Integrating solid-state NMR and computational modeling to investigate the structure and dynamics of membrane-associated ghrelin.

The peptide hormone ghrelin activates the growth hormone secretagogue receptor 1a, also known as the ghrelin receptor. This 28-residue peptide is acylated at Ser3 and is the only peptide hormone in the human body that is lipid-modified by an octanoyl group. Little is known about the structure and dynamics of membrane-associated ghrelin. We carried out solid-state NMR studies of ghrelin in lipid vesicles, followed by computational modeling of the peptide using Rosetta. Isotropic chemical shift data of isotopically labeled ghrelin provide information about the peptide's secondary structure. Spin diffusion experiments indicate that ghrelin binds to membranes via its lipidated Ser3. Further, Phe4, as well as electrostatics involving the peptide's positively charged residues and lipid polar headgroups, contribute to the binding energy. Other than the lipid anchor, ghrelin is highly flexible and mobile at the membrane surface. This observation is supported by our predicted model ensemble, which is in good agreement with experimentally determined chemical shifts. In the final ensemble of models, residues 8-17 form an α-helix, while residues 21-23 and 26-27 often adopt a polyproline II helical conformation. These helices appear to assist the peptide in forming an amphipathic conformation so that it can bind to the membrane.

Small-molecule ligand docking into comparative models with Rosetta.

Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models.

The activity of prolactin releasing peptide correlates with its helicity.

The prolactin releasing peptide (PrRP) is involved in regulating food intake and body weight homeostasis, but molecular details on the activation of the PrRP receptor remain unclear. C-terminal segments of PrRP with 20 (PrRP20) and 13 (PrRP8-20) amino acids, respectively, have been suggested to be fully active. The data presented herein indicate this is true for the wildtype receptor only; a 5-10-fold loss of activity was found for PrRP8-20 compared to PrRP20 at two extracellular loop mutants of the receptor. To gain insight into the secondary structure of PrRP, we used CD spectroscopy performed in TFE and SDS. Additionally, previously reported NMR data, combined with ROSETTANMR, were employed to determine the structure of amidated PrRP20. The structural ensemble agrees with the spectroscopic data for the full-length peptide, which exists in an equilibrium between α- and 3(10)-helix. We demonstrate that PrRP8-20's reduced propensity to form an α-helix correlates with its reduced biological activity on mutant receptors. Further, distinct amino acid replacements in PrRP significantly decrease affinity and activity but have no influence on the secondary structure of the peptide. We conclude that formation of a primarily α-helical C-terminal region of PrRP is critical for receptor activation.

Ligand-mimicking receptor variant discloses binding and activation mode of prolactin-releasing peptide.

The prolactin-releasing peptide receptor and its bioactive RF-amide peptide (PrRP20) have been investigated to explore the ligand binding mode of peptide G-protein-coupled receptors (GPCRs). By receptor mutagenesis, we identified the conserved aspartate in the upper transmembrane helix 6 (Asp(6.59)) of the receptor as the first position that directly interacts with arginine 19 of the ligand (Arg(19)). Replacement of Asp(6.59) with Arg(19) of PrRP20 led to D6.59R, which turned out to be a constitutively active receptor mutant (CAM). This suggests that the mutated residue at the top of transmembrane helix 6 mimics Arg(19) by interacting with additional binding partners in the receptor. Next, we generated an initial comparative model of this CAM because no ligand docking was required, and we selected the next set of receptor mutants to find the engaged partners of the binding pocket. In an iterative process, we identified two acidic residues and two hydrophobic residues that form the peptide ligand binding pocket. As all residues are localized on top or in the upper part of the transmembrane domains, we clearly can show that the extracellular surface of the receptor is sufficient for full signal transduction for prolactin-releasing peptide, rather than a deep, membrane-embedded binding pocket. This contributes to the knowledge of the binding of peptide ligands to GPCRs and might facilitate the development of GPCR ligands, but it also provides new targeting of CAMs involved in hereditary diseases.