MS2PIP: a tool for MS/MS peak intensity prediction
MS2PIP: a tool for MS/MS peak intensity prediction
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DOI：
10.1093/bioinformatics/btt544
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发表时间：
2013-12-15
期刊：
影响因子：
5.8
通讯作者：
Martens, Lennart
中科院分区：
文献类型：
--
作者：
Degroeve, Sven;Martens, Lennart
动机：串联质谱提供了将质谱信号观察结果与生成它们的化学实体相匹配的方法。该技术产生的信号光谱包含有关肽化学解离模式的信息，该肽使用碰撞诱导解离等方法被迫片段化。预测这些 MS 2 信号并了解这种碎片过程的能力对于敏感的高通量蛋白质组学研究非常重要。结果：我们提出了一种称为 (MSPIP)-P-2 的新工具，用于预测肽序列中最重要的碎片离子信号峰的强度。 (MSPIP)-P-2 使用随机森林回归学习算法对具有可信肽与谱匹配的大型数据集进行预处理，以促进数据驱动的模型归纳。在几个独立的评估集上对 (MSPIP)-P-2 的强度预测进行了评估，发现与当前最先进的 PeptideART 工具相比，其与观察到的碎片离子强度的相关性明显更好。
Motivation: Tandem mass spectrometry provides the means tomatch mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS 2 signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research.Results: We present a new tool called (MSPIP)-P-2 for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. (MSPIP)-P-2 pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of (MSPIP)-P-2 were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool.