Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

K-CLASP: A Tool to Identify Phosphosite Specific Kinases and Interacting Proteins.

ABSTRACT: Few methods are available to discover the cellular kinase that phosphorylates a specific amino acid, or phosphosite, on a protein. In addition, identifying the associated proteins bound near a phosphosite during phosphorylation would provide insights into cell biology and signaling. Here, we report K-CLASP (Kinase Catalyzed CrossLinking And Streptavidin Purification) as a method for both phosphosite-specific kinase identification and the discovery of kinase interacting proteins. K-CLASP offers a powerful tool to discover unanticipated protein-protein interactions in phosphorylation-mediated biological events.

SUBMITTER: Dedigama-Arachchige PM

PROVIDER: S-EPMC5481203 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

K-CLASP: A Tool to Identify Phosphosite Specific Kinases and Interacting Proteins.

Dedigama-Arachchige Pavithra M PM Pflum Mary Kay H MK

ACS chemical biology 20161017 12

Few methods are available to discover the cellular kinase that phosphorylates a specific amino acid, or phosphosite, on a protein. In addition, identifying the associated proteins bound near a phosphosite during phosphorylation would provide insights into cell biology and signaling. Here, we report K-CLASP (Kinase Catalyzed CrossLinking And Streptavidin Purification) as a method for both phosphosite-specific kinase identification and the discovery of kinase interacting proteins. K-CLASP offers a ...[more]

PMID: 27726338

Similar Datasets

Kinase inhibition profiles as a tool to identify kinases for specific phosphorylation sites.

Project description:There are thousands of known cellular phosphorylation sites, but the paucity of ways to identify kinases for particular phosphorylation events remains a major roadblock for understanding kinase signaling. To address this, we here develop a generally applicable method that exploits the large number of kinase inhibitors that have been profiled on near-kinome-wide panels of protein kinases. The inhibition profile for each kinase provides a fingerprint that allows identification of unknown kinases acting on target phosphosites in cell extracts. We validate the method on diverse known kinase-phosphosite pairs, including histone kinases, EGFR autophosphorylation, and Integrin β1 phosphorylation by Src-family kinases. We also use our approach to identify the previously unknown kinases responsible for phosphorylation of INCENP at a site within a commonly phosphorylated motif in mitosis (a non-canonical target of Cyclin B-Cdk1), and of BCL9L at S915 (PKA). We show that the method has clear advantages over in silico and genetic screening.

| S-EPMC7125195 | biostudies-literature

SOGA1 and SOGA2/MTCL1 are CLASP-interacting proteins required for faithful chromosome segregation in human cells.

Project description:CLASPs are key modulators of microtubule dynamics throughout the cell cycle. During mitosis, CLASPs independently associate with growing microtubule plus-ends and kinetochores and play essential roles in chromosome segregation. In a proteomic survey for human CLASP1-interacting proteins during mitosis, we have previously identified SOGA1 and SOGA2/MTCL1, whose mitotic roles remained uncharacterized. Here we performed an initial functional characterization of human SOGA1 and SOGA2/MTCL1 during mitosis. Using specific polyclonal antibodies raised against SOGA proteins, we confirmed their expression and reciprocal interaction with CLASP1 and CLASP2 during mitosis. In addition, we found that both SOGA1 and SOGA2/MTCL1 are phospho-regulated during mitosis by CDK1. Immunofluorescence analysis revealed that SOGA2/MTCL1 co-localizes with mitotic spindle microtubules and spindle poles throughout mitosis and both SOGA proteins are enriched at the midbody during mitotic exit/cytokinesis. GFP-tagging of SOGA2/MTCL1 further revealed a microtubule-independent localization at kinetochores. Live-cell imaging after siRNA-mediated knockdown of SOGA1 and SOGA2/MTCL1 showed that they are independently required for distinct aspects of chromosome segregation. Thus, SOGA1 and SOGA2/MTCL1 are bona fide CLASP-interacting proteins during mitosis required for faithful chromosome segregation in human cells.

| S-EPMC7610860 | biostudies-literature

SimPhospho: a software tool enabling confident phosphosite assignment.

Project description:Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance.We present SimPhospho, a fast and user-friendly tool for accurate simulation of phosphopeptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the Trans-Proteomic Pipeline and integrated in a phosphoproteomics data analysis workflow.SimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user's manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/.Supplementary data are available at Bioinformatics online.

| S-EPMC6061695 | biostudies-other

Interaction trap/two-hybrid system to identify interacting proteins.

Project description:This unit presents protocols designed to detect interacting proteins. Using yeast as a "test tube" and transcriptional activation of a reporter system, interacting proteins can be identified. The system can also be used to test complex formation for proteins for which there exists a reason to expect interaction.

| S-EPMC4095973 | biostudies-literature

DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases.

Project description:MotivationProtein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported.ResultsWe present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase-specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas.Availability and implementationThe source codes are available at https://github.com/Tastanlab/DeepKinZero.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7320620 | biostudies-literature

Multiparametric analysis of CLASP-interacting protein functions during interphase microtubule dynamics.

Project description:The microtubule (MT) plus-end tracking protein (+TIP) CLASP mediates dynamic cellular behaviors and interacts with numerous cytoplasmic proteins. While the influence of some CLASP interactors on MT behavior is known, a comprehensive survey of the proteins in the CLASP interactome as MT regulators is missing. Ultimately, we are interested in understanding how CLASP collaborates with functionally linked proteins to regulate MT dynamics. Here, we utilize multiparametric analysis of time-lapse MT +TIP imaging data acquired in Drosophila melanogaster S2R+ cells to assess the effects on individual microtubule dynamics for RNA interference-mediated depletion of 48 gene products previously identified to be in vivo genetic CLASP interactors. While our analysis corroborates previously described functions of several known CLASP interactors, its multiparametric resolution reveals more detailed functional profiles (fingerprints) that allow us to precisely classify the roles that CLASP-interacting genes play in MT regulation. Using these data, we identify subnetworks of proteins with novel yet overlapping MT-regulatory roles and also uncover subtle distinctions between the functions of proteins previously thought to act via similar mechanisms.

| S-EPMC3624261 | biostudies-literature

A Curated Resource for Phosphosite-specific Signature Analysis.

Project description:Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.

| S-EPMC6398202 | biostudies-literature

Irving Page Lecture: 5-HT(2A) serotonin receptor biology: interacting proteins, kinases and paradoxical regulation.

Project description:5-Hydroxytryptamine(2A) (5-HT(2A)) serotonin receptors are important pharmacological targets for a large number of central nervous system and peripheral serotonergic medications. In this review article I summarize work mainly from my lab regarding serotonin receptor anatomy, pharmacology, signaling and regulation. I highlight the role of serotonin receptor interacting proteins and the emerging paradigm of G-protein coupled receptor functional selectivity.

| S-EPMC3110590 | biostudies-literature

Modified yeast-two-hybrid system to identify proteins interacting with the growth factor progranulin.

Project description:Progranulin (PGRN), also known as granulin epithelin precursor (GEP), is a 593-amino-acid autocrine growth factor. PGRN is known to play a critical role in a variety of physiologic and disease processes, including early embryogenesis, wound healing, inflammation, and host defense. PGRN also functions as a neurotrophic factor, and mutations in the PGRN gene resulting in partial loss of the PGRN protein cause frontotemporal dementia. Our recent studies have led to the isolation of PGRN as an important regulator of cartilage development and degradation. Although PGRN, discovered nearly two decades ago, plays crucial roles in multiple physiological and pathological conditions, efforts to exploit the actions of PGRN and understand the mechanisms involved have been significantly hampered by our inability to identify its binding receptor(s). To address this issue, we developed a modified yeast two-hybrid (MY2H) approach based on the most commonly used GAL4 based 2-hybrid system. Compared with the conventional yeast two-hybrid screen, MY2H dramatically shortens the screen process and reduces the number of false positive clones. In addition, this approach is reproducible and reliable, and we have successfully employed this system in isolating the binding proteins of various baits, including ion channel, extracellular matrix protein, and growth factor. In this paper, we describe this MY2H experimental procedure in detail using PGRN as an example that led to the identification of TNFR2 as the first known PGRN-associated receptor.

| S-EPMC3462564 | biostudies-literature

Molecular Dynamics Simulation as a Tool to Identify Mutual Synergistic Folding Proteins.

Project description:Mutual synergistic folding (MSF) proteins belong to a recently emerged subclass of disordered proteins, which are disordered in their monomeric forms but become ordered in their oligomeric forms. They can be identified by experimental methods following their unfolding, which happens in a single-step cooperative process, without the presence of stable monomeric intermediates. Only a limited number of experimentally validated MSF proteins are accessible. The amino acid composition of MSF proteins shows high similarity to globular ordered proteins, rather than to disordered ones. However, they have some special structural features, which makes it possible to distinguish them from globular proteins. Even in the possession of their oligomeric three-dimensional structure, classification can only be performed based on unfolding experiments, which are frequently absent. In this work, we demonstrate a simple protocol using molecular dynamics simulations, which is able to indicate that a protein structure belongs to the MSF subclass. The presumption of the known atomic resolution quaternary structure is an obvious limitation of the method, and because of its high computational time requirements, it is not suitable for screening large databases; still, it is a valuable in silico tool for identification of MSF proteins.

| S-EPMC9861041 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data