InstaDeepAI/ms_proteometools

Dataset Card for High-Confidence ProteomeTools

Dataset used to train, validate and test InstaNovo and InstaNovo+.

Dataset Summary

This dataset consists of the highest-confidence peptide-spectral matches from three parts of the ProteomeTools datasets. The original datasets may be found in the PRIDE repository with identifiers:

• PXD004732 (Part I)
• PXD010595 (Part II)
• PXD021013 (Part III)

The dataset has been split on unique peptides with the following ratio:

• 80% train
• 10% validation
• 10% test

Dataset Structure

The dataset is tabular, where each row corresponds to a labelled MS2 spectra.

• sequence (string)
  The target peptide sequence excluding post-translational modifications
• modified_sequence (string)
  The target peptide sequence including post-translational modifications
• precursor_mz (float64)
  The mass-to-charge of the precursor (from MS1)
• charge (int64)
  The charge of the precursor (from MS1)
• mz_array (list[float64])
  The mass-to-charge values of the MS2 spectrum
• mz_array (list[float32])
  The intensity values of the MS2 spectrum

MaxQuant additional columns:

• experiment_name (string)
• evidence_index (in64)
• scan_number (in64)
• precursor_recalibrated_mz (float64)

Citation Information

If you use this dataset, please cite the original authors. The original ProteomeTools data is available on PRIDE with identifiers PXD004732 (Part I), PXD010595 (Part II), and PXD021013 (Part III).

Please also cite InstaNovo:

@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}
}