π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

peer-reviewed · Nature Communications · 2025

peer-reviewed · Nature Communications · 2025. Xiang Zhang (Shanghai AI Lab) et al. Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional…
Date 2025-01-02
Type peer-reviewed
Venue Nature Communications
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s41467-024-55021-3
Citations (OpenAlex) 31
Venue 2-year citedness 17.60

Preprint version: π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024-05-17, bioRxiv)

Abstract

Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning models often rely on autoregressive generation, which suffers from error accumulation and slow inference speeds. In this work, we introduce π-PrimeNovo, a non-autoregressive Transformer-based model for peptide sequencing. With our architecture design and a CUDA-enhanced decoding module for precise mass control, π-PrimeNovo achieves significantly higher accuracy and up to 89x faster inference than state-of-the-art methods, making it ideal for large-scale applications like metaproteomics. Additionally, it excels in phosphopeptide mining and detecting low-abundance post-translational modifications (PTMs), marking a substantial advance in peptide sequencing with broad potential in biological research.

Authors

  1. Xiang Zhang (Shanghai AI Lab) · Fudan University, Shanghai Artificial Intelligence Laboratory, University of British Columbia
  2. Tianze Ling · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics, Tsinghua University
  3. Zhi Jin · Shanghai Artificial Intelligence Laboratory, Soochow University
  4. Sheng Xu · Fudan University, Shanghai Artificial Intelligence Laboratory
  5. Zhiqiang Gao · Shanghai Artificial Intelligence Laboratory
  6. Boyan Sun · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics
  7. Zijie Qiu · Fudan University, Shanghai Artificial Intelligence Laboratory
  8. Jiaqi Wei · Shanghai Artificial Intelligence Laboratory, Zhejiang University
  9. Nanqing Dong · Shanghai Artificial Intelligence Laboratory
  10. Guangshuai Wang · Shanghai Artificial Intelligence Laboratory
  11. Guibin Wang · Beijing Institute of Lifeomics
  12. Leyuan Li · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics
  13. Muhammad Abdul-Mageed · Mohamed bin Zayed University of Artificial Intelligence, University of British Columbia
  14. Laks V.S. Lakshmanan · University of British Columbia
  15. Fuchu He · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), National Center for Protein Sciences (Beijing), State Key Laboratory of Medical Proteomics
  16. Wanli Ouyang · Shanghai Artificial Intelligence Laboratory
  17. Cheng Chang · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), National Center for Protein Sciences (Beijing), State Key Laboratory of Medical Proteomics
  18. Siqi Sun · Fudan University, Shanghai Artificial Intelligence Laboratory

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