π-HelixNovo2: Making Accurate Online De Novo Peptide Sequencing Available to All

peer-reviewed · Genomics, Proteomics & Bioinformatics · 2026

peer-reviewed · Genomics, Proteomics & Bioinformatics · 2026. Tingpeng Yang et al. De novo peptide sequencing, the mainstream technique for identifying novel peptides, has recently seen…
Date 2026-06-25
Type peer-reviewed
Venue Genomics, Proteomics & Bioinformatics
Publisher Oxford University Press (OUP)
Contribution algorithm
DOI 10.1093/gpbjnl/qzag049
Citations (OpenAlex) 0
Venue 2-year citedness 5.77

Abstract

De novo peptide sequencing, the mainstream technique for identifying novel peptides, has recently seen remarkable improvements due to deep learning approaches. However, existing models struggle to effectively enhance the encoding of mass spectra and the decoding of amino acids, which limits their overall performance. Moreover, these models lack peptide filtering for de novo peptides, and often present challenges for users without programming expertise. Here, we propose π-HelixNovo2, a de novo peptide sequencing model that integrates complementary spectrum and bidirectional decoding within a Transformer framework. We further propose a peptide filtering strategy to identify the correct peptide-spectrum matches from the results of π-HelixNovo2. Our experiments demonstrate that π-HelixNovo2 outperforms state-of-the-art models, offering reliable performance in identifying antibody peptides, multi-enzyme cleavage peptides, non-enzymatic peptides, and analyzing the gut metaproteome. Finally, we trained π-HelixNovo2 on the large-scale MassIVE-KB dataset, and present an open, user-friendly, and online computational platform to make π-HelixNovo2 freely available to all (https://openi.pcl.ac.cn/OpenI/pi-HelixNovo-NPU).

Authors

  1. Tingpeng Yang · Peng Cheng Laboratory, Tsinghua Shenzhen International Graduate School, Tsinghua University
  2. Tianze Ling · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics, Tsinghua University
  3. Boyan Sun · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics
  4. Zhendong Liang · Peng Cheng Laboratory, Tsinghua Shenzhen International Graduate School, Tsinghua University
  5. Cheng Lai · Peng Cheng Laboratory
  6. Jiangli Hu · Peng Cheng Laboratory
  7. Zexuan Yi · Peng Cheng Laboratory
  8. Yonghong He · Peng Cheng Laboratory, Tsinghua University
  9. Leyuan Li · Beijing Institute of Lifeomics, State Key Laboratory of Medical Proteomics
  10. Yue Yu · Peng Cheng Laboratory
  11. Cheng Chang · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), National Center for Protein Sciences (Beijing), State Key Laboratory of Medical Proteomics
  12. Yu Wang · Peng Cheng Laboratory

Methods and tools

  • π-HelixNovo2: Successor to π-HelixNovo with an emphasis on availability: an online inference service alongside the model architecture refinement. Same Tsinghua / Pengcheng Lab / NCPSB collaboration as the original.

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