Limitations of de novo peptide sequencing in resolving sequence ambiguity
peer-reviewed · Nature Methods · 2026
| Date | 2026-11-03 |
| Type | peer-reviewed |
| Venue | Nature Methods |
| Publisher | Springer Science and Business Media LLC |
| Contribution | benchmark |
| DOI | 10.1038/s41592-026-03301-9 |
| Citations (OpenAlex) | 1 |
| Venue 2-year citedness | 22.29 |
Preprint version: Limitations of de novo sequencing in resolving sequence ambiguity (2025-08-19, bioRxiv)
Abstract
De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectra create ambiguity, making unambiguous identification challenging. Recent deep learning advances have produced numerous de novo models that predict sequences and refine peptide-spectrum matches under such conditions. Yet, their relative strengths, weaknesses and ability to handle spectrum ambiguity remain unclear. Here we benchmark eight state-of-the-art models on three publicly available proteomics datasets, comparing performance using established metrics and quantifying inter-model agreement. We assess post-processing approaches, including iterative refinement, rescoring and reranking, for their ability to improve identification accuracy, and perform an error analysis to identify common mispredictions and their causes. Model performance varied, with considerable overlap of correct identifications. Post-processing yielded no or only modest improvements. Most sequencing errors were model-independent and driven by limited fragment ion coverage, a limitation also observed in database searches with large search spaces.
Methods and tools
- De novo sequence-ambiguity benchmark: Benchmark across 8 leading DL de novo peptide sequencers on three proteomics datasets, showing large overlap of correct calls between models and that post-processing yields only modest gains. The shared error source is limited fragment-ion coverage, a bottleneck that database search shares as well.