Native Mass Spectrometry Reveals Ligand-Specific Conformational Heterogeneity of the β1-Adrenergic Receptor Using Nanobody Probes

Abstract

G protein-coupled receptors (GPCRs) are central physiological signal transducers and represent a primary class of therapeutic targets. While emerging evidence highlights a critical link between ligand-induced receptor conformations and pharmacological outcomes, the structural diversity dictating GPCR-G protein interface interactions remains incompletely understood. High-resolution structural studies reveal discrete, ligand-stabilized states, but these static snapshots do not fully capture the dynamic processes underlying GPCR activation and ligand efficacy. To address this, we established a native mass spectrometry (nMS) platform to probe ligand-induced conformational heterogeneity at the GPCR intracellular interface. Using the β1-adrenergic receptor (β1AR) as a model, we demonstrated a strong correlation between ligand efficacy and receptor complex formation with diverse active-state-specific nanobodies. Notably, leveraging the quantitative capacity of nMS, we uncovered distinct propensities of two highly similar nanobodies, Nb80 and Nb6B9, and showed that Nb6B9 engages weak-agonist-stabilized receptor states more effectively than Nb80. These interactions indicate that Nb6B9 recognizes ligand-specific conformations distinct from active states induced by full agonists. To gain structural insights, we utilized Nb6B9 chimeras with exchanged complementarity-determining regions (CDRs) and identified CDR1 and CDR3 as the primary drivers for ligand-specific recognition, implicating the involvement of receptor transmembrane motifs TM5 and TM6 in this conformational diversity. Finally, we demonstrated that nanobody-based nMS can distinguish receptor responses induced by the two salbutamol enantiomers, implying stereochemistry-dependent intracellular interface heterogeneity. Together, our study highlights the utility of nanobody-based nMS for evaluating the structural heterogeneity and dynamic landscapes of GPCRs.