PXD057757

PXD057757 is an original dataset announced via ProteomeXchange.

Title	diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling
Description	Protein-protein interactions (PPIs) underlie most biological functions. Devastating human conditions like cancers, neurological disorders, and infections, hijack PPI networks to initiate disease, and to drive disease progression. Understanding precisely how diseases remodel PPI networks can, therefore, help clarifying disease mechanisms and identifying therapeutic targets. Protein kinases control most cellular processes through protein phosphorylation. The 518 human kinases, known as the kinome, are frequently dysregulated in disease and highly druggable with ATP-competitive inhibitors. Kinase activity, localization, and substrate recognition are regulated by dynamic PPI networks composed of scaffolding and adapters proteins, other signaling enzymes like small GTPases and E3 ligases, and phospho-substrates. Accordingly, mapping kinase PPI networks can help determining kinome activation states, and, in turn, cellular activation states; this information can be used for studying kinase-mediated cell signaling, and for prioritizing kinases for drug discovery. Previously, we have developed a high-throughput method for kinome PPI mapping based on mass spectrometry (MS)-based chemoproteomics that we named kinobead competition and correlation analysis (kiCCA). Here, we introduce 2nd generation (gen) kiCCA which utilizes data independent acquisition (DIA) with parallel accumulation serial fragmentation (PASEF) MS and a re-designed CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. Using neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT), we demonstrate that 2nd gen kiCCA (1) identified 6.1-times more kinase PPIs in native cell extracts compared to our 1st gen approach, (2) determined kinase-mediated signaling pathways that underly the neuroblastoma NMT, and (3) accurately predicted pharmacological targets for manipulating NMT states. Our 2nd gen kiCCA method is broadly useful for cell signaling research and kinase drug discovery.
HostingRepository	MassIVE
AnnounceDate	2025-02-06
AnnouncementXML	Submission_2025-02-06_16:02:04.768.xml
DigitalObjectIdentifier
ReviewLevel	Non peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Martin Golkowski
SpeciesList	scientific name: Homo sapiens; common name: human; NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	timsTOF Pro 2

Revision	Datetime	Status	ChangeLog Entry
0	2024-11-11 11:45:54	ID requested
⏵ 1	2025-02-06 16:02:05	announced

no publication

submitter keyword: protein kinase, protein-protein interaction, chemoproteomics, kinase inhibitor, anaplastic lymphoma kinase, neuroblastoma, plasticity, noradrenergic-mesenchymal transition, DatasetType:Proteomics

Martin Golkowski
contact affiliation	University of Utah
contact email	martin.golkowski@utah.edu
lab head
Martin Golkowski
contact affiliation	University of Washington
contact email	golkom@uw.edu
dataset submitter

MassIVE dataset URI

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://massive.ucsd.edu/v07/MSV000096379/