Modulating and evaluating receptor promiscuity through directed evolution and modeling

Abstract

The promiscuity of G-protein-coupled receptors (GPCRs) has broad implications in disease, pharmacology and biosensing. Promiscuity is a particularly crucial consideration for protein engineering, where the ability to modulate and model promiscuity is essential for developing desirable proteins. Here, we present methodologies for (i) modifying GPCR promiscuity using directed evolution and (ii) predicting receptor response and identifying important peptide features using quantitative structure-activity relationship models and grouping-exhaustive feature selection. We apply these methodologies to the yeast pheromone receptor Ste2 and its native ligand α-factor. Using directed evolution, we created Ste2 mutants with altered specificity toward a library of α-factor variants. We then used the Vectors of Hydrophobic, Steric, and Electronic properties and partial least squares regression to characterize receptor-ligand interactions, identify important ligand positions and properties, and predict receptor response to novel ligands. Together, directed evolution and computational analysis enable the control and evaluation of GPCR promiscuity. These approaches should be broadly useful for the study and engineering of GPCRs and other protein-small molecule interactions.