DLDN-Bench: A Benchmark Framework for Deep Learning de Novo Peptide Sequencing in Proteomics

preprint · bioRxiv · 2026

preprint · bioRxiv · 2026. Jannik Schneider et al. De novo peptide sequencing is an essential approach for analyzing mass spectrometry data because it enables…
Date 2026-06-10
Type preprint
Venue bioRxiv
Publisher Cold Spring Harbor Laboratory
Contribution benchmark
DOI 10.64898/2026.06.10.728383
Citations (OpenAlex) 0

Abstract

De novo peptide sequencing is an essential approach for analyzing mass spectrometry data because it enables the identification of novel peptides without relying on protein sequence databases. Recent advances in deep learning have substantially improved the performance of de novo sequencing methods, but the rapid emergence of new models has led to heterogeneous evaluation practices and limited comparability. To address this, we introduce DLDN-Bench, a benchmark framework including a set of benchmark datasets derived from human muscle biopsy mass spectrometry data retrieved from PRIDE and annotated through consensus across multiple widely used database search engines. Using these datasets, we systematically benchmark recent deep learning-based de novo sequencing tools alongside traditional approaches. Performance is assessed using established metrics, including precision and coverage relative to a pseudo-ground truth defined by cross-engine agreement. To demonstrate the utility of DLDN-Bench, we benchmark four recent deep learning models and make all results publicly available. This benchmark framework provides a standardized basis for comparing state-of-the-art methods and offers an extensible resource for evaluating future tools in de novo peptide sequencing. Code availabilityhttps://github.com/ddz-icb/DLDN-Bench Data availabilityhttps://doi.org/10.5281/zenodo.19627459

Authors

  1. Jannik Schneider · Forschungszentrum Jülich GmbH, German Diabetes Center (DDZ)
  2. Sonja Hartwig · German Center for Diabetes Research (DZD e.V.), German Diabetes Center (DDZ)
  3. Alexandra Chadt · German Center for Diabetes Research (DZD e.V.), German Diabetes Center (DDZ)
  4. Stefan Lehr · German Center for Diabetes Research (DZD e.V.), German Diabetes Center (DDZ)
  5. Hadi Al-Hasani · German Center for Diabetes Research (DZD e.V.), German Diabetes Center (DDZ)
  6. Michael Turewicz · German Center for Diabetes Research (DZD e.V.), German Diabetes Center (DDZ)

Methods and tools

  • DLDN-Bench: Benchmark framework for deep-learning de novo peptide sequencing in proteomics. Derives evaluation datasets from human muscle biopsy MS data on PRIDE annotated by cross-engine consensus, and reports precision / coverage of recent DL tools (and classical baselines) against that pseudo-ground-truth.

Cites (8)

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