Ligand induced interaction of thyroid hormone receptor beta with its coregulators

Thyroid hormones exert most of their physiological effects through two thyroid hormone receptor (TR) subtypes, TR alpha and TR beta, which associate with many transcriptional coregulators to mediate activation or repression of target genes. The search for selective TR beta ligands has been stimulated by the finding that several pharmacological actions mediated by TR beta might be beneficial in medical conditions such as obesity, hypercholesterolemia and diabetes. Here, we present a new methodology which employs surface plasmon resonance to investigate the interactions between TR beta ligand binding domain (LBD) complexes and peptides derived from the nuclear receptor interaction motifs of two of its coregulators, SRC2 and DAX1. The effect of several TR beta ligands, including the TR beta selective agonist GC-I and the TR beta selective antagonist NH-3, were investigated. We also determined the kinetic rate constants for the interaction of TR beta-T3 with both coregulators, and accessed the thermodynamic parameters for the interaction with DAX1. Our findings Suggest that flexibility plays an important role in the interaction between the receptor and its coregulators. and point out important aspects of experimental design that should be addressed when using TR beta LBD and its agonists. Furthermore, the methodology described here may be useful for the identification of new TR beta ligands. (C) 2008 Elsevier Ltd. All rights reserved.

Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

Structural modeling of high-affinity thyroid receptor-ligand complexes

Understanding the molecular basis of the binding modes of natural and synthetic ligands to nuclear receptors is fundamental to our comprehension of the activation mechanism of this important class of hormone regulated transcription factors and to the development of new ligands. Thyroid hormone receptors (TR) are particularly important targets for pharmaceuticals development because TRs are associated with the regulation of metabolic rates, body weight, and circulating levels of cholesterol and triglycerides in humans. While several high-affinity ligands are known, structural information is only partially available. In this work we obtain structural models of several TR-ligand complexes with unknown structure by docking high affinity ligands to the receptors` ligand binding domain with subsequent relaxation by molecular dynamics simulations. The binding modes of these ligands are discussed providing novel insights into the development of TR ligands. The experimental binding free energies are reasonably well-reproduced from the proposed models using a simple linear interaction energy free-energy calculation scheme.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds. (C) 2010 Elsevier Masson SAS. All rights reserved.

Recognition by the Thyroid Hormone Receptor of Canonical DNA Response Elements

To shed more light on the molecular requirements for recognition of thyroid response elements (TRES) by thyroid receptors (TRs), we compared the specific aspects of DNA TRE recognition by different TR constructs. Using fluorescence anisotropy, we performed a detailed and hierarchical study of TR-TRE binding. This wits done by comparing the binding affinities of three different TR constructs for four different TRE DNA elements, including palindromic sequences and direct repeats (F2, PAL, DR-1, and DR-4) as well as their interactions with nonspecific DNA sequences. The effect of MgCl(2) on suppressing of nonselective DNA binding to TR was also investigated. Furthermore, we determined the dissociation constants of the hTR beta DBD (DNA binding domain) and hTR beta DBD-LBD (DNA binding and ligand binding domains) for specific TRES. We found that a minimum DNA recognition peptide derived from DBD (H1TR) is sufficient for recognition and interaction with TREs, whereas scrambled DNA sequences were unrecognized. Additionally, we determined that the TR DBD binds to F2, PAL, and DR-4 with high affinity and similar K(d) values. The TR DBD-LBD recognizes all the tested TRES but binds preferentially to F2, with even higher affinity. Finally, our results demonstrate the important role played by LBDs in modulating TR-DNA binding.

A proposed EPR approach to evaluating agonist binding site of a peptide receptor

Angiotensin II (Ang II) and its transmembrane AT(1) receptor were selected in order to test an innovative strategy that might allow the assessment of the agonist binding site in the receptor molecule. With the use of the 2,2,6,6-tetramethylpiperidine-1-oxyl-4-amino-4-carboxylic acid (TOAC) paramagnetic probe, a biologically active agonist (TOAC(1)-Ang II), as well as an inactive control (TOAC(4)-Ang II) analogs were mixed in solution with various synthesized AT(1) fragments. Comparative intermolecular interactions, as estimated by analyzing the EPR spectra of solutions, suggested the existence of an agonist binding site containing a sequence composed of portions of the N-terminal (13-17) and the third extracellular loop (266-278) fragments of the AT(1) molecule. Therefore, this combined EPR-TOAC approach shows promise as an alternative for use also in other applications related to specific intermolecular association processes.

Alpha7 Nicotinic Acetylcholine Receptor Expression and Activity During Neuronal Differentiation of PC12 Pheochromocytoma Cells

Nicotinic acetylcholine receptors (nAChR) exert pivotal roles in synaptic transmission, neuroprotection and differentiation. Particularly, homomeric alpha 7 receptors participate in neurite outgrowth, presynaptic control of neurotransmitter release and Ca(2+) influx. However, the study of recombinant alpha 7 nAChRs in transfected cell lines is difficult due to low expression of functional receptor channels. We show that PC12 pheochromocytoma cells induced to differentiation into neurons are an adequate model for studying differential nAChR gene expression and receptor activity. Whole-cell current recording indicated that receptor responses increased during the course of differentiation. Transcription of mRNAs coding for alpha 3, alpha 5, alpha 7, beta 2 and beta 4 subunits was present during the course of differentiation, while mRNAs coding for alpha 2, alpha 4 and beta 3 subunits were not expressed in PC12 cells. alpha 7 subunit expression was highest following 1 day of induction to differentiation. Activity of alpha 7 nAChRs, however, was most elevated on day 2 as revealed by inhibition experiments in the presence of 10 nM methyllycaconitine, rapid current decay and receptor responsiveness to the alpha 7 agonist choline. Increased alpha 7 receptor activity was noted when PC12 were induced to differentiation in the presence of choline, confirming that chronic agonist treatment augments nAChR activity. In summary, PC12 cells are an adequate model to study the role and pharmacological properties of this receptor during neuronal differentiation.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.