An Automated System for the Analysis of G Protein-Coupled Receptor Transmembrane Binding Pockets:  Alignment, Receptor-Based Pharmacophores, and Their Application

Kratochwil, Nicole A.; Malherbe, Pari; Lindemann, Lothar; Ebeling, Martin; Hoener, Marius C.; Mühlemann, Andreas; Porter, Richard H.; Ståhl, Martin; Gerber, Paul R.

doi:10.1021/ci050221u

Cited by 55 publications

(48 citation statements)

References 87 publications

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“…This pocket-like crevice is covered by specific amino acids [71] which are predestinated for interaction with ligands in different particular modes [72]. For comparison of amino acids (figure 5) which are potentially important for ligand binding at different aminergic receptors we depicted those amino acids of ADRB2 that are determinants of this spatial region (figure 4, figure 6A).…”

Section: Resultsmentioning

confidence: 99%

Differential Modulation of Beta-Adrenergic Receptor Signaling by Trace Amine-Associated Receptor 1 Agonists

et al. 2011

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Trace amine-associated receptors (TAAR) are rhodopsin-like G-protein-coupled receptors (GPCR). TAAR are involved in modulation of neuronal, cardiac and vascular functions and they are potentially linked with neurological disorders like schizophrenia and Parkinson's disease. Subtype TAAR1, the best characterized TAAR so far, is promiscuous for a wide set of ligands and is activated by trace amines tyramine (TYR), phenylethylamine (PEA), octopamine (OA), but also by thyronamines, dopamine, and psycho-active drugs. Unfortunately, effects of trace amines on signaling of the two homologous β-adrenergic receptors 1 (ADRB1) and 2 (ADRB2) have not been clarified yet in detail. We, therefore, tested TAAR1 agonists TYR, PEA and OA regarding their effects on ADRB1/2 signaling by co-stimulation studies. Surprisingly, trace amines TYR and PEA are partial allosteric antagonists at ADRB1/2, whereas OA is a partial orthosteric ADRB2-antagonist and ADRB1-agonist. To specify molecular reasons for TAAR1 ligand promiscuity and for observed differences in signaling effects on particular aminergic receptors we compared TAAR, tyramine (TAR) octopamine (OAR), ADRB1/2 and dopamine receptors at the structural level. We found especially for TAAR1 that the remarkable ligand promiscuity is likely based on high amino acid similarity in the ligand-binding region compared with further aminergic receptors. On the other hand few TAAR specific properties in the ligand-binding site might determine differences in ligand-induced effects compared to ADRB1/2. Taken together, this study points to molecular details of TAAR1-ligand promiscuity and identified specific trace amines as allosteric or orthosteric ligands of particular β-adrenergic receptor subtypes.

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Section: Resultsmentioning

confidence: 99%

Differential Modulation of Beta-Adrenergic Receptor Signaling by Trace Amine-Associated Receptor 1 Agonists

et al. 2011

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“…The incorporation of information about the functional sites into the protein similarity design is an interesting research direction (Kratochwil et al , 2005). Recently, several kernel-based supervised network inference methods have been developed (Vert and Yamanishi, 2005; Yamanishi et al , 2004), but they are limited to interactions between homogeneous molecules (e.g.…”

Section: Discussionmentioning

confidence: 99%

Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

et al. 2008

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Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.Availability: Softwares are available upon request.Contact: Yoshihiro.Yamanishi@ensmp.frSupplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

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“…The ligands utilized in the present study demonstrate chemical similarities, and the adrenergic receptors and TAAR are evolutionary closely related (Borowsky et al 2001, Roeder 2005. As a consequence, their ligand binding pockets show significant overlapping properties and specificities, including, for instance, a conserved aspartate residue at transmembrane helix 3 (Huang 2003, Kratochwil et al 2005. Therefore, it can be speculated that 3-T1AM and NorEpi both bind at ADRA2A, but slight differences in the receptor-ligand interaction pattern should exist because of the differences in ligand structures, which altogether should cause three general findings: i) the observed noninduction of MAPK by 3-T1AM; ii) the consequential antagonistic effect of 3-T1AM on NorEpi-induced MAPK (by ligand competition at the overlapping ligand binding region); and iii) the induction of G i signaling also by 3-T1AM.…”

Section: -T1am Application In Vivo Does Not Influence Glucose Metabomentioning

confidence: 92%

3-iodothyronamine differentially modulates α-2A-adrenergic receptor-mediated signaling

Dinter¹,

Mühlhaus²,

Jacobi³

et al. 2015

Journal of Molecular Endocrinology

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Most in vivo effects of 3-iodothyronamine (3-T1AM) have been thus far thought to be mediated by binding at the trace amine-associated receptor 1 (TAAR1). Inconsistently, the 3-T1AM-induced hypothermic effect still persists in Taar1 knockout mice, which suggests additional receptor targets. In support of this general assumption, it has previously been reported that 3-T1AM also binds to the a-2A-adrenergic receptor (ADRA2A), which modulates insulin secretion. However, the mechanism of this effect remains unclear. We tested two different scenarios that may explain the effect: the sole action of 3-T1AM at ADRA2A and a combined action of 3-T1AM at ADRA2A and TAAR1, which is also expressed in pancreatic islets. We first investigated a potential general signaling modification using the label-free EPIC technology and then specified changes in signaling by cAMP inhibition and MAPKs (ERK1/2) determination. We found that 3-T1AM induced G i/o activation at ADRA2A and reduced the norepinephrine (NorEpi)-induced MAPK activation. Interestingly, in ADRA2A/TAAR1 hetero-oligomers, application of NorEpi resulted in uncoupling of the G i/o signaling pathway, but it did not affect MAPK activation. However, 3-T1AM application in mice over a period of 6 days at a daily dose of 5 mg/kg had no significant effects on glucose homeostasis. In summary, we report an agonistic effect of 3-T1AM on the ADRA2A-mediated G i/o pathway but an antagonistic effect on MAPK induced by NorEpi. Moreover, in ADRA2A/TAAR1 hetero-oligomers, the capacity of NorEpi to stimulate G i/o signaling is reduced by co-stimulation with 3-T1AM. The present study therefore points to a complex spectrum of signaling modification mediated by 3-T1AM at different G protein-coupled receptors.Key Words " G protein-coupled receptor " adrenergic receptor

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An Automated System for the Analysis of G Protein-Coupled Receptor Transmembrane Binding Pockets: Alignment, Receptor-Based Pharmacophores, and Their Application

Cited by 55 publications

References 87 publications

Differential Modulation of Beta-Adrenergic Receptor Signaling by Trace Amine-Associated Receptor 1 Agonists

Differential Modulation of Beta-Adrenergic Receptor Signaling by Trace Amine-Associated Receptor 1 Agonists

Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

3-iodothyronamine differentially modulates α-2A-adrenergic receptor-mediated signaling

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