Encoding mu-opioid receptor biased agonism with interaction fingerprints

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 D3.32, Y7.43, and Y3.33. 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.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.