Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions.

Area of Science:

• Pharmacology
• Proteomics
• Drug Discovery

Background:

• Target deconvolution is essential for drug discovery but is often expensive and slow.
• Current methods limit large-scale drug profiling and target identification.
• Efficiently identifying drug targets across diverse biological contexts remains a challenge.

Purpose of the Study:

• To introduce a novel Matrix-Augmented Pooling Strategy (MAPS) for simultaneous drug target deconvolution.
• To enhance experimental throughput and reduce costs in large-scale drug profiling.
• To investigate drug-target interactions across multiple cell lines.

Main Methods:

• Developed MAPS, a matrix-augmented pooling strategy involving optimized drug permutations in samples.
• Applied MAPS with thermal proteome profiling (TPP) for concurrent testing of 15 drugs.
• Performed target deconvolution across 5 different cell lines using the MAPS strategy.

Main Results:

• MAPS increased experimental throughput by 60x compared to traditional methods while maintaining sensitivity and specificity.
• Drug-target interactions and binding affinities were found to vary significantly across different cell lines.
• Identified BRAF and CSNK2A2 as potential off-targets for bafetinib and abemaciclib, respectively.

Conclusions:

• MAPS offers a cost-effective and high-throughput solution for drug target deconvolution.
• The study highlights the cell-line-specific nature of drug-target interactions.
• This work represents a significant advancement in large-scale thermal profiling for drug discovery.