DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning

preprint · bioRxiv · 2025

preprint · bioRxiv · 2025. Zixuan Cao et al. Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by…
Date 2025-03-20
Type preprint
Venue bioRxiv
Publisher Cold Spring Harbor Laboratory
Contribution algorithm
DOI 10.1101/2025.03.20.643920
Citations (OpenAlex) 2

Abstract

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases led to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieved much higher sequence coverages. DiNovo also showed great potential as a powerful complement or alternative to database search for peptide identification with quality control.Competing Interest StatementThe authors have declared no competing interest.

Authors

  1. Zixuan Cao · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  2. Xueli Peng · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  3. Di Zhang · Shandong University of Technology
  4. Piyu Zhou · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  5. Li Kang · Beijing Institute of Lifeomics, China Medical University
  6. Hao Chi · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  7. Ruitao Wu · Shandong University of Technology
  8. Zhiyuan Cheng · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  9. Yao Zhang · Beijing Institute of Lifeomics
  10. Jiaxin Dai · Beijing Institute of Lifeomics
  11. Yanchang Li · Beijing Institute of Lifeomics
  12. Lijin Yao · Shandong University of Technology
  13. Xinming Li · Shandong University of Technology
  14. Jinghan Yang · Chinese Academy of Sciences, University of Chinese Academy of Sciences
  15. Haipeng Wang · Chinese Academy of Sciences, Shandong University of Technology, University of Chinese Academy of Sciences
  16. Ping Xu · Beijing Institute of Lifeomics, China Medical University, National Center for Protein Sciences (Beijing)
  17. Yan Fu · Chinese Academy of Sciences, University of Chinese Academy of Sciences

Methods and tools

  • DiNovo: Mirror proteases + DL

Cites (21)

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