生物信息学
磷酸蛋白质组学
化学
质谱
串联质谱法
质谱法
磷酸化
鉴定(生物学)
生物系统
计算机科学
计算生物学
人工智能
色谱法
蛋白质磷酸化
生物化学
生物
植物
蛋白激酶A
基因
作者
Yi Yang,Péter Horvatovich,Liang Qiao
标识
DOI:10.1021/acs.jproteome.0c00580
摘要
Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been the most widely used technology for phosphoproteomics studies. As an alternative to database searching and probability-based phosphorylation site localization approaches, spectral library searching has been proved to be effective in the identification of phosphopeptides. However, incompletion of experimental spectral libraries limits the identification capability. Herein, we utilize MS/MS spectrum prediction coupled with spectral matching for site localization of phosphopeptides. In silico MS/MS spectra are generated from peptide sequences by deep learning/machine learning models trained with nonphosphopeptides. Then, mass shift according to phosphorylation sites, phosphoric acid neutral loss, and a "budding" strategy are adopted to adjust the in silico mass spectra. In silico MS/MS spectra can also be generated in one step for phosphopeptides using models trained with phosphopeptides. The method is benchmarked on data sets of synthetic phosphopeptides and is used to process real biological samples. It is demonstrated to be a method requiring only computational resources that supplements the probability-based approaches for phosphorylation site localization of singly and multiply phosphorylated peptides.
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