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--- |
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license: other |
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license_name: embl-ebi-terms-of-use |
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license_link: https://www.ebi.ac.uk/about/terms-of-use/ |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: dataset-phospho-train-*.parquet |
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- split: validation |
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path: dataset-phospho-valid-*.parquet |
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- split: test |
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path: dataset-phospho-test-*.parquet |
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dataset_info: |
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features: |
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- name: sequence |
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dtype: string |
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- name: precursor_charge |
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dtype: int64 |
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- name: precursor_mz |
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dtype: float64 |
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- name: mz_array |
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sequence: float64 |
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- name: intensity_array |
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sequence: float64 |
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- name: experiment_name |
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dtype: string |
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tags: |
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- biology |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Dataset Card for InstaNovo-P finetuning data |
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The dataset used for fine tuning InstaNovo-P is comprised of a collection of reprocessed PRIDE projects in [Scop3P](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00306). (For a list of the projects, see [Dataset Sources](https://huggingface.co/datasets/InstaDeepAI/InstaNovo-P#dataset-sources)). |
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- **Curated by:** Jesper Lauridsen, Pathmanaban Ramasamy |
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- **License:** [EMBL-EBI terms of use](https://www.ebi.ac.uk/about/terms-of-use/) |
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## Dataset Details |
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### Dataset Description |
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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](https://www.ebi.ac.uk/pride/archive/projects/PXD005366) and [PXD000218](https://www.ebi.ac.uk/pride/archive/projects/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 |
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( 6%) contained oxidated methionine. |
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### Dataset Structure |
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To partition the fine-tuning dataset into training, validation and test, [GraphPart](https://academic.oup.com/nargab/article/5/4/lqad088/7318077), an algorithm for homology partitioning, was applied on the set of unique peptide sequences. GraphPart was set to use [MMseqs2](https://www.nature.com/articles/nbt.3988) 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. |
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### Dataset Sources |
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PRIDE Accession codes used for training, validation and test sets: |
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* [PXD006482](https://www.ebi.ac.uk/pride/archive/projects/PXD006482) (1) |
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* [PXD005366](https://www.ebi.ac.uk/pride/archive/projects/PXD005366) (2) |
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* [PXD004447](https://www.ebi.ac.uk/pride/archive/projects/PXD004447) (3) |
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* [PXD004940](https://www.ebi.ac.uk/pride/archive/projects/PXD004940) (4) |
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* [PXD004452](https://www.ebi.ac.uk/pride/archive/projects/PXD004452) (5) |
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* [PXD004415](https://www.ebi.ac.uk/pride/archive/projects/PXD004415) (6) |
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* [PXD004252](https://www.ebi.ac.uk/pride/archive/projects/PXD004252) (7) |
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* [PXD003198](https://www.ebi.ac.uk/pride/archive/projects/PXD003198) (8) |
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* [PXD003657](https://www.ebi.ac.uk/pride/archive/projects/PXD003657) (9) |
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* [PXD003531](https://www.ebi.ac.uk/pride/archive/projects/PXD003531) (10) |
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* [PXD003215](https://www.ebi.ac.uk/pride/archive/projects/PXD003215) (11) |
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* [PXD002394](https://www.ebi.ac.uk/pride/archive/projects/PXD002394) (12) |
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* [PXD002286](https://www.ebi.ac.uk/pride/archive/projects/PXD002286) (13) |
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* [PXD002255](https://www.ebi.ac.uk/pride/archive/projects/PXD002255) (14) |
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* [PXD002057](https://www.ebi.ac.uk/pride/archive/projects/PXD002057) (15) |
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* [PXD001565](https://www.ebi.ac.uk/pride/archive/projects/PXD001565) (16) |
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* [PXD001550](https://www.ebi.ac.uk/pride/archive/projects/PXD001550) (17) |
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* [PXD001546](https://www.ebi.ac.uk/pride/archive/projects/PXD001546) (17) |
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* [PXD001060](https://www.ebi.ac.uk/pride/archive/projects/PXD001060) (18) |
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* [PXD001374](https://www.ebi.ac.uk/pride/archive/projects/PXD001374) (19) |
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* [PXD001333](https://www.ebi.ac.uk/pride/archive/projects/PXD001333) (20) |
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* [PXD000474](https://www.ebi.ac.uk/pride/archive/projects/PXD000474) (21) |
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* [PXD000612](https://www.ebi.ac.uk/pride/archive/projects/PXD000612) (22) |
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* [PXD001170](https://www.ebi.ac.uk/pride/archive/projects/PXD001170) (23) |
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* [PXD000964](https://www.ebi.ac.uk/pride/archive/projects/PXD000964) (24) |
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* [PXD000836](https://www.ebi.ac.uk/pride/archive/projects/PXD000836) (25) |
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* [PXD000674](https://www.ebi.ac.uk/pride/archive/projects/PXD000674) (26) |
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* [PXD000680](https://www.ebi.ac.uk/pride/archive/projects/PXD000680) (27) |
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* [PXD000218](https://www.ebi.ac.uk/pride/archive/projects/PXD000218) (28) |
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1. Peng, X. et al. Identification of missing proteins in the phosphoproteome of kidney cancer. J. Proteome Res. 16, 4364–4373, |
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DOI: 10.1021/acs.jproteome.7b00332 (2017). |
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2. Post, H. et al. Robust, sensitive, and automated phosphopeptide enrichment optimized for low sample amounts applied to |
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primary hippocampal neurons. J. Proteome Res. 16, 728–737, DOI: 10.1021/acs.jproteome.6b00753 (2016). |
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3. Tsiatsiani, L. et al. Opposite electron-transfer dissociation and higher-energy collisional dissociation fragmentation |
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characteristics of proteolytic k/r(x)n and (x)nk/r peptides provide benefits for peptide sequencing in proteomics and |
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phosphoproteomics. J. Proteome Res. 16, 852–861, DOI: 10.1021/acs.jproteome.6b00825 (2016). |
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4. Espadas, G., Borràs, E., Chiva, C. & Sabidó, E. Evaluation of different peptide fragmentation types and mass analyzers in |
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data-dependent methods using an orbitrap fusion lumos tribrid mass spectrometer. PROTEOMICS 17, DOI: 10.1002/pmic. |
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201600416 (2017). |
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5. Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. |
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Cell Syst. 4, 587–599.e4, DOI: 10.1016/j.cels.2017.05.009 (2017). |
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6. Tran, T. T., Strozynski, M. & Thiede, B. Quantitative phosphoproteome analysis of cisplatin-induced apoptosis in jurkat t |
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cells. PROTEOMICS 17, DOI: 10.1002/pmic.201600470 (2017). |
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7. Liu, Z., Wang, F., Chen, J., Zhou, Y. & Zou, H. Modulating the selectivity of affinity absorbents to multi-phosphopeptides |
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by a competitive substitution strategy. J. Chromatogr. A 1461, 35–41, DOI: 10.1016/j.chroma.2016.07.042 (2016). |
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8. Humphrey, E. S. et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling. |
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Mol. Cell. Proteomics 15, 2671–2685 (2016). |
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9. Picariello, G. et al. Antibody-independent identification of bovine milk-derived peptides in breast-milk. Food Funct. 7, |
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3402–3409 (2016). |
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10. Francavilla, C. et al. Phosphoproteomics of primary cells reveals druggable kinase signatures in ovarian cancer. Cell |
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Reports 18, 3242–3256, DOI: 10.1016/j.celrep.2017.03.015 (2017). |
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11. Lyon, S. M. et al. A method for whole protein isolation from human cranial bone. Anal. Biochem. 515, 33–39, DOI: |
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10.1016/j.ab.2016.09.021 (2016). |
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12. Nguyen, E. V. et al. Hyper-phosphorylation of sequestosome-1 distinguishes resistance to cisplatin in patient derived high |
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grade serous ovarian cancer cells. Mol. amp; Cell. Proteomics 16, 1377–1392, DOI: 10.1074/mcp.m116.058321 (2017). |
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13. Drake, J. M. et al. Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166, 1041–1054, |
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DOI: 10.1016/j.cell.2016.07.007 (2016). |
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14. Su, N. et al. Special enrichment strategies greatly increase the efficiency of missing proteins identification from regular |
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proteome samples. J. Proteome Res. 14, 3680–3692 (2015). |
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15. Creedon, H. et al. Identification of novel pathways linking epithelial-to-mesenchymal transition with resistance to |
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her2-targeted therapy. Oncotarget 7, 11539–11552, DOI: 10.18632/oncotarget.7317 (2016). |
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16. van der Mijn, J. C. et al. Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics. J. |
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Proteomics 127, 259–263, DOI: 10.1016/j.jprot.2015.04.006 (2015). |
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17. Piersma, S. R. et al. Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer |
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cell lines. J. Proteomics 127, 247–258, DOI: 10.1016/j.jprot.2015.03.019 (2015). |
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18. Ruprecht, B. et al. Comprehensive and reproducible phosphopeptide enrichment using iron immobilized metal ion affinity |
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chromatography (fe-imac) columns. Mol. amp; Cell. Proteomics 14, 205–215, DOI: 10.1074/mcp.m114.043109 (2015). |
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19. Kauko, O. et al. Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic |
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ras and cip2a signaling. Sci. Reports 5, DOI: 10.1038/srep13099 (2015). |
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20. Alpert, A. J., Hudecz, O. & Mechtler, K. Anion-exchange chromatography of phosphopeptides: weak anion exchange |
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versus strong anion exchange and anion-exchange chromatography versus electrostatic repulsion-hydrophilic interaction |
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chromatography. Anal. Chem. 87, 4704–4711 (2015). |
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21. Suni, V., Imanishi, S. Y., Maiolica, A., Aebersold, R. & Corthals, G. L. Confident site localization using a simulated |
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phosphopeptide spectral library. J. Proteome Res. 14, 2348–2359 (2015). |
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17/25 |
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22. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of tyr and ser/thr-based signaling. |
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Cell Reports 8, 1583–1594, DOI: 10.1016/j.celrep.2014.07.036 (2014). |
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23. Tong, J. et al. Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in |
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acute myeloid leukemia. PROTEOMICS 17, DOI: 10.1002/pmic.201600361 (2017). |
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24. Bauer, M. et al. Evaluation of data-dependent and -independent mass spectrometric workflows for sensitive quantification |
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of proteins and phosphorylation sites. J. Proteome Res. 13, 5973–5988 (2014). |
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25. Shevchuk, O. et al. HOPE-fixation of lung tissue allows retrospective proteome and phosphoproteome studies. J. Proteome |
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Res. 13, 5230–5239 (2014). |
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26. Publication pending |
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27. Molden, R. C., Goya, J., Khan, Z. & Garcia, B. A. Stable isotope labeling of phosphoproteins for large-scale phosphorylation |
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rate determination. Mol. amp; Cell. Proteomics 13, 1106–1118, DOI: 10.1074/mcp.o113.036145 (2014). |
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28. Rajeeve, V., Vendrell, I., Wilkes, E., Torbett, N. & Cutillas, P. R. Cross-species proteomics reveals specific modulation of |
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signaling in cancer and stromal cells by phosphoinositide 3-kinase (PI3K) inhibitors. Mol. Cell. Proteomics 13, 1457–1470 |
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(2014). |
