Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of...

Area of Science:

• Biochemistry
• Proteomics
• Drug Discovery

Background:

• Understanding protein-protein interactions is key for mechanism elucidation and selective inhibitor development.
• Current methods for mapping binding selectivity are often laborious and lack comprehensiveness.

Purpose of the Study:

• To introduce a novel, efficient strategy for comprehensively mapping protein binding landscapes.
• To dissect and improve the affinity and selectivity of amyloid protein precursor inhibitor (APPI) variants for specific human serine proteases.

Main Methods:

• Combined experimental multi-target selective library screening with in silico next-generation sequencing analysis.
• Mapped the binding landscape of APPI against four human serine proteases: kallikrein-6, mesotrypsin, and anionic and cationic trypsins.

Main Results:

• Successfully mapped the binding landscape of a non-selective trypsin inhibitor (APPI) to four distinct human serine proteases.
• Demonstrated the ability to dissect and enhance APPI's affinity and selectivity towards individual proteases using the generated map.

Conclusions:

• The developed strategy provides a comprehensive and efficient method for mapping protein binding landscapes.
• This platform facilitates the development of next-generation target-selective probes and therapeutic agents based on protein-protein interactions.