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Dataset Card: Astral dataset PXD055983 for InstaNovo-P
Summary
This dataset was used to asses the ability of InstaNovo-P to generalize to unseen detector types.
To this end, we reprocessed the PXD055983 dataset acquired on an Orbitrap Astral instrument using the Astral detector with the same database search workflow as described in the InstaNovo-P paper.
Original data source:
| Field | Value |
|---|---|
| Title | Differential protein expression in new1 knock-out yeast |
| Description | A label-free quantitative proteomics experiment was performed to study the impact of new1 knock-out on differential protein expression in S. cerevisiae (baker’s yeast). |
| HostingRepository | PRIDE |
| AnnounceDate | 2026-02-04 |
| AnnouncementXML | Submission_2026-02-04_01:43:28.657.xml |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Jia-Xuan Chen |
| SpeciesList | scientific name: Saccharomyces cerevisiae (Baker's yeast); NCBI TaxID: NEWT:4932; |
| ModificationList | iodoacetamide derivatized residue |
| Instrument | Orbitrap Astral |
| URL | http://central.proteomexchange.org/cgi/GetDataset?ID=PXD055983 |
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 = { PXD055983 (Revision 953a031) },
year = 2026,
url = { https://huggingface.co/datasets/InstaDeepAI/PXD055983 },
doi = { 10.57967/hf/7816 },
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}
}