Presence of transient helical segments in the galanin-like peptide evident from 1H NMR, circular dichroism, and prediction studies

Brain and intestinal expression of galanin-like peptide (GALP), galanin receptor R1 and galanin receptor R2, and GALP regulation of food intake in goldfish (Carassius auratus)

Galanin-like peptide (GALP) is a 60 amino acid neuropeptide originally discovered from porcine hypothalamus, and is involved in the regulation of food intake in mammals. Since its discovery, GALP and its receptors (GALR1 and GALR2) have been characterized in mammals, but no publications are available on GALP in fish and other non-mammals. The present study aimed to characterize brain and intestinal GALP and its receptors using immunohistochemistry in a teleost, the goldfish (Carassius auratus), and to study its effects on feeding behavior. Immunostaining of brain sections shows the presence of GALP- and GALR1- and GALR2-like immunoreactive cells in different encephalic areas, including the telencephalon, some hypothalamic nuclei, the optic tectum, the torus longitudinalis and the cerebellum. Signal for GALP was also observed in the fasciculus retroflexus. In the gut, GALP-and GALR1 and GALR2 immunoreactive cells were detected in the mucosa. Results from the feeding study demonstrate that intracerebroventricular administration of GALP (1ng/g bodyweight) increases goldfish food intake at 1h post-injection. These observations form the first report on the presence of GALP in the fish brain and gut, and also on its modulatory role on fish feeding behavior. GALP, as in mammals, appears to be a functional neuropeptide in goldfish.

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.

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.

Analysis of structured and intrinsically disordered regions of transmembrane proteins

Integral membrane proteins display two major types of transmembrane structure, helical bundles and beta barrels. The main functional roles of transmembrane proteins are the transport of small molecules and cell signaling, and sometimes these two roles are coupled. For cytosolic, water-soluble proteins, signaling and regulatory functions are often carried out by intrinsically disordered regions. Our long range goal is to determine whether integral membrane proteins likewise use disordered regions for signaling and regulation. Here we carried out a systematic bioinformatics investigation of intrinsically disordered regions obtained from integral membrane proteins for which crystal structures have been determined, and for which the intrinsic disorder was identified as missing electron density. We found 120 disorder-containing integral membrane proteins having a total of 33675 residues, with 3209 of the residues distributed among 240 different disordered regions. These disordered regions were compared with those obtained from water-soluble proteins with regards to their amino acid compositional biases, and to the accuracies of various disorder predictors. The results of these analyses show that the disordered regions from helical bundle integral membrane proteins, those from beta barrel integral membrane proteins, and those from water soluble proteins all exhibit statistically distinct amino acid compositional biases. Despite these differences in composition, current algorithms make reasonably accurate predictions of disorder for these membrane proteins. Although the small size of the current data sets are limiting, these results suggest that developing new predictors that make use of data from disordered regions in helical bundles and beta barrels, especially as these datasets increase in size, will likely lead to significantly more accurate disorder predictions for these two classes of integral membrane proteins.