论文标题
DEPS：改进的从头肽测序的深度学习模型
DePS: An improved deep learning model for de novo peptide sequencing
论文作者
论文摘要
质谱数据中的从头肽测序是蛋白质鉴定的重要方法。最近，将各种深度学习方法应用于从头肽测序，DeepNovov2是代表模型之一。在这项研究中，我们提出了一个增强的模型，该模型可以提高从头肽测序的准确性，即使信号峰缺失或串联质谱数据中的大量嘈杂峰。结果表明，对于相同的DeepNovov2测试集，DEPS模型分别为氨基酸回忆，氨基酸精度和肽召回率分别获得了74.22％，74.21％和41.68％的出色结果。此外，结果表明，DEP在跨物种数据集上的表现优于deepnovov2。
De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross species dataset.
