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> Physiology > Molecular Function > Phosphorylation

Phosphorylation

Phosphorylation is a crucial biological process where a phosphate group is added to a molecule, typically a protein, by an enzyme called a kinase.
This modification can alter the structure, function, and activity of the target molecule, making it an essential mechanism for regulating cellular processes, such as signal transduction, metabolism, and cell cycle control.
Phosphorylation plays a key role in a wide range of physiological and pathological conditions, including cell signaling, hormone regulation, and the development of diseases like cancer and neurodegenerative disorders.
Researchers studying phosphorylation rely on optimal protocols and methods to ensure reproducibility and accuracy in their experiments.
PubCompare.ai's AI-driven comparisons can help identify the best protocols from literature, preprints, and patents, taking your phosphorylation studies to the next leve.

Most cited protocols related to «Phosphorylation»

1
Causal analysis of biological networks
Causal analysis algorithms are based on a ‘master’ network which is derived from the Ingenuity Knowledge Base, and given by a directed multigraph with nodes representing mammalian genes, chemicals, protein families, complexes, microRNA species and biological processes, and edges reflecting observed cause–effect relationships. For the following let be the set of all genes, and the set of all biological processes. For each edge we define functions and that map to its unique source and target nodes, respectively. The graph has no self-edges, i.e. Each edge in is associated with a set of underlying findings obtained from the literature, where each finding is associated with a ‘sign’ that represents the regulation direction of the causal effect. If effect is activating (inhibiting), and for the direction of the effect is unknown or ambiguous. Depending on the underlying findings, edges are classified into the distinct types, ‘T’, ‘A’ and ‘P’, represented by three disjoint subsets of E: Et, Ea and Ep. T-edges are related to transcription and expression events including protein–DNA binding (i.e. regulation of the abundance of the target node), while A-edges represent the functional activation or inhibition of the target node (e.g. through phosphorylation in a signaling cascade). P-edges are associated with the regulation of biological processes (e.g. apoptosis). The master network G is a multigraph since two given source and target nodes can be connected by a T-edge, and an A-edge at the same time.
The various finding categories and their respective association with edge types and signs are given in a table in the Supplementary Material. Findings about changes of molecular modification states (e.g. phosphorylation) are included in the A-edge type if an activating or inhibiting effect can be inferred. All T-edges are connected to genes as their target nodes, and all P-edges connect to biological processes, Depending on the signs of the underlying findings, each edge is in turn associated with a unique direction of the causal effect that is either activating, inhibiting or unknown, and represented by the sign In addition, we also associate edges with weights reflecting our confidence in the assigned direction of the effect. Details are given in the Supplementary Material.
Krämer A., Green J., Pollard J J.r, & Tugendreich S. (2013). Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics, 30(4), 523-530.
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Publication 2013
Apoptosis Biological Processes Genes Mammals MicroRNAs Phosphorylation Proteins Psychological Inhibition Transcription, Genetic
2
Phosphoproteomics Analysis of Ovarian Cancer
The proteomics data used in the case study were obtained from a phosphoproteomics study of ovarian cancer13 (link) (specifically Supplementary Table 3 of the study). The list of proteins used to retrieve the network was extracted from the significantly regulated phosphorylation sites listed in this table. Furthermore, log-ratios of abundance in disease versus healthy tissues were computed based on the average abundance values over the samples listed in the same table. To facilitate the subsequent visualization in Cytoscape, we also modified the Supplementary Table 3 by keeping only the significantly regulated phosphorylation sites and sorting them by significance. This modified version of the table, which was imported into Cytoscape, is provided as Table S1.
During import of the associated log-ratios and phosphorylation cluster assignments, the most significant phosphorylation site was chosen whenever multiple sites were found on the same protein (by first sorting the table based on “Gene name” and then on “adj. p-value”).
All analyses were performed on the 12th of April 2018 using Cytoscape version 3.6.1 and stringApp version 1.3.2 and are provided in a Cytoscape session (https://doi.org/10.6084/m9.figshare.7258235). Additionally, we used clusterMaker2 version 1.2.1 to perform Markov clustering (MCL)14 (link) of the protein network and EnhancedGraphics version 1.2.015 (link) to enable stringApp visualization of enriched terms as circular plots onto the network nodes.
Doncheva N.T., Morris J.H., Gorodkin J, & Jensen L.J. (2018). Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. Journal of proteome research, 18(2), 623-632.
Publication 2018
Genes Ovary Phosphorylation Proteins Tissues
3
Phosphoproteomic Analysis of Mouse Organs
A mouse dataset corresponding to the spectral type (CID, Low, Phosphorylation, Trypsin) contains 181,093 spectra. This dataset was generated from the Gygi laboratory (Harvard Medical School). Nine mouse organ proteins were digested with trypsin and the resulting peptides were fractionated via SCX. Phosphopeptides were enriched via immobilized metal affinity chromatography and analyzed in duplicates via LC-MS/MS on an LTQ-Orbitrap mass spectrometer. Out of 9 organ tissues analyzed, we used the spectra generated from the brain tissue. The detailed experimental procedures are described in [21 (link)].
Kim S, & Pevzner P.A. (2014). Universal database search tool for proteomics. Nature communications, 5, 5277.
Publication 2014
Brain Chromatography, Affinity Metals Mice, Laboratory Peptides Phosphopeptides Phosphorylation Proteins Tandem Mass Spectrometry Tissues Trypsin
4
Automated HDX-WB Peptide Detection
As in previous solutions [6 (link), 8 (link)–10 , 18 (link)], the software attempts to determine the correct RT range for each peptide automatically. The input retention times are provided in the peptide set, presented in the downstream interface, and used to define RT range in the event that the peptide is not found within the specified limits of the detection algorithm (e.g., signal intensity, ppm, and fit score). The HDX-WB peptide isotope detection algorithm follows a similar approach to HD Desktop, in which the theoretical distribution for the peptide is initially calculated with Qmass [29 (link), 30 (link)], and is then compared with the experimental spectra with a least squares regression. For this version of software, we precalculate and save all possible theoretical distributions for a given peptide, and then compare them with the experimental data from individual scans. Because we now exclusively acquire data with high resolution FT-MS instruments (Orbitrap), we no longer require a co-add moving window approach as described in HD Desktop. Filters are then applied such as mass accuracy, m/z range, retention time range, and intensity to define the best matched %D value. This approach of indexing all possible theoretical distributions for each scan increases the speed of peptide detection without compromising accuracy.
In cases where MS/MS-based peptide identification is unavailable or limited in sequence coverage, HDX-WB provides the ability to extract all possible peptides from the protein sequence in place of a predetermined input peptide list. This operates in a manner similar to Hexicon [5 (link)]. To account for low enzymatic specificity, the software determines all possible combinations of peptide sequences between user defined residues in length and runs them through the detect algorithm however cleavage after H, K, P, and R may be eliminated from consideration based upon the Hamuro rules of pepsin specificity [31 (link)]. This has been shown to be a reasonable approach with novel or common enzymes used in HDX, such as pepsin or Fungal XIII, albeit somewhat more computationally expensive. The input list of peptides is not a requirement if this option is used and the approach has been shown to provide increased sequence coverage [5 (link)]. However, care should be taken when using this approach, as no product ion information is considered in the peptide identification.
An important consideration when searching MS1 data from predefined peptide sets is the detection of mass conflicts, in which a putative peptide can share the same or nearly the same mass with one, or many, other peptides within the peptide set. The software defines a mass conflict as two or more peptides within the peptide set whose theoretical monoisotopic mass is less than or equal to the error tolerance designated in the experiment set up. A mass conflict will indicate potential false positives from the detection process, as peptides with the same elemental composition will result in the same isotopic distribution and mass. HDX-WB provides the ability to automatically detect and flag peptides with mass conflicts within a user’s dataset, and allow the user to validate them manually.
HDX-WB is able to detect the potential presence of modifications from raw data; however, site localization is not possible because it is MS1 raw data being interrogated. For example, a search for one serine phosphorylation site on the peptide LULSSTVK would need to consider the forms LULpSSTVK and LULSpSTVK. Both of these are comprised of the same elemental composition and, as a result, the MS1 spectra are identical. The configuration of the available modifications within HDX-WB is made available in an external file allowing users to customize them as needed. These installed modifications are subsequently made available via the detection interface. Modifications may also be added directly into the peptide set if the site is well characterized. The software additionally provides support for detection of point mutations.
Pascal B.D., Willis S., Lauer J.L., Landgraf R.R., West G.M., Marciano D., Novick S., Goswami D., Chalmers M.J, & Griffin P.R. (2012). HDX Workbench: Software for the Analysis of H/D Exchange MS Data. Journal of the American Society for Mass Spectrometry, 23(9), 10.1007/s13361-012-0419-6.
Publication 2012
Amino Acid Sequence Cytokinesis Enzymes FLAG peptide Immune Tolerance Isotopes Pepsin A Peptides Phosphorylation Point Mutation Radionuclide Imaging Retention (Psychology) Serine Tandem Mass Spectrometry
5
Comprehensive Multi-omics Profiling of Bladder Cancer
Tumour and normal samples were obtained with institutional review board-approved consent and processed using a modified AllPrep kit (Qiagen) to obtain purified DNA and RNA. Quality-control analyses revealed only modest batch effects (Supplementary Text S13.1). The tumours were profiled using Affymetrix SNP 6.0 microarrays for SCNAs, low-pass WGS (HiSeq) for SCNAs and translocations, RNA-seq (HiSeq) for mRNA and miRNA expression, Illumina Infinium (HumanMethylation450) arrays for DNA methylation, HiSeq for exome sequencing and RPPA for protein expression and phosphorylation. Statistical analysis and biological interpretation of the data were spearheaded by the TCGA Genome Data Analysis Centers. Sequence files are in CGHub (https://cghub.ucsc.edu/). All other molecular, clinical and pathological data are available through the TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/). Data matrices, molecular analysis results and supporting information are at http://tcga-data.nci.nih.gov/docs/publications/bladder_2013/. The data can be explored through a compendium of Next-Generation Clustered Heat Maps (http://bioinformatics.mdanderson.org/main/TCGA/Supplements/NGCHM-BLCA), the cBio Cancer Genomics Portal (http://cbioportal.org), PARADIGM (http://sysbio.soe.ucsc.edu/paradigm/tutorial/), SpliceSeq (http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview), MBatch batch effects assessor (http://bioinformatics.mdanderson.org/tcgabatcheffects) and Regulome Explorer (http://explorer.cancerregulome.org/). Also see Supplementary Materials.
Comprehensive molecular characterization of urothelial bladder carcinoma. (2014). Nature, 507(7492), 315-322.
Publication 2014
Biopharmaceuticals Dietary Supplements DNA Chips Ethics Committees, Research Genome Malignant Neoplasms Methylation Microarray Analysis MicroRNAs Microtubule-Associated Proteins Neoplasms Phosphorylation RNA, Messenger RNA-Seq Translocation, Chromosomal type III polyketide synthase Urinary Bladder

Most recents protocols related to «Phosphorylation»

1
Interferon Signaling in ADAR1-Dependent Cancer Cells
Not available on PMC !

Example 9

An analysis of gene ontology (GO) categories associated with ADAR1 dependent cells revealed that NCI-H1650 and HCC366 (“HCC-366”), two ADAR1 dependent cell lines, both have elevated basal expression of interferon inducible genes (FIG. 35). The expression levels of interferon-inducible genes were also elevated in NCI-H196 cells (FIG. 36).

In light of the correlation between ADAR1 dependency and the expression of interferon-inducible genes, additional cancer cell lines from the Molecular Signatures Database (MSigDB) (Liberzon et al. (2015) Cell Systems 1:417-425) was examined. Cancer Cell Line Encyclopedia (CCLE) clustering was performed based on the Type I/Interferon-a gene set, which contained 97 genes including PKR. The resulting cluster included HCC366, NCI-H1650 and 9 additional lung cell lines. Among these cell lines, HCC1438 and NCI-H596 were sensitive to knockout of ADAR1 by lentiviral CRISPR-Cas9 (FIG. 37).

All the above-identified ADAR1 dependent cancer cell lines showed elevated interferon signaling markers, e.g., phosphorylation of STAT1 and expression of interferon-stimulated gene (ISGs) (FIG. 38). Elevated interferon signaling in the ADAR1 dependent cancer cell lines did not necessarily lead to PD-L1 overexpression (FIG. 38). Cell lines in the high interferon signaling cluster (LN215_CENTRAL_NERVOUS_SYSTEM, NCIH596_LUNG, HCC1438_LUNG, T3M10_LUNG, NCIH1869_LUNG, SW900_LUNG, HCC366_LUNG, SKLU1_LUNG, NCIH1650_LUNG, HCC4006_LUNG, and NCIH1648_LUNG) displayed high IFN-β, but not IFN-α (FIG. 39). As such, cancer cell lines sensitive to ADAR1 or ISG15 knockdown displayed elevated interferon secretion and downstream signaling. To further investigate the relationship between ADAR1 and IFN-β secretion, it was found that ADAR1 knockout led to amplified IFN-β secretion in cell lines primed with high basal interferon activation (FIG. 40). It was also found that ADAR1 dependent cell lines do not show enhanced sensitivity to IFN-α or IFN-β alone (FIG. 41).

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
Full text: Click here
Patent 2024
CD274 protein, human Cell Lines Cells Central Nervous System Clustered Regularly Interspaced Short Palindromic Repeats Gene Expression Genes Hypersensitivity Interferon-alpha Interferons Interferon Type I Light Lung Malignant Neoplasms Phosphorylation secretion STAT1 protein, human
2
Bioactivity Validation of Compounds 3 and 7
Not available on PMC !

Example 19

To confirm bioactivity of 3 and 7, experiments were performed with the HH cell line, a mature T cell line derived from peripheral blood of a patient with aggressive cutaneous T cell leukemia/lymphoma (ATCC® CRL-2105™) which been demonstrated to only express the IL-2Rβ/γ. One of the earliest events in cytokine mediated activation of lymphocytes such as CD8+ T cells and NK cells is Janus Associated Kinase mediated phosphorylation and activation of Signal transducer and activator of transcription (pSTAT5). Thus, pSTAT5 was used to measure biological activity of 3 and 7 alongside 12. 3 demonstrated clear bioactivity in IL-2Rβ/γ expressing HH cells (EC50: 773 ng/ml) that was approximately 3.5 fold lower than 12 (EC50: 233 ng/ml). Additionally, 7 induced bioactivity (EC50: 756 ng/ml) very similar to 3, demonstrating that 7 retains bioactivity after being released from prodrug 5 even after accelerated (stress) conditions.

US11879001B2. Conjugate comprising an IL-2 moiety (2024-01-23). ASCENDIS PHARMA ONCOLOGY DIVISION A/S [DK]. Inventors: Nina Gunnarsson [DK], Matiss Maleckis [DK], David B. Rosen [US].
Full text: Click here
Patent 2024
Biopharmaceuticals BLOOD CD8-Positive T-Lymphocytes Cell Lines Cells Cytokine IL19 protein, human Kinase, Janus Leukemia Lymphocyte Activation Lymphoma, T-Cell, Cutaneous Natural Killer Cells Patients Phosphorylation Prodrugs Transcription, Genetic Transducers
3
Targeting ADAR1 in High Interferon Tumors
Not available on PMC !

Example 17

Since interferon signaling is spontaneously activated in a subset of cancer cells and exposes potential therapeutic vulnerabilities, it was tested whether there is evidence for similar endogenous interferon activation in primary human tumors. An IFN-GES threshold was computed to predict ADAR dependency across the CCLE cell lines and was determined to be a z-score above 2.26 (FIG. 66, panel A). This threshold was applied to The Cancer Genome Atlas (TCGA) tumors, to identify primary cancers with similarly high interferon activation. Restricting the analysis to the 4,072 samples analyzed by TCGA with at least 70% tumor purity as estimated by the ABSOLUTE algorithm (Carter et al. (2012) Nat. Biotechnol. 30:413-421), 2.7% of TCGA tumors displayed IFN-GESs above this threshold (FIG. 66, panel B and. GSEA of amplified genes in these high purity, high interferon tumors revealed the top pathway as “Type I Interferon Receptor Binding”, comprising 17 genes that all encode type I interferons and are clustered on chromosome 9p21.3 (FIG. 67).

Furthermore, analysis of TCGA copy number data showed that the interferon gene cluster including IFN-β (IFNβI), IFN-ε (IFNE), IFN-ω (IFNWI), and all 13 subtypes of IFN-α on chromosome 9p21.3, proximal to the CDKN2A/CDKN2B tumor suppressor locus, is one of the most frequently homozygously deleted regions in the cancer genome. The interferon genes comprise 16 of the 26 most frequently deleted coding genes across 9,853 TCGA cancer specimens for which ABSOLUTE copy number data are available (FIG. 66, panels C and D). Interferon signaling and activation, both in tumors with high IFN-GESs or deletions in chromosome 9p, therefore represent a biomarker to stratify patients who benefit from interferon modulating therapies.

In summary, specific cancer cell lines have been identified with elevated IFN-β signaling triggered by an activated cytosolic DNA sensing pathway, conferring dependence on the RNA editing enzyme, ADAR1. In cells with low, basal interferon signaling, the cGAS-STING pathway is inactive and PKR levels are reduced (FIG. 68, panel A). Upon cGAS-STING activation, interferon signaling and PKR protein levels are elevated but ADAR1 is still able to suppress PKR activation (FIG. 68, panel B). However, once ADAR1 is deleted, the abundant PKR becomes activated and leads to downstream signaling and cell death (FIG. 68, panel C). This is also shown in normal cells lines (e.g. A549 and NCI-H1437) once exogenous interferon is introduced (FIG. 68, panel D). ADAR1 deficiency in cell lines with high interferon levels, whether from endogenous or exogenous sources, led to phosphorylation and activation of PKR, ATF4-mediated gene expression, and apoptosis. Recent studies have shown that cGAS activation and innate interferon signaling, induced by cytosolic DNA released from the nucleus by DNA damage and genome instability (Mackenzie et al. (2017) Nature 548:461-465; Harding et al. (2017) Nature 548:466-470), led to elevated interferon-related gene expression signatures, which have been linked to resistance to DNA damage, chemotherapy, and radiation in cancer cells (Weichselbaum et al. (2008) Proc. Natl. Acad. Sci. USA 105:18490-18495). In high-interferon tumors, blocking ADAR1 might be effective to induce PKR-mediated apoptotic pathways while upregulating type I interferon signaling, which could contribute to anti-tumor immune responses (Parker et al. (2016) Nature 16:131-144). Alternatively, in tumors without activated interferon signaling, ADAR1 inhibition can be combined with localized interferon inducers, such as STING agonists, chemotherapy, or radiation. Generation of specific small molecule inhibitors targeting ADAR1 exploits this novel vulnerability in lung and other cancers and serves to enhance innate immunity in combination with immune checkpoint inhibitors.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].
Full text: Click here
Patent 2024
agonists Apoptosis ATF4 protein, human Biological Markers CDKN2A Gene Cell Death Cell Lines Cell Nucleus Cells Chromogranin A Chromosome Deletion Chromosomes, Human, Pair 3 Cytosol DNA Damage Electromagnetic Radiation Enzymes Gene, Cancer Gene Clusters Gene Expression Genes Genome Genomic Instability Homo sapiens IFNAR2 protein, human Immune Checkpoint Inhibitors Immunity, Innate inhibitors Interferon-alpha Interferon Inducers interferon omega 1 Interferons Interferon Type I Lung Malignant Neoplasms Neoplasms Oncogenes Patients Pharmacotherapy Phosphorylation Proteins Psychological Inhibition Response, Immune Tumor Suppressor Genes
4
Monitoring Signaling Pathways in CML Cell Lines
Not available on PMC !

Example 7

In order to provide a more readily available and reproducible cell system (and to avoid the problems seen with existing methods), experimental systems based on tissue culture cell lines may be utilized to monitor the impact of drugs on signaling pathways.

Flow cytometric methods using tissue culture cells have been routinely used for investigating the effects of drugs, for example, inhibitors of Bcr/Abl kinase that are useful in the therapy of chronic myeloid leukemia (CML). CML is associated with the Philadelphia chromosome, a genetic translocation that fuses the Abl1 gene on chromosome 9 with part of the BCR gene on chromosome 22. The resulting fusion protein contains a receptor tyrosine kinase that constitutively activates several downstream signaling pathways, including P-STAT5, P-Crkl, P-mTOR, and P—HSF. The Abl kinase is the target of several therapeutics currently used clinically, including imatinib (GLEEVEC™), nilotinib, and dasatinib. These compounds act by inhibiting the tyrosine kinase activity at the receptor level, and also concomitantly inhibit all downstream signaling pathways.

As a representative model of CML, human K562 cell line, which expresses the Bcr/Abl fusion protein and constitutively phosphorylates the downstream STAT5 target (Cytometry 54A; 75-88, 2003), was used in the following experiment. As shown in FIG. 10, treatment of K562 cells for 30 min with 2 μM GLEEVEC™ (imatinib, or STI571) results in >95% inhibition of the phosphorylation of the downstream STAT5 target. Also, as shown in FIG. 10, although the phosphorylation of STAT5 is inhibited after 30 min imatinib exposure, there is no change in the cell cycle, as measured by DNA content.

Phosphorylated STAT5 (P-STAT5) acts as a transcriptional activator of several target proteins, including Cyclin D. Constitutive expression of Cyclin D (induced by P-STAT5) maintains K562 cells in cell cycle. It was found that exposure to imatinib for 24 hr decreases S-phase (as a marker of cell proliferation) by ˜50%, and further exposure to imatinib for an additional 24 hr decreases S-phase by an additional 50-70% (data not shown).

US11867690B2. Identification of functional cell states (2024-01-09). ASEDASCIENCES AG [CH]. Inventors: Bartlomiej Rajwa [US], Vincent T Shankey [US].
Full text: Click here
Patent 2024
Cell Culture Techniques Cell Cycle Cell Lines Cell Proliferation Cells Chromosomes, Human, Pair 9 Chromosomes, Human, Pair 22 CRKL protein Cyclin D Cyclins Dasatinib Flow Cytometry FRAP1 protein, human Fusion Proteins, bcr-abl Genes Gleevec Homo sapiens Imatinib inhibitors K562 Cells Leukemias, Chronic Granulocytic nilotinib Pharmaceutical Preparations Philadelphia Chromosome Phosphorylation Phosphotransferases Proteins Protein Targeting, Cellular Psychological Inhibition Receptor Protein-Tyrosine Kinases SERPINA3 protein, human Signal Transduction Pathways Staphylococcal Protein A STAT5A protein, human STI571 Tissues Transcription, Genetic Translocation, Chromosomal Vision
5
Investigating β2AR-βarr Binding Interactions
Not available on PMC !

Example 1

This example demonstrates that the binding interaction of βarr with the β2-adrenergic receptor (β2AR).

The binding of βarr to GPCRs is mainly initiated through an interaction with the phosphorylated receptor C terminus, and conformational changes induced in βarr by this interaction promote coupling to the receptor TM core, as shown in FIG. 1. Co-immunoprecipitation experiments confirmed that heterotrimeric Gs protein, but not βarr1, can interact with purified non-phosphorylated β2-adrenergic receptor (β2AR), as shown in FIG. 2A.

To verify that this apparent lack of interaction with βarr is not simply due to poor complex stability, two assays capable of detecting complex formation in situ were performed. First, competition radioligand binding was used to measure the allosteric effects of transducers on ligand binding to the receptor. As described by the ternary complex model, first for G proteins and later for βarrs, ligand-induced changes in receptor conformation enhance the binding and affinity of transducers, which reciprocally increase ligand affinity by stabilizing an active receptor state (De Lean A, et al. (1980) J Biol Chem 255(15):7108-7117., Gurevich V V, et al. (1997) J Biol Chem 272(46):28849-28852). When wild-type (WT) β2AR was reconstituted in high-density lipoprotein (HDL) particles to mimic a cellular membrane environment (Denisov I G & Sligar S G (2016) Nat Struct Mol Biol 23(6):481-486), G protein enhanced the affinity of the full agonist isoproterenol for non-phosphorylated HDL-β2AR by nearly 1000-fold, as expected, but βarr1 had no effect even at micromolar concentrations, as shown in FIG. 2B.

Second, to directly monitor β2AR conformational changes associated with activation, the C265 at the cytoplasmic end of TM6 was labeled with monobromobimane, an environmentally sensitive fluorophore. Receptor activation leads to an outward movement of TM6 that places the bimane label in a more solvent-exposed position, causing a decrease in fluorescence and a shift in λmax (Yao X J, et al. (2009) Proc Natl Acad Sci USA 106(23):9501-9506). Indeed, isoproterenol reduced β2AR-bimane fluorescence compared to control (DMSO), and addition of Gs but not βarr1 further attenuated fluorescence, as shown in FIG. 2C.

The results of this example demonstrate that non-phosphorylated β2AR fails to form a productive interaction with βarr.

US11866482B2. System and method for homogenous GPCR phosphorylation and identification of beta-2 adrenergic receptor positive allosteric modulators (2024-01-09). Duke University [US]. Inventors: Robert J. Lefkowitz [US], Seungkirl Ahn [US], Biswaranjan Pani [US], Alem W. Kahsai [US], Laura Wingler [US], Dean Staus [US].
Full text: Click here
Patent 2024
Adrenergic Agents beta-2 Adrenergic Receptors Biological Assay Co-Immunoprecipitation Cytoplasm Fluorescence GTP-Binding Proteins high density lipoprotein receptors High Density Lipoproteins Homozygote Isoproterenol Ligands monobromobimane Movement Phosphorylation Plasma Membrane Proteins Solvents Sulfoxide, Dimethyl Transducers

Top products related to «Phosphorylation»

1
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[γ-32P]ATP is a radiolabeled compound containing the radioactive isotope phosphorus-32 (32P) attached to the gamma phosphate group of adenosine triphosphate (ATP). This product is commonly used as a tracer in various biochemical and molecular biology applications, such as nucleic acid labeling and analysis.
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More about "Phosphorylation"

Phosphorylation is a fundamental biological process where a phosphate group is covalently attached to a molecule, typically a protein, by an enzyme known as a kinase.
This post-translational modification can alter the structure, function, and activity of the target molecule, making it a crucial mechanism for regulating essential cellular processes such as signal transduction, metabolism, and cell cycle control.
Phosphorylation plays a pivotal role in a wide range of physiological and pathological conditions, including cell signaling, hormone regulation, and the development of diseases like cancer and neurodegenerative disorders.
Researchers studying phosphorylation rely on optimized protocols and methods to ensure reproducibility and accuracy in their experiments. [γ-32P]ATP, a radioactive isotope of ATP, is commonly used to monitor and quantify phosphorylation events.
Proteome Discoverer, a powerful bioinformatics software, can help identify and analyze phosphorylation sites within complex protein samples.
PVDF membranes are often used in western blotting techniques to detect and quantify phosphorylated proteins.
Fetal bovine serum (FBS) and protease inhibitor cocktails are commonly used to maintain the integrity of phosphorylated proteins during sample preparation.
Lipofectamine 2000, a transfection reagent, can be utilized to introduce kinases or phosphatases into cells, allowing for the study of their effects on phosphorylation.
The housekeeping gene β-actin is frequently used as a loading control in phosphorylation studies.
To further enhance phosphorylation research, the SuperScript double-stranded cDNA synthesis kit can be employed to generate cDNA from mRNA, which can then be used for gene expression analysis related to phosphorylation pathways.
The Mascot search engine is a powerful tool for identifying and quantifying phosphorylated peptides from mass spectrometry data.
PubCompare.ai's AI-driven comparisons can help researchers identify the best protocols from literature, preprints, and patents, taking their phosphorylation studies to the next level by improving reproducibility and accuracy.