metadata
dataset_info:
features:
- name: sequence
dtype: large_string
- name: modified_sequence
dtype: large_string
- name: precursor_charge
dtype: int64
- name: precursor_mz
dtype: float64
- name: mz_array
large_list: float64
- name: intensity_array
large_list: float64
- name: experiment_name
dtype: large_string
- name: spectrum_id
dtype: large_string
splits:
- name: test
num_bytes: 89580538
num_examples: 41158
download_size: 59261700
dataset_size: 89580538
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
Dataset Card: 21PTM dataset PXD009449 for InstaNovo-P
To assess the model performance of InstaNovo-P on phosphorylated peptides, we used a subset of project PXD009449 as evaluation dataset.
Original data source:
| Field | Value |
|---|---|
| Title | Systematic characterization of 21 post-translational modification using synthetic peptides |
| Description | The data presented in this study in the - context of the ProteoemTools project - is based on the synthesis of about 5000 synthetic |
| HostingRepository | PRIDE |
| AnnounceDate | 2024-10-22 |
| AnnouncementXML | Submission_2024-10-22_04:44:20.696.xml |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Daniel Zolg |
| SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
| ModificationList | monomethylated residue; 3'-nitro-L-tyrosine; N6-malonyl-L-lysine; biotinylated residue; phosphorylated residue; acetylated residue; |
| Instrument | Orbitrap Fusion Lumos |
| URL | https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD009449 |
Citation:
If you use InstaNovo-P in your research, please cite: InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics
@article {Lauridsen2025.05.14.654049,
author = {Lauridsen, Jesper and Ramasamy, Pathmanaban and Catzel, Rachel and Canbay, Vahap and Mabona, Amandla and Eloff, Kevin and Fullwood, Paul and Ferguson, Jennifer and Kirketerp-M{\o}ller, Annekatrine and Goldschmidt, Ida Sofie and Claeys, Tine and van Puyenbroeck, Sam and Lopez Carranza, Nicolas and Schoof, Erwin M. and Martens, Lennart and Van Goey, Jeroen and Francavilla, Chiara and Jenkins, Timothy Patrick and Kalogeropoulos, Konstantinos},
title = {InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics},
elocation-id = {2025.05.14.654049},
year = {2025},
doi = {10.1101/2025.05.14.654049},
publisher = {Cold Spring Harbor Laboratory},
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{\textquoteright}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.Competing Interest StatementR.C, A.M., K.E, N.L.C., and J.V.G. are employees of InstaDeep, 5 Merchant Square, London, UK. The other authors declare no competing interests.},
URL = {https://www.biorxiv.org/content/early/2025/05/18/2025.05.14.654049},
eprint = {https://www.biorxiv.org/content/early/2025/05/18/2025.05.14.654049.full.pdf},
journal = {bioRxiv}
}
If you use this dataset, please cite
@misc{instadeep_ltd_2026,
author = { InstaDeep Ltd },
title = { PXD009449 (Revision 7676f2c) },
year = 2026,
url = { https://huggingface.co/datasets/InstaDeepAI/PXD009449 },
doi = { 10.57967/hf/7818 },
publisher = { Hugging Face }
}
If you use the InstaNovo model to generate predictions, please also cite: InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments
@article{eloff_kalogeropoulos_2025_instanovo,
title = {InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale
proteomics experiments},
author = {Eloff, Kevin and Kalogeropoulos, Konstantinos and Mabona, Amandla and Morell,
Oliver and Catzel, Rachel and Rivera-de-Torre, Esperanza and Berg Jespersen,
Jakob and Williams, Wesley and van Beljouw, Sam P. B. and Skwark, Marcin J.
and Laustsen, Andreas Hougaard and Brouns, Stan J. J. and Ljungars,
Anne and Schoof, Erwin M. and Van Goey, Jeroen and auf dem Keller, Ulrich and
Beguir, Karim and Lopez Carranza, Nicolas and Jenkins, Timothy P.},
year = 2025,
month = {Mar},
day = 31,
journal = {Nature Machine Intelligence},
doi = {10.1038/s42256-025-01019-5},
issn = {2522-5839},
url = {https://doi.org/10.1038/s42256-025-01019-5}
}