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54 results for “phosphoproteomics”
IV-KAPhE kinase-substrate assignments for the entire human phosphoproteome
<p>This data set includes the full, all-vs-all kinase-substrate assignments by the IV-KAPhE method for the entire human phosphoproteome (union of the PhosphoSitePlus human phosphosite database and the Ochoa et al. 2020 high-confidence human phosphoproteome). This is an unfiltered version of Supplemental Table S1 from Invergo BM (2022) "Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data".</p> <p>The data set also includes files to facilitate scoring new human phosphosites, particularly the in vitro half of the IV-KAPhE model. "naive-bayes-plus-model.tar.gz" is an archive of HDF5 files comprising the "Naive Bayes+" multi-label, in vitro kinase-substrate assignment model used in the IV-KAPhE model, as described in the manuscript. These files are to be used with the motif-kit software package and can be used to score new sites. "kinase-int-domains-sig.tsv" and "kinase-sub-domains-sig.tsv" contain Pfam domains enriched among each kinase's interacting partners or substrates, respectively. Finally, "human-kinase-interactions.tsv" and "human-kinase-2nd-interactions.tsv" contain physical interactions and indirect ("2 hop") interactions between human protein kinases and other proteins, as described in the manuscript.</p>
Processed proteomic and phosphoproteomic timeseries from Ostreococcus tauri, with Gene Ontology enrichment, from "A phospho-dawn of protein modification anticipates light onset in the picoeukaryote O. tauri"
<p>Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga <em>Ostreococcus tauri </em>under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.</p>
A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells
<p>Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. <em>In vitro</em> experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.</p>
A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells
<p>Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. <em>In vitro</em> experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.</p>
pY and pSTY phosphoproteomic data for interactive volcano plots - by Glykofridis et al.
<p>These CSV files are generated by Glykofridis et al. (2021) and part of the supplementary data of "<strong>Phosphoproteomic analysis of FLCN inactivation highlights differential kinase pathways and regulatory TFEB phosphoserines</strong>" to be published in Molecular and Cellular Proteomics.</p> <p>The CSV files are used as input to generate (interactive) volcano plots, using the web app VolcanoNoseR. The code is archived here: https://zenodo.org/record/3625858</p> <p>The most up-to-date version of the interactive web app is available here: <a href="https://huygens.science.uva.nl/VolcaNoseR/">https://huygens.science.uva.nl/VolcaNoseR/</a></p>
Quantitative Mouse Phosphoproteomic Dataset
<p>A curated and developed mouse phosphoproteomic database consisting of 10 publications that hold 33 experiments between them. This newly developed database has 136 conditions with 142,705 unique peptides and 10,052 unique UniProt ids.</p>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
Supplementary information to the article by van Beijnum et al. "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment"
<p>Supplementary information to the article "Integrating phenotypic search and phosphoproteomic profiling of active kinases for optimization of drug mixtures for RCC treatment".</p> <p><strong>Judy R. van Beijnum<sup>1</sup>, Andrea Weiss<sup>2, 3</sup>, Robert H. Berndsen<sup>1,2 </sup>, Tse J. Wong<sup>1</sup>, Louise C. Reckman<sup>1</sup>, Sander R. Piersma<sup>4,5</sup>, Marloes Zoetemelk<sup>2,3</sup>, Richard de Haas<sup>1,4,5</sup>, Olivier Dormond<sup>6</sup>, Axel Bex<sup>7,8</sup>, Alexander A. Henneman<sup>4,5</sup>, Connie R. Jimenez<sup>4,5</sup>, Arjan W. Griffioen<sup>1</sup>, Patrycja Nowak-Sliwinska<sup>2,3,9</sup>*</strong></p> <p> </p> <p><sup>1</sup> Angiogenesis Laboratory, Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands;</p> <p><sup>2</sup> Molecular Pharmacology Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland*;</p> <p><sup>3 </sup>Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland</p> <p><sup>4 </sup>Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands</p> <p><sup>5</sup> OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands</p> <p><sup>6</sup> Department of Visceral surgery, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland<sup> </sup> </p> <p><sup>7</sup> Royal Free London NHS Foundation Trust, Renal Cancer Centre, UCL Division of Surgical and Interventional Science, London, UK</p> <p><sup>8</sup> Netherlands Cancer Institute, Amsterdam, The Netherlands</p> <p><sup>9 </sup> Translational Research Centre in Oncohaematology, Geneva, Switzerland</p> <p> Correspondence: <a href="mailto:Patrycja.Nowak-Sliwinska@unige.ch">Patrycja.Nowak-Sliwinska@unige.ch</a></p>
Dataset supporting the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference"
<p>Datasets involved in the benchmarking of kinase activity inference as presented in the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference".</p>
Evaluation of Phosphoproteomics data-driven signalling network inference
<p><strong>Data</strong></p> <p>Each processed data set and its adjacency matrices representing gold standard networks are stored in one specific folder. The folders have the data set name.</p> <p>The process of obtaining the pairwise results and the evaluation metrics are divided into two main parts:</p> <p><strong>1. Pairwise results</strong></p> <ul> <li>First, all the data set folders have to be placed in one main folder.</li> <li>Then run the script: <em>RunAll_Matrices.R</em></li> <li>This script: <ul> <li>Goes through each data set folder: <ul> <li>reads the data set</li> <li>calculates for each method the resulting matrix</li> <li>Save in the data folder one file with all resulting matrices: file type RData</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>2. Evaluation </strong></p> <ul> <li>After this first step, each data set folder has a file that contains all the resulting adjacency matrices.</li> <li>The second step is to run the script RunAll_Golds.R</li> <li>This script <ul> <li>Goes through each folder: <ul> <li>reads the data set</li> <li>reads the result adjacency matrices from RData files</li> <li>reads the gold networks</li> <li>Calculates all evaluation metrics for each result and each gold network type.</li> </ul> </li> </ul> </li> </ul>
Figure 1 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 1. Proportion of serine, threonine, and tyrosine in phosphorylation sites.
MSFragger open searches of proteome shotgun and phosphoproteomic runs PXD013868 (Mergner et al., 2020)
<p>MSFragger open searches of phosphoproteomics and shotgun proteome runs of large-scale tissue atlas in Arabidopis (Mergner et al., 2020). Part of the Plant PTM Viewer 2.0 update paper.</p>
Proteomics and phosphoproteomics analysis of the tyrosine phosphatase SHP2 acquired resistance to SHP099 in the context of AML
<p>Proteomics analysis of AML cell lines (MV-4-11, MOLM-13, EOL-1 and OCI-M1) presenting acquired resistance to SHP099 and their parent sensitive cells in presence of the SHP2 allosteric inhibitor SHP099 or DMSO (control). These samples were labelled with TMT and analysed by mass spectrometry. </p> <p>The same approach was conducted to analyse the phosphoproteome of MV-4-11 cells (parent and resistant) upon SHP099 treatment.</p> <p>These data are associated with the manuscript "<strong>Tyr-62 phosphorylation of the tyrosine phosphatase SHP2 enables acquired resistance to SHP2 allosteric inhibitors</strong>"</p>
Kinex infers causal kinases from phosphoproteomics data
<p>Reference table of 82,755 pre-scored peptides containing serine and/or threonine phosphorylation sites for 303 kinases. This reference table is used as input for Kinex, a Python package, which infers causal serine/threonine kinases from phosphoproteomics data. <br><br>Kinex is released with the GNU General Public License, openly accessible to all users at https://github.com/bedapub/kinex.</p>
Proteomic and phosphoproteomic profiling of AML
Open the record for dataset details and reuse information.
Dataset related to article "Phosphoproteomic mapping of CCR5 and ACKR2 signaling properties"
<p>This record contains raw data related to article "Phosphoproteomic mapping of CCR5 and ACKR2 signaling properties"</p> <p>ACKR2 is an atypical chemokine receptor structurally uncoupled from G proteins and unable to activate those signaling pathways used by conventional chemokine receptors to promote cell migration. Nevertheless, ACKR2 regulates inflammation and immune responses by shaping chemokine gradients in tissues by means scavenging inflammatory chemokines. To investigate the signaling pathways downstream ACKR2, a quantitative SILAC-based phosphoproteomic analysis coupled to systems biology approaches based on network analysis was carried out in a HEK293 cell model expressing ACKR2 or its conventional counterpart CCR5, stimulated or not with the common agonist CCL3L1 for short (3 min) and long (30 min) time points. As expected, many of the identified proteins are known to participate in conventional signal transduction pathways and in the regulation of cytoskeleton dynamics. However, our analyses revealed unique phosphorylation and network signatures, suggesting roles for ACKR2 other than its scavenger activity. In conclusion, mapping of phosphorylation events at holistic level indicated that conventional and atypical chemokine receptors differ for signaling properties and provide an unprecedented level of detail of chemokine receptor signaling aimed at identifying potential targets for regulation of ACKR2 and CCR5 function.</p>
msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease
<p>Data associated with the paper</p> <p>Pham TV, Henneman AA, Truong NX, Jimenez CR, msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease, <em>Bioinformatics</em>, 2024;btae432, https://doi.org/10.1093/bioinformatics/btae432</p> <p>olsen-directDIA-SN.sne<br> - Spectronaut 18 search of the Olsen dataset.</p> <p>PXD014525-sn-18-olsen.zip<br> - text export of olsen-directDIA-SN.sne</p> <p>20220530_113551_Phospho_optimal_dia-PASEF_21min_v16_Report.zip<br> - Spectronaut 16 output of the data in PXD034128</p> <p>Phospho_EGF_diAID.zip<br> - DIA-NN 1.8 output of the data in PXD034128</p> <p>PXD034128-diann-1.8.2-beta27.zip<br> - DIA-NN 1.8.1 search of the data in PXD034128</p> <p>20231106_133349_optimal4.sne<br> - Spectronaut 18 search of the data in PXD034128</p> <p>PXD034128-sn-18.zip<br> - text export of 20231106_133349_optimal4.sne</p> <p>uniprot-reviewed_yes_AND_organism__Homo_sapiens__Human___9606___--.zip<br> - protein sequences</p> <p>ptm.rs<br> - Spectronaut report export scheme</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.