Corticotropin-releasing factor-binding protein ligand inhibitor blunts excessive weight gain in genetically obese Zucker rats and rats during nicotine withdrawal

Elevation of the neuropeptide corticotropin-releasing factor (CRF) in the brain is associated with a reduction of food intake and body weight gain in normal and obese animals. A protein that binds CRF and the related peptide, urocortin, with high affinity, CRF-binding protein (CRF-BP), may play a role in energy homeostasis by inactivating members of this peptide family in ingestive and metabolic regulatory brain regions. Intracerebroventricular administration in rats of the high-affinity CRF-BP ligand inhibitor, rat/human CRF (6-33), which dissociates CRF or urocortin from CRF-BP and increases endogenous brain levels of “free” CRF or urocortin significantly blunted exaggerated weight gain in Zucker obese subjects and in animals withdrawn from chronic nicotine. Chronic administration of CRF suppressed weight gain nonselectively by 60% in both Zucker obese and lean control rats, whereas CRF-BP ligand inhibitor treatment significantly reduced weight gain in obese subjects, without altering weight gain in lean control subjects. Nicotine abstinent subjects, but not nicotine-naive controls, experienced a 35% appetite suppression and a 25% weight gain reduction following acute and chronic administration, respectively, of CRF-BP ligand inhibitor. In marked contrast to the effects of a CRF-receptor agonist, the CRF-BP ligand inhibitor did not stimulate adrenocorticotropic hormone secretion or elevate heart rate and blood pressure. These results provide support for the hypothesis that the CRF-BP may function within the brain to limit selected actions of CRF and/or urocortin. Furthermore, CRF-BP may represent a novel and functionally selective target for the symptomatic treatment of excessive weight gain associated with obesity of multiple etiology.

Top-down free-energy minimization on protein potential energy landscapes

The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a β-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.