量机（SVM;机器学习算法）二元分类器来预测试探性肽磷酸化是否为真。我们表现出良好的协议（85%）之间的搜索后验证磷酸肽/光谱匹配的多重测试和支持向量机。在盲法试验中，自动方法与人工专家验证非常一致。此外，该算法进行了测试的合成磷酸肽的鉴定。我们表明，在串联质谱中的磷酸盐中性损失可以用来评估磷酸肽/光谱匹配的正确性。根据所使用的搜索算法，具有径向基函数的SVM分类器提供了从95.7%到96.8%的阳性数据集的分类准确度。建立一个鉴定的效力是一个必要的步骤，进一步搜索后查询的光谱完整定位的磷酸化位点。我们目前的实现执行验证磷酸丝氨酸/磷酸苏氨酸含有肽具有一个或两个磷酸化位点从离子阱质谱仪上收集的数据。基于SVM的算法已在软件包DeBunker中实现。我们说明了基于SVM的软件DeBunker在一个大型磷酸化数据集上的应用。
We developed and compared two approaches for automated validation of phosphopeptide tandem mass spectra identified using database searching algorithms. Phosphopeptide identifications were obtained through SEQUEST searches of a protein database appended with its decoy (reversed sequences). Statistical evaluation and iterative searches were employed to create a high-quality data set of phosphopeptides. Automation of postsearch validation was approached by two different strategies. By using statistical multiple testing, we calculate a p value for each tentative peptide phosphorylation. In a second method, we use a support vector machine (SVM; a machine learning algorithm) binary classifier to predict whether a tentative peptide phosphorylation is true. We show good agreement (85%) between postsearch validation of phosphopeptide/spectrum matches by multiple testing and that from support vector machines. Automatic methods conform very well with manual expert validation in a blinded test. Additionally, the algorithms were tested on the identification of synthetic phosphopeptides. We show that phosphate neutral losses in tandem mass spectra can be used to assess the correctness of phosphopeptide/spectrum matches. An SVM classifier with a radial basis function provided classification accuracy from 95.7% to 96.8% of the positive data set, depending on search algorithm used. Establishing the efficacy of an identification is a necessary step for further postsearch interrogation of the spectra for complete localization of phosphorylation sites. Our current implementation performs validation of phosphoserine/phosphothreonine-containing peptides having one or two phosphorylation sites from data gathered on an ion trap mass spectrometer. The SVM-based algorithm has been implemented in the software package DeBunker. We illustrate the application of the SVM-based software DeBunker on a large phosphorylation data set.