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π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing
Zhang, Xiang Ling, Tianze Jin, Zhi Xu, Sheng Gao, Zhiqiang Sun, Boyan Qiu, Zijie Wei, Jiaqi Dong, Nanqing Wang, Guangshuai
Zhang, Xiang
Ling, Tianze
Jin, Zhi
Xu, Sheng
Gao, Zhiqiang
Sun, Boyan
Qiu, Zijie
Wei, Jiaqi
Dong, Nanqing
Wang, Guangshuai
Supervisor
Department
Natural Language Processing
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Type
Journal article
Date
2025
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Language
English
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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 ?-PrimeNovo, a non-autoregressive Transformer-based model for peptide sequencing. With our architecture design and a CUDA-enhanced decoding module for precise mass control, ?-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.
Citation
X. Zhang et al., “π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing,” Nature Communications 2024 16:1, vol. 16, no. 1, pp. 1–16, Jan. 2025, doi: 10.1038/s41467-024-55021-3.
Source
Nature Communications
Conference
Keywords
Peptide sequencing, Non-autoregressive Transformer, Deep learning, Mass spectrometry, CUDA-enhanced decoding
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Publisher
Springer Nature