Building a Bridge between G-Protein-Coupled Receptor Modelling, Protein Crystallography and 3D QSAR Studies for Ligand Design

Kim, Ki Hwan

doi:10.1007/0-306-46858-1_15

Cited by 5 publications

(6 citation statements)

References 63 publications

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“…This model was then refined by means of docking combined with molecular mechanics (MM) and molecular dynamics techniques (MD). Our MD simulations on the rhodopsin-based model of GPR17 suggested that the primary nucleotide binding pocket in GPR17 is contained in an accessible crevice enclosed between transmembrane (TM) helices (mainly TM3, TM5, TM6 and TM7) and extracellular loop (EL) 2, in general agreement with the binding site proposed for small molecules to other class A rhodopsin-like GPCRs and for nucleotides to already known P2Y receptors [ 31 - 33 ]. Based on our computational data, we also hypothesized that at the extracellular interface of the receptor, the N-terminus (Nt) region and EL2 and EL3 form accessory binding surfaces that could address ligands to the deeper main binding pocket.…”

Section: Introductionsupporting

confidence: 75%

Forced unbinding of GPR17 ligands from wild type and R255I mutant receptor models through a computational approach

et al. 2010

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BackgroundGPR17 is a hybrid G-protein-coupled receptor (GPCR) activated by two unrelated ligand families, extracellular nucleotides and cysteinyl-leukotrienes (cysteinyl-LTs), and involved in brain damage and repair. Its exploitment as a target for novel neuro-reparative strategies depends on the elucidation of the molecular determinants driving binding of purinergic and leukotrienic ligands. Here, we applied docking and molecular dynamics simulations (MD) to analyse the binding and the forced unbinding of two GPR17 ligands (the endogenous purinergic agonist UDP and the leukotriene receptor antagonist pranlukast from both the wild-type (WT) receptor and a mutant model, where a basic residue hypothesized to be crucial for nucleotide binding had been mutated (R255I) to Ile.ResultsMD suggested that GPR17 nucleotide binding pocket is enclosed between the helical bundle and extracellular loop (EL) 2. The driving interaction involves R255 and the UDP phosphate moiety. To support this hypothesis, steered MD experiments showed that the energy required to unbind UDP is higher for the WT receptor than for R255I. Three potential binding sites for pranlukast where instead found and analysed. In one of its preferential docking conformations, pranlukast tetrazole group is close to R255 and phenyl rings are placed into a subpocket highly conserved among GPCRs. Pulling forces developed to break polar and aromatic interactions of pranlukast were comparable. No differences between the WT receptor and the R255I receptor were found for the unbinding of pranlukast.ConclusionsThese data thus suggest that, in contrast to which has been hypothesized for nucleotides, the lack of the R255 residue doesn't affect the binding of pranlukast a crucial role for R255 in binding of nucleotides to GPR17. Aromatic interactions are instead likely to play a predominant role in the recognition of pranlukast, suggesting that two different binding subsites are present on GPR17.

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Section: Introductionsupporting

confidence: 75%

Forced unbinding of GPR17 ligands from wild type and R255I mutant receptor models through a computational approach

et al. 2010

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“…To put the ligand-based QSAR and CoMFA models in a perspective that takes into consideration the structure of the target enzyme, 27 we examined the position of the CoMFA contours in the AChE active site. This is a common practice when the 3D structure of the target protein is available, and actually, it has been done in two of the above-cited CoMFA studies on AChE inhibitors.…”

Section: Discussionmentioning

confidence: 99%

“…To put the ligand-based QSAR and CoMFA models in a perspective that takes into consideration the structure of the target enzyme, we examined the position of the CoMFA contours in the AChE active site. This is a common practice when the 3D structure of the target protein is available, and actually, it has been done in two of the above-cited CoMFA studies on AChE inhibitors. , Even if the CoMFA contour maps can by no means be interpreted as some sort of 3D “complementary” picture of an enzyme active site, it is reasonable to look for some consistency of the steric (and, eventually, electrostatic) features revealed by the 3D statistical analysis and the “actual” enzyme−ligand interaction site.…”

Section: Discussionmentioning

confidence: 99%

SAR of 9-Amino-1,2,3,4-tetrahydroacridine-Based Acetylcholinesterase Inhibitors: Synthesis, Enzyme Inhibitory Activity, QSAR, and Structure-Based CoMFA of Tacrine Analogues

et al. 2000

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In this study, we attempted to derive a comprehensive SAR picture for the class of acetylcholinesterase (AChE) inhibitors related to tacrine, a drug currently in use for the treatment of the Alzheimer's disease. To this aim, we synthesized and tested a series of 9-amino-1,2,3,4-tetrahydroacridine derivatives substituted in the positions 6 and 7 of the acridine nucleus and bearing selected groups on the 9-amino function. By means of the Hansch approach, QSAR equations were obtained, quantitatively accounting for both the detrimental steric effect of substituents in position 7 and the favorable electron-attracting effect exerted by substituents in positions 6 and 7 of the 9-amino-1,2,3,4-tetrahydroacridine derivatives. The three-dimensional (3D) properties of the inhibitors were taken into consideration by performing a CoMFA analysis on the series of AChE inhibitors made by 12 9-amino-1,2,3, 4-tetrahydroacridines and 13 11H-indeno[1,2-b]quinolin-10-ylamines previously developed in our laboratory. The alignment of the molecules to be submitted to the CoMFA procedure was carried out by taking advantage of docking models calculated for the interactions of both the unsubstituted 9-amino-1,2,3,4-tetrahydroacridine and 11H-indeno[1,2-b]quinolin-10-ylamine with the target enzyme. A highly significant CoMFA model was obtained using the steric field alone, and the features of such a 3D QSAR model were compared with the classical QSAR equations previously calculated. The two models appeared consistent, the main aspects they had in common being (a) the individuation of the strongly negative contribution of the substituents in position 7 of tacrine and (b) a tentative assignment of the hydrophobic character to the favorable effect exerted by the substituents in position 6. Finally, a new previously unreported tacrine derivative designed on the basis of both the classical and the 3D QSAR equations was synthesized and kinetically evaluated, to test the predictive ability of the QSAR models. The 6-bromo-9-amino-1,2,3,4-tetrahydroacridine was predicted to have a pIC(50) value of 7.31 by the classical QSAR model and 7.40 by the CoMFA model, while its experimental IC(50) value was equal to 0.066 (+/-0.009) microM, corresponding to a pIC(50) of 7.18, showing a reasonable agreement between predicted and observed AChE inhibition data.

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“…Mutagenesis and biophysical studies of several GPCRs have indicated that small-molecule agonists and antagonists bind to a hydrophilic pocket buried in the transmembrane core of the receptor. 24 For example, binding of biogenic amines to their corresponding receptors is characterized by a complex network of interactions involving several transmembrane domains in which key residues in TM3, TM5, and TM6 are essential for forming the binding pocket, with speci®city for agonist recognition. 25 It is believed that in these receptors, the ligand's amine group pairs with a carboxyl group from an Asp residue located in TM3, whereas its catechol ring interacts with residues in TM5 and TM6.…”

Section: G P C R S T R U C T U R Ementioning

confidence: 99%

Modeling the 3D structure of GPCRs from sequence

Shacham¹,

Topf²,

Avisar³

et al. 2001

Medicinal Research Reviews

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G-protein-coupled receptors (GPCRs) are a large and functionally diverse protein superfamily, which form a seven transmembrane (TM) helices bundle with alternating extra-cellular and intracellular loops. GPCRs are considered to be one of the most important groups of drug targets because they are involved in a broad range of body functions and processes and are related to major diseases. In this paper we present a new technology, named PREDICT, for modeling the 3D structure of any GPCR from its amino acid sequence. This approach takes into account both internal protein properties (i.e., the amino acid sequence) and the properties of the membrane environment. Unlike competing approaches, the new technology does not rely on the single known structure of rhodopsin, and is thus capable of predicting novel GPCR conformations. We demonstrate the capabilities of PREDICT in reproducing the known experimental structure of rhodopsin. In principle, PREDICT-generated models offer new opportunities for structure-based drug discovery towards GPCR targets.

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Building a Bridge between G-Protein-Coupled Receptor Modelling, Protein Crystallography and 3D QSAR Studies for Ligand Design

Cited by 5 publications

References 63 publications

Forced unbinding of GPR17 ligands from wild type and R255I mutant receptor models through a computational approach

Forced unbinding of GPR17 ligands from wild type and R255I mutant receptor models through a computational approach

SAR of 9-Amino-1,2,3,4-tetrahydroacridine-Based Acetylcholinesterase Inhibitors: Synthesis, Enzyme Inhibitory Activity, QSAR, and Structure-Based CoMFA of Tacrine Analogues

Modeling the 3D structure of GPCRs from sequence

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