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

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. Lakshmanan, Fuchu He, Wanli Ouyang (), Cheng Chang () and Siqi Sun ()
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Xiang Zhang: Shanghai Artificial Intelligence Laboratory
Tianze Ling: Tsinghua University
Zhi Jin: Shanghai Artificial Intelligence Laboratory
Sheng Xu: Shanghai Artificial Intelligence Laboratory
Zhiqiang Gao: Shanghai Artificial Intelligence Laboratory
Boyan Sun: Beijing Institute of Lifeomics
Zijie Qiu: Shanghai Artificial Intelligence Laboratory
Jiaqi Wei: Shanghai Artificial Intelligence Laboratory
Nanqing Dong: Shanghai Artificial Intelligence Laboratory
Guangshuai Wang: Shanghai Artificial Intelligence Laboratory
Guibin Wang: Beijing Institute of Lifeomics
Leyuan Li: Beijing Institute of Lifeomics
Muhammad Abdul-Mageed: University of British Columbia
Laks V. S. Lakshmanan: University of British Columbia
Fuchu He: Beijing Institute of Lifeomics
Wanli Ouyang: Shanghai Artificial Intelligence Laboratory
Cheng Chang: Beijing Institute of Lifeomics
Siqi Sun: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-55021-3

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