Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D<sub>3</sub>) Subtype Receptor:  Discovery of Novel and Potent D<sub>3</sub>Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach

Varady, Judith; Wu, Xihan; Fang, Xueliang; Ji, Min; Hu, Zhijuan; Levant, Beth; Wang, Shaomeng

doi:10.1021/jm030085p

Cited by 132 publications

(162 citation statements)

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“…Although a homology model based on the inactive structure of rhodopsin can be a valuable tool in receptor research and ligand design (43), the modeled structure may not be representative of the additional conformations that the receptor assumes upon activation. This is important, because agonists bind to these active receptor conformations (44).…”

Section: Discussionmentioning

confidence: 99%

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

Abaffy

Malhotra

Luetje

2007

Journal of Biological Chemistry

103

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Sequence differences between members of the mouse olfactory receptor MOR42 subfamily (MOR42-3 and MOR42-1) are likely to be the basis for variation in ligand binding preference among these receptors. We investigated the specificity of MOR42-3 for a variety of dicarboxylic acids. We used site-directed mutagenesis, guided by homology modeling and ligand docking studies, to locate functionally important residues. Receptors were expressed in Xenopus oocytes and assayed using high throughput electrophysiology. The importance of the Val-113 residue, located deep within the receptor, was analyzed in the context of interhelical interactions. We also screened additional residues predicted to be involved in ligand binding site, based on comparison of ortholog/paralog pairs from the mouse and human olfactory receptor genomes (Man, O., Gilad, Y., and Lancet, D. (2004) Protein Sci. 13, 240 -254). A network of 8 residues in transmembrane domains III, V, and VI was identified.These residues form part of the ligand binding pocket of MOR42-3. C12 dicarboxylic acid did not activate the receptor in our functional assay, yet our docking simulations predicted its binding site in MOR42-3. Binding without activation implied that C12 dicarboxylic acid might act as an antagonist. In our functional assay, C12 dicarboxylic acid did indeed act as an antagonist of MOR42-3, in agreement with molecular docking studies. Our results demonstrate a powerful approach based on the synergy between computational predictions and physiological assays.

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Section: Discussionmentioning

confidence: 99%

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

Abaffy

Malhotra

Luetje

2007

Journal of Biological Chemistry

103

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“…These modeling efforts have been described in a recent review (14). Some of these models have been successful in screening for known ligands embedded in random libraries (17), as well as for discovery of novel dopamine D3 ligands when used in conjunction with a pharmacophore-based method (18).…”

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confidence: 99%

G protein-coupled receptors:In silicodrug discovery in 3D

Becker¹,

Marantz²,

Shacham³

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

163

158

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The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the PREDICT method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 ''virtual hit'' compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, K i < 5 M). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1-to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.modeling ͉ in silico screening ͉ structure-based G protein-coupled receptors (GPCRs) are membraneembedded proteins, responsible for communication between the cell and its environment (1). As a consequence, many major diseases, such as hypertension, cardiac dysfunction, depression, anxiety, obesity, inflammation, and pain, involve malfunction of these receptors (2), making them among the most important drug targets for pharmacological intervention (3-5). Thus, whereas GPCRs are only a small subset of the human genome, they are the targets for Ϸ50% of all recently launched drugs (6). As targets of paramount importance, it is expected that drug discovery for GPCRs would benefit from the introduction of computational methodologies (7), especially as these methods can be used in conjunction with such experimental methods as high-throughput screening (8, 9), NMR, and crystallography (10).Unfortunately, GPCRs, like other membrane-embedded proteins, have characteristics that make their 3D structure extremely difficult to determine experimentally. To date, the only GPCR for which a 3D structure was determined by x-ray crystallography is bovine rhodopsin (11), which is unique among GPCRs in that its ligand, retinal, is covalently bound and that it responds to light rather than to ligand binding. Hence, in the case of GPCRs, the limited availability of structural data has forced the computational design of ligands to heavily rely on ligand-based techniques. Indeed, for many GPCRs, the natural ligand can provide a good starting point, leading to useful pharmacophore models that can be used for identifying lead structures with novel scaffolds (6). These methods have been successfully applied for the discovery of peptide agonists to the somatostatin receptor (12) and for...

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“…In fact, the use of 3D GPCR structural models in drug design and structurebased virtual screening studies has increasingly emerged in recent literature. [6][7][8][9][10][11][12][13][14][15] Among these studies, it has been demonstrated that the homology models of dopamine D3, muscarinic M1, vasopressin V1a receptors, and 5HT 2c were reliable enough to retrieve known antagonists via structurebased virtual screening from several compound databases. 7,14 Rhodopsin-based homology models of the R 1A receptor could be used as the structural basis for the lead finding and optimization through the application of a hierarchical virtual screening procedure.…”

mentioning

confidence: 99%

“…12 The impressive discovery of novel potent dopamine D3 ligands using a hybrid pharmacophore-and structure-based database searching approach has also been reported. 13 Overall, these few existing virtual screening studies revealed the feasibility of using the homology-predicted 3D GPCR structural models for receptor-based in silico drug design, although the virtual screening methods and the hit scoring and ranking processes are still under development.…”

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confidence: 99%

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GPCR Structure‐Based Virtual Screening Approach for CB2 Antagonist Search.

Chen¹,

Wang²,

Xie³

2007

ChemInform

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The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

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Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D₃) Subtype Receptor: Discovery of Novel and Potent D₃Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach

Cited by 132 publications

References 77 publications

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

G protein-coupled receptors:In silicodrug discovery in 3D

GPCR Structure‐Based Virtual Screening Approach for CB2 Antagonist Search.

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Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach

Cited by 132 publications

References 77 publications

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

The Molecular Basis for Ligand Specificity in a Mouse Olfactory Receptor

G protein-coupled receptors:In silicodrug discovery in 3D

GPCR Structure‐Based Virtual Screening Approach for CB2 Antagonist Search.

Contact Info

Product

Resources

About

Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D₃) Subtype Receptor: Discovery of Novel and Potent D₃Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach