DePS: An improved deep learning model for de novo peptide sequencing

• URL: http://arxiv.org/abs/2203.08820v1
• Date: Wed, 16 Mar 2022 16:45:48 GMT
• Title: DePS: An improved deep learning model for de novo peptide sequencing
• Authors: Cheng Ge, Yi Lu, Jia Qu, Liangxu Xie, Feng Wang, Hong Zhang, Ren Kong and Shan Chang
• Abstract summary: In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing. 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.
• Score: 7.468176246958974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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