Thyrotropin releasing hormone (TRH) selectively binds and activates the melanocortin 1 receptor.

We tested the endogenous tripeptide TRH (thyrotropin releasing hormone) ability to bind to MC (melanocortin) receptor subtypes. We discovered that TRH binds to the human MCI receptor expressed in COS cells and to murine B16 melanoma cells with 5790+/-1010 nM and 6370+/-1260 nM Ki's, respectively. TRH did not bind to the human MC3, MC4 or MC5 receptor subtypes. Moreover, TRH also stimulated cAMP production in murine B16 melanoma cells reaching the same maximum level of cAMP as found for alpha-MSH. However, several analogues of TRH, including TRH-OH, TRH-Gly-NH2 and other analogues, where each of the three amino acid residues in TRH had been exchanged by a related residue, did not bind to any of the MC receptors tested in this study. C(alpha) atoms of molecular models of TRH and the core of a MSH peptide were aligned with r.m.s. of 0.01 A. Moreover, TRH could be docked into a binding pocket of a molecular model of the MC1 receptor at only a little higher energy than a short cyclic MSH peptide. The data indicate a similarity in the mode of TRH and MSH activation of the MCI receptor.     

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

Background  

Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.     

ApoB100-LDL Acts as a Metabolic Signal from Liver to Peripheral Fat Causing Inhibition of Lipolysis in Adipocytes

Background

Free fatty acids released from adipose tissue affect the synthesis of apolipoprotein B-containing lipoproteins and glucose metabolism in the liver. Whether there also exists a reciprocal metabolic arm affecting energy metabolism in white adipose tissue is unknown.

Methods and Findings

We investigated the effects of apoB-containing lipoproteins on catecholamine-induced lipolysis in adipocytes from subcutaneous fat cells of obese but otherwise healthy men, fat pads from mice with plasma lipoproteins containing high or intermediate levels of apoB100 or no apoB100, primary cultured adipocytes, and 3T3-L1 cells. In subcutaneous fat cells, the rate of lipolysis was inversely related to plasma apoB levels. In human primary adipocytes, LDL inhibited lipolysis in a concentration-dependent fashion. In contrast, VLDL had no effect. Lipolysis was increased in fat pads from mice lacking plasma apoB100, reduced in apoB100-only mice, and intermediate in wild-type mice. Mice lacking apoB100 also had higher oxygen consumption and lipid oxidation. In 3T3-L1 cells, apoB100-containing lipoproteins inhibited lipolysis in a dose-dependent fashion, but lipoproteins containing apoB48 had no effect. ApoB100-LDL mediated inhibition of lipolysis was abolished in fat pads of mice deficient in the LDL receptor (Ldlr^−/−Apob ^100/100).

Conclusions

Our results show that the binding of apoB100-LDL to adipocytes via the LDL receptor inhibits intracellular noradrenaline-induced lipolysis in adipocytes. Thus, apoB100-LDL is a novel signaling molecule from the liver to peripheral fat deposits that may be an important link between atherogenic dyslipidemias and facets of the metabolic syndrome.     

Relationships between kinetic constants and the amino acid composition of enzymes from the yeast Saccharomyces cerevisiae glycolysis pathway

The kinetic models of metabolic pathways represent a system of biochemical reactions in terms of metabolic fluxes and enzyme kinetics. Therefore, the apparent differences of metabolic fluxes might reflect distinctive kinetic characteristics, as well as sequence-dependent properties of the employed enzymes. This study aims to examine possible linkages between kinetic constants and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolytic pathway. The values of Michaelis-Menten constant (KM), turnover number (kcat), and specificity constant (ksp = kcat/KM) were taken from BRENDA (15, 17, and 16 values, respectively) and protein sequences of nine enzymes (HXK, GADH, PGK, PGM, ENO, PK, PDC, TIM, and PYC) from UniProtKB. The AAC and sequence properties were computed by ExPASy/ProtParam tool and data processed by conventional methods of multivariate statistics. Multiple linear regressions were found between the log-values of kcat (3 models, 85.74% < Radj.2 <94.11%, p < 0.00001), KM (1 model, Radj.2 = 96.70%, p < 0.00001), ksp (3 models, 96.15% < Radj.2 < 96.50%, p < 0.00001), and the sets of AA frequencies (four to six for each model) selected from enzyme sequences while assessing the potential multicollinearity between variables. It was also found that the selection of independent variables in multiple regression models may reflect certain advantages for definite AA physicochemical and structural propensities, which could affect the properties of sequences. The results support the view on the actual interdependence of catalytic, binding, and structural residues to ensure the efficiency of biocatalysts, since the kinetic constants of the yeast enzymes appear as closely related to the overall AAC of sequences.     

Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases

The prime side specificity of dengue protease substrates was investigated by use of proteochemometrics, a technology for drug target interaction analysis. A set of 48 internally quenched peptides were designed using statistical molecular design (SMD) and assayed with proteases of four subtypes of dengue virus (DEN-1-4) for Michaelis (K(m)) and cleavage rate constants (k(cat)). The data were subjected to proteochemometrics modeling, concomitantly modeling all peptides on all the four dengue proteases, which yielded highly predictive models for both activities. Detailed analysis of the models then showed that considerably differing physico-chemical properties of amino acids contribute independently to the K(m) and k(cat) activities. For k(cat), only P1' and P2' prime side residues were important, while for K(m) all four prime side residues, P1'-P4', were important. The models could be used to identify amino acids for each P' substrate position that are favorable for, respectively, high substrate affinity and cleavage rate.     

Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 cell surface hydrophobicity and survival of the cells under adverse environmental conditions

Changes in the cell surface hydrophobicity (CSH) of probiotic bacteria Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 and the survival of these cells were examined in response to varied cultivation conditions and adverse environmental conditions. An inverse linear relationship (P < 0.01) was detected between the CSH of intact L. acidophilus La5 and B. lactis Bb12 and survival of cells subjected to subsequent freezing/thawing, long-term storage or exposure to mineral and bile acids. The observed relationships were supported by significant correlations between the CSH and changes in composition of the cell envelopes (proteins, lipids and carbohydrates) of L. acidophilus La5 and B. lactis Bb12 examined using FT-IR spectroscopy and conventional biochemical analysis methods. The results also suggest that the estimates of hydrophobicity, being a generalized characteristic of cell surfaces, are important parameters to predict the ability of intact probiotic bacteria to endure extreme environments and therefore should be monitored during cultivation. A defined balance of cell components, which can be characterized by the reduced CSH values, apparently helps to ensure the resistance, improved viability and hence the overall probiotic properties of bacteria.