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.

中文翻译：

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片段质谱预测有助于磷酸化位点定位

液相色谱串联质谱法 (LC–MS/MS) 是磷酸化蛋白质组学研究中使用最广泛的技术。作为数据库搜索和基于概率的磷酸化位点定位方法的替代方法，谱库搜索已被证明可有效识别磷酸肽。然而，实验谱库的不完善限制了识别能力。在此，我们利用 MS/MS 谱图预测和谱图匹配对磷酸肽进行位点定位。计算机MS/MS 谱图是通过使用非磷酸肽训练的深度学习/机器学习模型从肽序列生成的。然后，根据磷酸化位点、磷酸中性损失和“出芽”策略进行质量转移来调整计算机质谱。还可以使用经过磷酸肽训练的模型一步生成磷酸肽的计算机 MS/MS 谱图。该方法以合成磷酸肽的数据集为基准，用于处理真实的生物样本。它被证明是一种只需要计算资源的方法，可以补充基于概率的单磷酸化和多磷酸化肽磷酸化位点定位方法。