Background: G protein-coupled receptors (GPCRs) can signal in the absence of agonists through constitutive activity. This activity can be enhanced by mutations, resulting in receptors known as constitutively active mutants (CAMs). Such receptors are implicated in various physiological and pathophysiological conditions, and also offer significant therapeutic potential. However, the molecular basis of their constitutive activity remains unknown.

Results: To investigate how CAMs affect receptor activation, we employed enhanced sampling simulations to study the dopamine D2 receptor (D2R), a key target in central nervous system therapies. Free energy landscape analyses revealed that CAMs promote a conformational shift favoring an active state similar to the agonist-bound receptor. To then identify novel CAMs, we developed a comprehensive strategy combining structural comparison, in-silico residue scanning, and free energy calculations, validated by luminescence-complementation-based assays. Applied to D2R, this approach uncovered a new single-point CAM, D2R-I48^1.46W, which was functionally validated. Further investigation revealed that this mutation activates allosteric communication pathways primarily involving transmembrane helix 5, particularly Ser194^5.43, underscoring its role in transmitting activation signals to the intracellular domain.

Conclusions: This study elucidates how CAMs reshape the activation landscape of D2R and establishes a broadly applicable computational-experimental framework for discovering constitutively active GPCR variants. These CAMs provide valuable ligand-independent models for probing receptor activation mechanisms at structural, cellular, and physiological levels.

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the beta 2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the beta 2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the beta 2 adrenergic receptor.

Nanobody binding stabilizes G-protein-coupled receptors (GPCR) in a fully active state and modulates their affinity for bound ligands. However, the atomic-level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like beta 2 adrenergic receptor (beta 2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes structural features of the beta 2AR with an similar to 14 angstrom outward movement of transmembrane helix 6 and a close proximity of transmembrane (TM) helices 5 and 7, and favors the fully active-like conformation of the receptor, independent of ligand binding, in contrast to the conditions under which no intracellular binding partner is bound, in which case the receptor is only stabilized in an intermediateactive state. This activation is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intracellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding triggers tighter and stronger local communication networks between the Nb80 and the ligand-binding sites, primarily involving residues around ICL2 and the intracellular end of TM3, TM5, TM6, as well as ECL2, ECL3, and the extracellular ends of TM6 and TM7. In particular, we identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in conformations modulated by a full agonist, BI167107, and a G-protein-biased partial agonist, salmeterol, involving mainly TM1 and TM2, and TM5, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which should be taken into account in structure-based drug discovery.