InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics
peer-reviewed · Nature Communications · 2026
| Date | 2026-07-10 |
| Type | peer-reviewed |
| Venue | Nature Communications |
| Publisher | Springer Nature |
| Contribution | algorithm |
| DOI | 10.1038/s41467-026-75138-x |
| Citations (OpenAlex) | 5 |
| Venue 2-year citedness | 15.88 |
Preprint version: InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics (2025-05-14, bioRxiv)
Abstract
Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the model’s capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.
Methods and tools
- InstaNovo-P: Phosphoproteomics
Cites (6)
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- De novo peptide sequencing rescoring and FDR estimation with Winnow (2025) crossref
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) crossref
- De novo peptide sequencing by deep learning (2017) crossref