DePS：一种改进的用于从头肽测序的深度学习模型
DePS: An improved deep learning model for de novo peptide sequencing
摘要：
基于质谱数据的肽段从头测序是蛋白质鉴定的重要方法。最近，各种深度学习方法被应用于从头肽测序，DeepNovoV2是代表性模型之一。在这项研究中，我们提出了一个增强的模型，DePS，它可以提高从头肽测序的准确性，即使在串联质谱数据中丢失的信号峰或大量的噪声峰。结果表明，对于DeepNovoV2的相同测试集，DePS模型在氨基酸召回率、氨基酸精确率和肽召回率方面分别获得了74.22%、74.21%和41.68%的优异结果。此外，结果表明DePS在跨物种数据集上的表现优于DeepNovoV2。
英文摘要：
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.
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