Deep Receptor Scanning Reveals General Sequence Constraints on GPCR Biosynthesis

Area of Science:

• Biochemistry
• Pharmacology
• Genomics

Background:

• G protein-coupled receptors (GPCRs) are crucial signaling molecules and major drug targets.
• Many GPCRs remain understudied despite their importance.
• Existing methods are insufficient for large-scale GPCR characterization.

Purpose of the Study:

• To develop and validate a high-throughput platform for comprehensive GPCR characterization.
• To quantitatively assess GPCR transcript abundance, translation, and cell surface expression.
• To identify structural determinants of GPCR expression using machine learning.

Main Methods:

• A "deep receptor scanning" platform was established to analyze 766 human GPCRs and 174 splice variants in parallel.
• Quantitative characterization of canonical and alternative receptor transcripts, translational efficiency, and plasma membrane expression.
• Machine learning algorithms were applied to identify structural features modulating GPCR expression.

Main Results:

• The platform successfully characterized a large number of human GPCRs and their variants.
• Data revealed insights into transcript abundance, translational efficiency, and cell surface expression patterns.
• Machine learning identified key structural features influencing GPCR expression levels.

Conclusions:

• The deep receptor scanning platform offers a versatile tool for efficient GPCRome exploration.
• This approach facilitates the study of underrepresented GPCRs and their functional properties.
• The findings provide a foundation for future drug discovery targeting GPCRs.