End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.

Area of Science:

• Chemical Proteomics
• Drug Discovery
• Biochemistry

Background:

• Photoaffinity labeling (PAL) is crucial for identifying protein-ligand interactions but is often limited by time-consuming sample preparation and analysis.
• Existing chemical proteomic workflows require extensive manual manipulation and lengthy data acquisition.
• There is a need for more efficient and automated methods to deconvolute complex protein-ligand binding events.

Purpose of the Study:

• To develop a semiautomated, plate-based workflow for photoaffinity labeling sample processing.
• To improve the speed and efficiency of identifying protein targets of small molecules.
• To enhance the sensitivity and reliability of chemical proteomics studies.

Main Methods:

• Implemented a carboxylate bead-based protein cleanup procedure to remove small-molecule contaminants.
• Coupled the cleanup method with plate-based, semiautomated sample processing.
• Utilized label-free, data-independent acquisition (DIA) mass spectrometry for sample analysis.
• Tested the workflow with known photoaffinity labeling ligands: (+)-JQ-1, lenalidomide, and dasatinib.

Main Results:

• Achieved significant improvements in workflow time per sample compared to standard practices.
• Demonstrated confident identification and rank ordering of known and putative protein targets.
• Showcased outstanding protein signal-to-background enrichment sensitivity.
• Validated the workflow's flexibility for experiments with diverse variables.

Conclusions:

• The developed carboxylate bead-based cleanup and semiautomated processing workflow accelerates photoaffinity labeling analysis.
• This approach offers a robust and efficient strategy for drug discovery and chemical proteomics.
• The unified end-to-end throughput enhances the ability to identify and characterize protein-ligand interactions.