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

Author

Listed:
  • Xiang Zhang

    (Shanghai Artificial Intelligence Laboratory
    University of British Columbia)

  • Tianze Ling

    (Tsinghua University
    Beijing Institute of Lifeomics)

  • Zhi Jin

    (Shanghai Artificial Intelligence Laboratory)

  • Sheng Xu

    (Shanghai Artificial Intelligence Laboratory
    Fudan University)

  • Zhiqiang Gao

    (Shanghai Artificial Intelligence Laboratory)

  • Boyan Sun

    (Beijing Institute of Lifeomics)

  • Zijie Qiu

    (Shanghai Artificial Intelligence Laboratory
    Fudan University)

  • Jiaqi Wei

    (Shanghai Artificial Intelligence Laboratory
    Zhejiang University)

  • Nanqing Dong

    (Shanghai Artificial Intelligence Laboratory)

  • Guangshuai Wang

    (Shanghai Artificial Intelligence Laboratory
    Fudan University)

  • Guibin Wang

    (Beijing Institute of Lifeomics)

  • Leyuan Li

    (Beijing Institute of Lifeomics)

  • Muhammad Abdul-Mageed

    (University of British Columbia
    MBZUAI)

  • Laks V. S. Lakshmanan

    (University of British Columbia)

  • Fuchu He

    (Beijing Institute of Lifeomics
    International Academy of Phronesis Medicine (Guangdong))

  • Wanli Ouyang

    (Shanghai Artificial Intelligence Laboratory)

  • Cheng Chang

    (Beijing Institute of Lifeomics)

  • Siqi Sun

    (Fudan University)

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.

Suggested Citation

  • Xiang Zhang & Tianze Ling & Zhi Jin & Sheng Xu & Zhiqiang Gao & Boyan Sun & Zijie Qiu & Jiaqi Wei & Nanqing Dong & Guangshuai Wang & Guibin Wang & Leyuan Li & Muhammad Abdul-Mageed & Laks V. S. Lakshm, 2025. "π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55021-3
    DOI: 10.1038/s41467-024-55021-3
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    References listed on IDEAS

    as
    1. Daniela Klaproth-Andrade & Johannes Hingerl & Yanik Bruns & Nicholas H. Smith & Jakob Träuble & Mathias Wilhelm & Julien Gagneur, 2024. "Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Kaiyuan Liu & Yuzhen Ye & Sujun Li & Haixu Tang, 2023. "Accurate de novo peptide sequencing using fully convolutional neural networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Sangtae Kim & Pavel A. Pevzner, 2014. "MS-GF+ makes progress towards a universal database search tool for proteomics," Nature Communications, Nature, vol. 5(1), pages 1-10, December.
    4. Ying Jiang & Aihua Sun & Yang Zhao & Wantao Ying & Huichuan Sun & Xinrong Yang & Baocai Xing & Wei Sun & Liangliang Ren & Bo Hu & Chaoying Li & Li Zhang & Guangrong Qin & Menghuan Zhang & Ning Chen & , 2019. "Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma," Nature, Nature, vol. 567(7747), pages 257-261, March.
    5. Ruedi Aebersold & Matthias Mann, 2003. "Mass spectrometry-based proteomics," Nature, Nature, vol. 422(6928), pages 198-207, March.
    6. Melih Yilmaz & William E. Fondrie & Wout Bittremieux & Carlo F. Melendez & Rowan Nelson & Varun Ananth & Sewoong Oh & William Stafford Noble, 2024. "Sequence-to-sequence translation from mass spectra to peptides with a transformer model," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
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