KAEA

KAEA infers kinase-signaling networks from phosphoproteomic data to enable accurate inference of kinase activities and identification of therapeutic response biomarkers.

Key Features:

• Phosphopeptide enrichment and LC-MS/MS: Employs titanium dioxide phosphopeptide enrichment followed by liquid chromatography tandem mass spectrometry (LC-MS) for comprehensive detection and quantification of phosphorylation events.
• SetRank enrichment algorithm: Applies the SetRank algorithm to analyze differential phosphoproteomics profiles with reduced false positive rates for kinase activity inference.
• Extensive kinase-substrate reference database: Integrates five experimentally validated kinase-substrate meta-databases with NetworKIN in-silico predictions to support kinase-substrate associations.
• Application to myeloid cell lines: Demonstrated on human myeloid cell lines K562, NB4, THP1, OCI-AML3, MOLM13, and MV4-11 exposed to kinase inhibitors Nilotinib and Midostaurin to identify direct and indirect targets.
• Kinase activity characterization: Identifies over- and under-active kinases in unperturbed and perturbed myeloid cell lines to map signaling network changes.

Scientific Applications:

• Translational cancer research: Elucidates kinase-signaling network dynamics in myeloid malignancies from phosphoproteomic data.
• Biomarker discovery: Supports identification of kinase activity signatures predictive of treatment response and resistance.
• Therapeutic target characterization: Enables detection of direct and indirect targets of kinase inhibitors, including Nilotinib and Midostaurin.
• Personalized medicine: Informs predictions of therapeutic outcomes based on phosphoproteomic kinase activity profiles.

Methodology:

Data are processed using the SetRank enrichment algorithm applied to differential phosphoproteomics profiles within an integrated kinase-substrate reference database that combines five experimentally validated meta-databases and NetworKIN predictions to infer kinase activities.