Predicting intensity ranks of peptide fragment ions.
Predicting intensity ranks of peptide fragment ions.
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DOI：
10.1021/pr800677f
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发表时间：
2009-05
影响因子：
4.4
通讯作者：
Frank AM
中科院分区：
文献类型：
--
作者：
Frank AM
肽片段的精确建模对于肽谱匹配的鲁棒评分函数的开发是必要的，肽谱匹配是基于MS/MS的识别算法的基石。不幸的是，肽片段化是一个复杂的过程，可能涉及几个相互竞争的化学途径，这使得很难开发出准确描述它的生成概率模型。然而，现在生成的大量MS/MS数据使得使用数据驱动的机器学习方法来开发基于判别性排名的模型成为可能，这些模型可以预测肽片段离子的强度排名。我们使用简单的基于序列的特征，通过提升算法组合到模型中，以高精度进行峰值排名预测。在随附的手稿中，我们演示了如何使用这些预测模型来显着提高肽识别算法的性能。在没有足够的实验数据来指导峰选择过程的情况下，该模型也可以用于最佳MRM转换的设计。预测算法也可以通过PepNovo+独立运行，可从http://bix.ucsd.edu/Software/PepNovo.html下载。
Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.
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