鸟枪磷酸化蛋白质组学能够对生物样品中的磷酸肽进行高通量分析。与该技术相关的主要挑战之一是在数据分析期间相对低的磷酸肽鉴定速率。这种限制阻碍了shot弹枪磷酸化蛋白质组学提供的潜力的充分实现。在这里,我们介绍了DeepRescore2,这是一种计算工作流程,利用基于深度学习的保留时间和碎片离子强度预测来改善磷酸肽识别和磷位点定位。使用最先进的计算工作流程作为基准,DeepRescore2在合成数据集中将正确识别的肽谱匹配的数量增加了17%,并在生物数据集中识别了19%-46%的磷酸肽。在肝癌数据集中,基于最先进的工作流程,无法识别肿瘤和正常组织之间30%的显着改变的磷酸位点以及从DeepRescore2处理的数据中识别出的60%的预后相关磷酸位点。值得注意的是,DeepRescore2处理的数据独特地将EGFR过度激活识别为预后不良的肝癌的新靶点。这是通过实验验证的。深度学习预测在DeepRescore2中的集成改善了磷酸肽的识别并促进了生物学发现。
Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.