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InstaDeepAI
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InstaNovo-P

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metadata 
license: other
license_name: embl-ebi-terms-of-use
license_link: https://www.ebi.ac.uk/about/terms-of-use/
configs:
  - config_name: default
    data_files:
      - split: train
        path: dataset-phospho-train-*.parquet
      - split: validation
        path: dataset-phospho-valid-*.parquet
      - split: test
        path: dataset-phospho-test-*.parquet
dataset_info:
  features:
    - name: sequence
      dtype: string
    - name: precursor_charge
      dtype: int64
    - name: precursor_mz
      dtype: float64
    - name: mz_array
      sequence: float64
    - name: intensity_array
      sequence: float64
    - name: experiment_name
      dtype: string
tags:
  - biology
size_categories:
  - 1M<n<10M

Dataset Card for InstaNovo-P finetuning data

The dataset used for fine tuning InstaNovo-P is comprised of a collection of reprocessed PRIDE projects in Scop3P. (For a list of the projects, see Dataset Sources).

Dataset Details

Dataset Description

The dataset originally contains 4,053,346 PSMs. To only fine-tune on high confidence PSMs, the dataset is filtered at a confidence threshold of 0.80, reducing it to 2,760,939 PSMs, representing 74,686 unique peptide sequences. Most of the data is of human origin, except for PXD005366 and PXD000218, which contain a mix of human and mouse. All PSMs that were used to train the model contained at least one phosphorylated site, while 169, 114 PSMs ( 6%) contained oxidated methionine.

Dataset Structure

To partition the fine-tuning dataset into training, validation and test, GraphPart, an algorithm for homology partitioning, was applied on the set of unique peptide sequences. GraphPart was set to use MMseqs2 with a partitioning threshold of 0.8 and a train-validation-test ratio of 0.7/0.1/0.2 . Of the 74,686 unique sequences, 390 were removed by GraphPart, reducing the total number of PSMs to 2,691,117 in a 2,008,923/232,641/449,553-split, although during training, a random subset of only 2% of the validation set was used in order to reduce computation.

Dataset Sources

PRIDE Accession codes used for training, validation and test sets:

  1. Peng, X. et al. Identification of missing proteins in the phosphoproteome of kidney cancer. J. Proteome Res. 16, 4364–4373, DOI: 10.1021/acs.jproteome.7b00332 (2017).
  2. Post, H. et al. Robust, sensitive, and automated phosphopeptide enrichment optimized for low sample amounts applied to primary hippocampal neurons. J. Proteome Res. 16, 728–737, DOI: 10.1021/acs.jproteome.6b00753 (2016).
  3. Tsiatsiani, L. et al. Opposite electron-transfer dissociation and higher-energy collisional dissociation fragmentation characteristics of proteolytic k/r(x)n and (x)nk/r peptides provide benefits for peptide sequencing in proteomics and phosphoproteomics. J. Proteome Res. 16, 852–861, DOI: 10.1021/acs.jproteome.6b00825 (2016).
  4. Espadas, G., Borràs, E., Chiva, C. & Sabidó, E. Evaluation of different peptide fragmentation types and mass analyzers in data-dependent methods using an orbitrap fusion lumos tribrid mass spectrometer. PROTEOMICS 17, DOI: 10.1002/pmic. 201600416 (2017).
  5. Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599.e4, DOI: 10.1016/j.cels.2017.05.009 (2017).
  6. Tran, T. T., Strozynski, M. & Thiede, B. Quantitative phosphoproteome analysis of cisplatin-induced apoptosis in jurkat t cells. PROTEOMICS 17, DOI: 10.1002/pmic.201600470 (2017).
  7. Liu, Z., Wang, F., Chen, J., Zhou, Y. & Zou, H. Modulating the selectivity of affinity absorbents to multi-phosphopeptides by a competitive substitution strategy. J. Chromatogr. A 1461, 35–41, DOI: 10.1016/j.chroma.2016.07.042 (2016).
  8. Humphrey, E. S. et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling. Mol. Cell. Proteomics 15, 2671–2685 (2016).
  9. Picariello, G. et al. Antibody-independent identification of bovine milk-derived peptides in breast-milk. Food Funct. 7, 3402–3409 (2016).
  10. Francavilla, C. et al. Phosphoproteomics of primary cells reveals druggable kinase signatures in ovarian cancer. Cell Reports 18, 3242–3256, DOI: 10.1016/j.celrep.2017.03.015 (2017).
  11. Lyon, S. M. et al. A method for whole protein isolation from human cranial bone. Anal. Biochem. 515, 33–39, DOI: 10.1016/j.ab.2016.09.021 (2016).
  12. Nguyen, E. V. et al. Hyper-phosphorylation of sequestosome-1 distinguishes resistance to cisplatin in patient derived high grade serous ovarian cancer cells. Mol. amp; Cell. Proteomics 16, 1377–1392, DOI: 10.1074/mcp.m116.058321 (2017).
  13. Drake, J. M. et al. Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166, 1041–1054, DOI: 10.1016/j.cell.2016.07.007 (2016).
  14. Su, N. et al. Special enrichment strategies greatly increase the efficiency of missing proteins identification from regular proteome samples. J. Proteome Res. 14, 3680–3692 (2015).
  15. Creedon, H. et al. Identification of novel pathways linking epithelial-to-mesenchymal transition with resistance to her2-targeted therapy. Oncotarget 7, 11539–11552, DOI: 10.18632/oncotarget.7317 (2016).
  16. van der Mijn, J. C. et al. Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics. J. Proteomics 127, 259–263, DOI: 10.1016/j.jprot.2015.04.006 (2015).
  17. Piersma, S. R. et al. Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer cell lines. J. Proteomics 127, 247–258, DOI: 10.1016/j.jprot.2015.03.019 (2015).
  18. Ruprecht, B. et al. Comprehensive and reproducible phosphopeptide enrichment using iron immobilized metal ion affinity chromatography (fe-imac) columns. Mol. amp; Cell. Proteomics 14, 205–215, DOI: 10.1074/mcp.m114.043109 (2015).
  19. Kauko, O. et al. Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic ras and cip2a signaling. Sci. Reports 5, DOI: 10.1038/srep13099 (2015).
  20. Alpert, A. J., Hudecz, O. & Mechtler, K. Anion-exchange chromatography of phosphopeptides: weak anion exchange versus strong anion exchange and anion-exchange chromatography versus electrostatic repulsion-hydrophilic interaction chromatography. Anal. Chem. 87, 4704–4711 (2015).
  21. Suni, V., Imanishi, S. Y., Maiolica, A., Aebersold, R. & Corthals, G. L. Confident site localization using a simulated phosphopeptide spectral library. J. Proteome Res. 14, 2348–2359 (2015). 17/25
  22. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of tyr and ser/thr-based signaling. Cell Reports 8, 1583–1594, DOI: 10.1016/j.celrep.2014.07.036 (2014).
  23. Tong, J. et al. Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in acute myeloid leukemia. PROTEOMICS 17, DOI: 10.1002/pmic.201600361 (2017).
  24. Bauer, M. et al. Evaluation of data-dependent and -independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites. J. Proteome Res. 13, 5973–5988 (2014).
  25. Shevchuk, O. et al. HOPE-fixation of lung tissue allows retrospective proteome and phosphoproteome studies. J. Proteome Res. 13, 5230–5239 (2014).
  26. Publication pending
  27. Molden, R. C., Goya, J., Khan, Z. & Garcia, B. A. Stable isotope labeling of phosphoproteins for large-scale phosphorylation rate determination. Mol. amp; Cell. Proteomics 13, 1106–1118, DOI: 10.1074/mcp.o113.036145 (2014).
  28. Rajeeve, V., Vendrell, I., Wilkes, E., Torbett, N. & Cutillas, P. R. Cross-species proteomics reveals specific modulation of signaling in cancer and stromal cells by phosphoinositide 3-kinase (PI3K) inhibitors. Mol. Cell. Proteomics 13, 1457–1470 (2014).