Accurate and ultra-fast de novo HLA-I immunopeptide sequencing with FoxNovo

preprint · LangTaoSha (LTS) Preprint · 2026

preprint · LangTaoSha (LTS) Preprint · 2026. Ze-Xuan Chen et al. We present FoxNovo, a hybrid deep learning-combinatorial framework for de novo sequencing of immunopeptides…
Date 2026-08-03
Type preprint
Venue LangTaoSha (LTS) Preprint
Publisher LangTaoSha Preprint Server
Contribution algorithm
DOI 10.65215/LTSpreprints.2026.08.02.000299
Citations (OpenAlex) 0

Abstract

We present FoxNovo, a hybrid deep learning-combinatorial framework for de novo sequencing of immunopeptides trained on a large-scale HLA-I immunopeptidomics dataset assembled and reprocessed from public mass spectrometry (MS) repositories. This integration achieved >90% peptide accuracy on the reported benchmarks while enabling repository-scale analysis at ~2,800 spectra per second—more than 100-fold faster than the evaluated beam-search baseline under the reported benchmark conditions. To mimic the heterogeneous spectral quality encountered in experimental MS analyses, we constructed controlled peak-removal stress tests, in which FoxNovo retained higher accuracy than the evaluated methods at different simulation levels. We subsequently re-analyzed 168 million spectra from all collected 4,423 MS raw files in only 18 hours on a single GPU, equivalent to ~245 raw files per hour. This repository-scale application yielded score-filtered canonical and putative ncORF-mapped peptide predictions and recovered 41 of 42 non-canonical HLA-I peptides previously validated by targeted MS. FoxNovo demonstrates the potential of integrating AI with combinatorial decoding for scalable immunopeptidomics. The source code is available at https://github.com/fennomix/fennomix.novo.

Authors

  1. Ze-Xuan Chen · Westlake University, Zhejiang University
  2. Chang-Rong You · Westlake University, Zhejiang University
  3. Ching Tarn · Westlake University
  4. Xie-Xuan Zhou · Westlake University
  5. Wen-Feng Zeng · Chinese Academy of Sciences, University of Chinese Academy of Sciences, Westlake University

Methods and tools

  • FoxNovo: Non-autoregressive de novo sequencer specialised for HLA-I immunopeptides. Uses dual-token m/z encoding (integer + decimal vocabularies, ~4k tokens instead of ~3M fine-grained bins) in the spectrum encoder, then dynamic-programming top-K decoding over the NAR probability matrix to enforce exact precursor-mass constraints. Reaches >90% peptide accuracy at ~2,800 spectra/s, over 100x faster than the beam-search baseline, and was used to re-analyse 168M spectra from 4,423 raw files in 18 h on one GPU, recovering 41 of 42 targeted-MS-validated non-canonical peptides.

Seen in the charts

Back to the full map

Back to top