Zero-shot de novo peptide sequencing with open posttranslational modification discovery

peer-reviewed · Nature Biotechnology · 2026

peer-reviewed · Nature Biotechnology · 2026. Zeping Mao et al. De novo peptide sequencing directly infers sequences from mass spectrometry data without relying on protein…
Date 2026-05-19
Type peer-reviewed
Venue Nature Biotechnology
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s41587-026-03116-1
Citations (OpenAlex) 2
Venue 2-year citedness 12.89

Preprint version: Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025-06-27, Research Square)

Abstract

De novo peptide sequencing directly infers sequences from mass spectrometry data without relying on protein databases. Although recent deep learning models can also identify posttranslational modifications (PTMs), they require labeled training data for this task. Here we introduce rotary positional embedding-enhanced de novo sequencing algorithm (RNovA), a transformer-based de novo sequencing algorithm enhanced with relative positional embeddings and a reinforcement-learning-style sequential decision framework. RNovA enables open PTM discovery in a zero-shot setting-without retraining or a predefined list of candidate residues-while maintaining state-of-the-art performance on standard benchmarks. Demonstrating this capability, we successfully identified peptides modified by kynurenine-an uncommon and biologically relevant PTM-in clinical samples from patients with RA and validated this discovery with synthetically synthesized reference peptides. Furthermore, we demonstrated open de novo PTM discovery by analyzing the bacterial strain A1232E, which lacks a reference proteome, and detected an unannotated glutamic acid modification. RNovA enables exploration of previously inaccessible regions of the proteome, including peptides with unexpected or unannotated modifications.

Authors

  1. Zeping Mao · Bioinformatics Solutions Inc., University of Waterloo
  2. Chao Peng · Baizhen Biotechnologies Inc.
  3. Yuling Chen · Tsinghua University
  4. Ping Wu · Baizhen Biotechnologies Inc.
  5. Qianqiu Zhang · University of Waterloo
  6. Yonghan Yu · University of Waterloo
  7. Ruixue Zhang · University of Waterloo
  8. Lei Xin · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., Western University
  9. Baozhen Shan · Bioinformatics Solutions (Canada), Bioinformatics Solutions Inc., Western University
  10. Haiteng Deng · Tsinghua University, Yangtze Delta Region Institute of Tsinghua University
  11. Ming Li · Bioinformatics Solutions Inc., Central China Institute of Artificial Intelligence, Peng Cheng Laboratory, University of Waterloo, University of Western Ontario

Methods and tools

  • RNovA: Zero-shot open PTM discovery

Data deposited

  • Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (as deposited) · PXD076296

Data used

  • A quantitative and site-specific atlas of the PADI4-induced citrullinome reveals widespread existence of citrullination (as deposited) · PXD038702
  • CHPP chr 1,8,20 proteome dataset using the HCC cell lines of Hep3B and MHCC97H, instrument is Q Exactive, part 2 of 3 (as deposited) · PXD000533
  • CHPP chr 1,8,20 proteome dataset using the HCC cell lines of MHCC97H and HCCLM3, instrument is Q Exactive, part 1 of 3 (as deposited) · PXD000529
  • ProteomeTools (21-PTM subset) · InstaDeepAI/PXD009449, PXD009449

Cites (10)

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