  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.
