dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

preprint · arXiv · 2026

preprint · arXiv · 2026. Alfred Nilsson et al. We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised…
Date 2026-10-05
Type preprint
Venue arXiv
Publisher arXiv
Contribution algorithm
DOI 10.48550/arXiv.2610.06282
Citations (OpenAlex) 0

Abstract

We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo sequencing of peptides. This invariance exploits the physical relationship between precursor properties (mass and charge) and fragment-ion evidence, without requiring peptide sequence labels. We introduce dIon, which adapts the DINO framework with two latent prediction tasks, both recovering a clean teacher representation: one from a spectrum mixture, using the precursor as a selection query, and one from a partial spectrum with the precursor withheld. The first associates precursor information with fragment-ion evidence; the second prevents representational collapse onto that information alone. Mechanistic probes support both effects, and ablations show that the full objective performs best. Under identical end-to-end training, dIon initialization improves de novo peptide precision over training from scratch by 5.5 and 8.4 percentage points on the held-out MassIVE-KB and Kingdoms test sets, and by 2.3 and 4.8 percentage points with a larger supervised training corpus. The resulting models surpass fully supervised state-of-the-art de novo sequencing models on the diverse, multi-species Kingdoms corpus under the same greedy-decoding protocol. Without peptide labels, dIon learns strong native peptide-similarity geometry compared with other learned models; with limited peptide-supervised adaptation, it achieves the best retrieval and pair-discrimination performance across all representation benchmarks.

Authors

  1. Alfred Nilsson · KTH Royal Institute of Technology
  2. Joel Lapin · Technical University of Munich
  3. Samuel H. Payne · Brigham Young University
  4. Mathias Wilhelm · Technical University of Munich
  5. Lukas Käll · KTH Royal Institute of Technology

Methods and tools

  • dIon: Self-supervised DINO-style pretraining of a spectrum encoder, from a precursor-conditioned spectrum mixture and a partial spectrum with the precursor withheld; initialising a Casanovo-style de novo sequencer from it raises peptide precision over training from scratch.

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