Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane.

The D2 dopamine receptor (DRD2) is a therapeutic target for Parkinson's disease1 and antipsychotic drugs2. DRD2 is activated by the endogenous neurotransmitter dopamine and synthetic agonist drugs such as bromocriptine3, leading to stimulation of Gi and inhibition of adenylyl cyclase. Here we used cryo-electron microscopy to elucidate the structure of an agonist-bound activated DRD2-Gi complex reconstituted into a phospholipid membrane. The extracellular ligand-binding site of DRD2 is remodelled in response to agonist binding, with conformational changes in extracellular loop 2, transmembrane domain 5 (TM5), TM6 and TM7, propagating to opening of the intracellular Gi-binding site. The DRD2-Gi structure represents, to our knowledge, the first experimental model of a G-protein-coupled receptor-G-protein complex embedded in a phospholipid bilayer, which serves as a benchmark to validate the interactions seen in previous detergent-bound structures. The structure also reveals interactions that are unique to the membrane-embedded complex, including helix 8 burial in the inner leaflet, ordered lysine and arginine side chains in the membrane interfacial regions, and lipid anchoring of the G protein in the membrane. Our model of the activated DRD2 will help to inform the design of subtype-selective DRD2 ligands for multiple human central nervous system disorders.

Computational design of ligand-binding membrane receptors with high selectivity.

Accurate modeling and design of protein-ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding. In structure modeling tests, we blindly predicted, with near-atomic accuracy, ligand conformations bound to G-protein-coupled receptors (GPCRs) that have rarely been identified using traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally uncharacterized GPCRs. We then applied this technique to design functional human dopamine receptors with novel ligand-binding selectivity. Most blindly predicted ligand-binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand-binding profile of membrane receptors that remain difficult to crystallize.

High-resolution modeling of transmembrane helical protein structures from distant homologues.

Eukaryotic transmembrane helical (TMH) proteins perform a wide diversity of critical cellular functions, but remain structurally largely uncharacterized and their high-resolution structure prediction is currently hindered by the lack of close structural homologues. To address this problem, we present a novel and generic method for accurately modeling large TMH protein structures from distant homologues exhibiting distinct loop and TMH conformations. Models of the adenosine A2AR and chemokine CXCR4 receptors were first ranked in GPCR-DOCK blind prediction contests in the receptor structure accuracy category. In a benchmark of 50 TMH protein homolog pairs of diverse topology (from 5 to 12 TMHs), size (from 183 to 420 residues) and sequence identity (from 15% to 70%), the method improves most starting templates, and achieves near-atomic accuracy prediction of membrane-embedded regions. Unlike starting templates, the models are of suitable quality for computer-based protein engineering: redesigned models and redesigned X-ray structures exhibit very similar native interactions. The method should prove useful for the atom-level modeling and design of a large fraction of structurally uncharacterized TMH proteins from a wide range of structural homologues.