Toward Predictive Models of Biased Agonists of the Mu Opioid Receptor

The mu-opioid receptor (MOR), a member of the G-protein-coupled receptor superfamily, is pivotal in pain modulation and analgesia. Biased agonism at MOR offers a promising avenue for developing safer opioid therapeutics by selectively engaging specific signaling pathways. This study presents a comprehensive analysis of biased agonists using a newly curated database, BiasMOR, comprising 166 unique molecules with annotated activity data for GTPγS, cAMP, and β-arrestin assays. Advanced structure-activity relationship (SAR) analyses, including network similarity graphs, maximum common substructures, and activity cliff identification, reveal critical molecular features underlying bias signaling. Modelability assessments indicate high suitability for predictive modeling, with RMODI indices exceeding 0.96 and SARI indices highlighting moderately continuous SAR landscapes for cAMP and β-arrestin assays. Interaction patterns for biased agonists are discussed, including key residues such as D^3.32, Y^7.43, and Y^3.33. Comparative studies of enantiomer-specific interactions further underscore the role of ligand-induced conformational states in modulating signaling pathways. This work underscores the potential of combining computational and experimental approaches to advance the understanding of MOR-biased signaling, paving the way for safer opioid therapies. The database provided here will serve as a starting point for designing biased mu opioid receptor ligands and will be updated as new data become available. Increasing the repertoire of biased ligands and analyzing molecules collectively, as the database described here, contributes to pinpointing structural features responsible for biased agonism that can be associated with biological effects still under debate.