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- Ploj, Karolina, et al.
(författare)
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Effects of melanocortin receptor ligands on ethanol intake and opioid peptide levels in alcohol-preferring AA rats
- 2002
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Ingår i: Brain Research Bulletin. - 0361-9230 .- 1873-2747. ; 59:2, s. 97-104
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Tidskriftsartikel (refereegranskat)abstract
- Melanocortin (MC) peptides are suggested to play a role in opiate dependence, where they antagonise the addictive properties of opiates. To further study the involvement of the MCs in drug dependence, we analysed the effects of the MC(4)-receptor antagonist HS014 (1 nmol/rat), and the non-selective MC-receptor agonist MTII (1 nmol/rat), using i.c.v. administration, on ethanol intake in alcohol-preferring AA rats. The rats had access to ethanol during 40 days, resulting in a mean ethanol intake of 6.6 g/kg/day, before treatment. One group received only artificial cerebrospinal fluid solution. MTII caused a reduction in ethanol intake and ethanol preference, whereas HS014 was without effect. No effect on water intake was observed. A decrease in food intake was detected after MTII, whereas HS014 induced an increase in food intake. Analysis of dynorphin B and Met-enkephalin-Arg(6)Phe(7) immunoreactive levels revealed that MTII and HS014 altered opioid peptide levels in several brain areas and the pituitary gland of the rats with an established ethanol intake. This is the first report showing that manipulation of the MC-receptor system changes ethanol intake in chronically ethanol-drinking AA rats. In addition, manipulation of the MC system modulates ethanol-induced changes in opioid peptide levels.
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- Prusis, Peteris, et al.
(författare)
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PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand–receptor interactions
- 2001
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Ingår i: Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. ; 1544:1-2, s. 350-7
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Tidskriftsartikel (refereegranskat)abstract
- A novel method has been developed for the analysis of ligand–receptor interactions. The method utilizes binding data generated from the analysis of chimeric proteins with chimeric peptides. To each chimeric part of the peptide and receptor are assigned descriptors, thus creating a matrix of X descriptors. These descriptors are then correlated with the experimentally determined interaction binding affinities for each chimeric receptor/peptide pair by use of partial least-squares projection to latent structures (PLS). The method was applied to analyze the interactions of chimeric MSH-peptides with wild-type MC1 and MC3 receptors, and MC1/MC3 receptor chimeras (in total 40 peptide–receptor combinations). Two types of PLS models could be created, one that revealed the relationships between receptor and peptide structure and peptide binding pKi values (i.e., affinity) (R2 and Q2 being 0.71 and 0.62, respectively), and another that revealed the relationships between peptide and receptor structure and peptide–receptor selectivity (R2 and Q2 being 0.64 and 0.57, respectively). After addition of cross-terms these models improved significantly; the R2 and Q2 being 0.93 and 0.75 for affinity, and 0.92 and 0.72 for selectivity, respectively. The analysis shows that the high affinity of the MSH-peptides is primarily achieved by interactions of the peptides’ C-terminal amino acids with TM2 and TM3 of the receptor, and, to a lesser extent, by the interaction of the N-terminus with TM1, TM2 and TM3 of the receptor. However, in contrast, the MC1 receptor selectivity is primarily determined by an interaction of the peptides’ N-termini with TM2/3 of the receptor. Moreover, the cross-terms of the PLS model revealed the existence of a strong interaction between TM6/7 and TM2/3 of the receptors.
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- Strömbergsson, Helena, et al.
(författare)
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Proteochemometrics modelling of receptor ligand interactions using rough sets
- 2004
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Ingår i: Proceedings of the German conference on Bioinformatics. - 3885793822 ; , s. 85-94
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Konferensbidrag (refereegranskat)abstract
- We report on a model for the interaction of chimeric melanocortin G-protein coupled receptors with peptide ligands using the rough set approach. Rough sets generate If-Then rule models using Boolean reasoning. Two separate datasets have been analyzed, for which the binding affinities have previously been measured experimentally. The receptors and ligands are described by vectors of strings. Different partitions of each dataset were evaluated in order to find an optimal partition into rough set decision classes. To obtain a measurement of the accuracy of the rough set classifier generated from each dataset, a 10-fold cross validation (CV) was performed. The Area Under Curve (AUC) was calculated for each iteration during CV. This resulted in an AUC mean of 0.94 (SD 0.12) and 0.93 (SD 0.16) for the first and second dataset respectively. The CV results show that the rough set models exhibit a high classification quality. The decision rules generated from the rough set model inductions are easy to interpret. We apply this information to develop models of the interaction between ligands and receptors.
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