Dataset Viewer (First 5GB)
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MQNHGYENPTY[UNIMOD:21]K | 2 | 781.307007 | [
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SRDPHY[UNIMOD:21]DDFR | 3 | 463.183258 | [
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DHLY[UNIMOD:21]STVC[UNIMOD:4]KPR | 2 | 728.324036 | [
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SQAKPGTPGGTGGPAPQY[UNIMOD:21] | 2 | 875.887756 | [
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HTPY[UNIMOD:21]S[UNIMOD:21]IRC[UNIMOD:4]ER | 2 | 739.784058 | [
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AVDGY[UNIMOD:21]VKPQIK | 2 | 649.326172 | [
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AVC[UNIMOD:4]STY[UNIMOD:21]LQSR | 2 | 632.769958 | [
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VKGEY[UNIMOD:21]DVTVPK | 2 | 657.816833 | [
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AVC[UNIMOD:4]STY[UNIMOD:21]LQSR | 2 | 632.769714 | [
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GSTAENAEY[UNIMOD:21]LR | 2 | 645.768616 | [
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AVC[UNIMOD:4]STY[UNIMOD:21]LQSR | 2 | 632.769348 | [
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EVGY[UNIMOD:21]LEFTGPPQKPPR | 3 | 632.3056 | [
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End of preview. Expand
in Data Studio
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).
- Curated by: Jesper Lauridsen, Pathmanaban Ramasamy
- License: EMBL-EBI terms of use
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:
- PXD006482 (1)
- PXD005366 (2)
- PXD004447 (3)
- PXD004940 (4)
- PXD004452 (5)
- PXD004415 (6)
- PXD004252 (7)
- PXD003198 (8)
- PXD003657 (9)
- PXD003531 (10)
- PXD003215 (11)
- PXD002394 (12)
- PXD002286 (13)
- PXD002255 (14)
- PXD002057 (15)
- PXD001565 (16)
- PXD001550 (17)
- PXD001546 (17)
- PXD001060 (18)
- PXD001374 (19)
- PXD001333 (20)
- PXD000474 (21)
- PXD000612 (22)
- PXD001170 (23)
- PXD000964 (24)
- PXD000836 (25)
- PXD000674 (26)
- PXD000680 (27)
- PXD000218 (28)
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- Humphrey, E. S. et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling. Mol. Cell. Proteomics 15, 2671–2685 (2016).
- Picariello, G. et al. Antibody-independent identification of bovine milk-derived peptides in breast-milk. Food Funct. 7, 3402–3409 (2016).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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
- 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).
- 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).
- 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).
- Shevchuk, O. et al. HOPE-fixation of lung tissue allows retrospective proteome and phosphoproteome studies. J. Proteome Res. 13, 5230–5239 (2014).
- Publication pending
- 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).
- 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).
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