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

preprint · arXiv · 2022

preprint · arXiv · 2022. Cheng Ge et al. De novo peptide sequencing from mass spectrometry data is an important method for protein identification…
Date 2022-03-16
Type preprint
Venue arXiv
Publisher arXiv
Contribution algorithm
DOI 10.48550/arXiv.2203.08820
Citations (OpenAlex) 3

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.

Authors

  1. Cheng Ge · Jiangsu University of Technology, Ocean University of China
  2. Yi Lu · Jiangsu University of Technology
  3. Jia Qu · Changzhou University
  4. Liangxu Xie · Jiangsu University of Technology
  5. Feng Wang · Changzhou University
  6. Hong Zhang · China University of Mining and Technology
  7. Ren Kong · Jiangsu University of Technology
  8. Shan Chang · Jiangsu University of Technology

Methods and tools

  • DePS: Improved deep learning model

Cites (12)

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