Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

Area of Science:

• Biochemistry
• Molecular Biology
• Systems Biology

Background:

• Protein phosphorylation is a critical regulatory mechanism in cellular signaling.
• Dysregulated phosphorylation is implicated in various diseases, including cancer.
• Current methods for analyzing global phosphorylation patterns can be limited.

Purpose of the Study:

• To develop a novel protein array system, Phospho-Totum, for comprehensive analysis of sample phosphorylation states.
• To utilize artificial intelligence (AI) for advanced characterization of phosphorylation data.
• To explore applications in elucidating disease mechanisms and drug discovery.

Main Methods:

• Developed the Phospho-Totum protein array containing 1471 proteins from 273 signaling pathways.
• Replicated sample-specific phosphorylation states on the array based on tyrosine kinase activation.
• Employed AI-assisted tools for pathway and kinase activation estimation, and inhibitor screening.

Main Results:

• Successfully reproduced sample phosphorylation states on a large-scale protein array.
• Enabled measurement of phosphorylation levels for 1471 substrates.
• Generated AI-driven insights into pathway activation, kinase activity, and potential therapeutic targets.

Conclusions:

• The Phospho-Totum system offers a powerful platform for studying phosphorylation dynamics.
• This technology can elucidate disease pathophysiology by mirroring sample phosphorylation.
• It holds significant potential for drug discovery, particularly in identifying kinase inhibitors.