Modeling the 3D structure of GPCRs from sequence

Shacham, Sharon; Topf, Maya; Avisar, Noa; Glaser, Fabian; Marantz, Yael; Bar-Haim, Shay; Noiman, Silvia; Naor, Zvi; Becker, Oren M.

doi:10.1002/med.1019

Cited by 64 publications

(44 citation statements)

References 46 publications

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“…In parallel, novel programs were developed, such as PREDICT (Shacham et al 2001(Shacham et al , 2004 or MEMBSTRUCK (Vaidehi et al 2002). These programs were based on physical-chemical considerations, and their aim was to optimize the helical-packing geometry, tilts between the helices, helix orientations, side chain rotamers, helix-membrane crossings, and helical kinks.…”

Section: Gpcr 3d Structure Modelingmentioning

confidence: 99%

Modeling of mammalian olfactory receptors and docking of odorants

et al. 2012

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Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is overexpressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

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Section: Gpcr 3d Structure Modelingmentioning

confidence: 99%

Modeling of mammalian olfactory receptors and docking of odorants

et al. 2012

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“…79 The rhodopsin model calculated with this approach was close to the subsequently published crystal structure (root mean square deviation [rmsd] 2.88 Å for 186 C a -atoms in the TM domain). Other methods for GPCR modeling that do not rely on a structural template include MembStruck 80 and PREDICT, 81 which also produced realistic models of rhodopsin (rmsd of 3.1 Å and 3.87 Å, respectively, vs the rhodopsin X-ray structure in the 7TM domain) and were used to model other GPCRs. Although such ab initio methods were able to achieve medium accuracy in the modeling of the a -helical TM domains of GPCRs, they failed to correctly predict the structure of the receptor loops.…”

Section: Homology Modeling Of Opioid Receptorsmentioning

confidence: 99%

Homology modeling of opioid receptor-ligand complexes using experimental constraints

Pogozheva

Przydzial

Mosberg

2005

AAPS J

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A BSTRACTOpioid receptors interact with a variety of ligands, including endogenous peptides, opiates, and thousands of synthetic compounds with different structural scaffolds. In the absence of experimental structures of opioid receptors, theoretical modeling remains an important tool for structurefunction analysis. The combination of experimental studies and modeling approaches allows development of realistic models of ligand-receptor complexes helpful for elucidation of the molecular determinants of ligand affi nity and selectivity and for understanding mechanisms of functional agonism or antagonism. In this review we provide a brief critical assessment of the status of such theoretical modeling and describe some common problems and their possible solutions. Currently, there are no reliable theoretical methods to generate the models in a completely automatic fashion. Models of higher accuracy can be produced if homology modeling, based on the rhodopsin X-ray template, is supplemented by experimental structural constraints appropriate for the active or inactive receptor conformations, together with receptor-specifi c and ligand-specifi c interactions. The experimental constraints can be derived from mutagenesis and cross-linking studies, correlative replacements of ligand and receptor groups, and incorporation of metal binding sites between residues of receptors or receptors and ligands. This review focuses on the analysis of similarity and differences of the refi ned homology models of m , d , and k -opioid receptors in active and inactive states, emphasizing the molecular details of interaction of the receptors with some representative peptide and nonpeptide ligands, underlying the multiple modes of binding of small opiates, and the differences in binding modes of agonists and antagonists, and of peptides and alkaloids.K EYWORDS: ligand docking , modeling , opioid receptors , opioid ligands , pharmacophore model INTRODUCTIONClinical interest in opioid receptors (ORs) is related to the development of strong analgesics without potential for abuse or adverse side effects. This task, however, cannot be accomplished without understanding the differences in the OR subtypes as well as the modes of interactions of drugs/ ligands with these receptors.Research on ORs was signifi cantly advanced by the cloning of d -opioid (DOR), m -opioid (MOR), and k -opioid (KOR) receptors in the early 1990s. 1 , 2 Sequence comparison confi rmed that ORs belong to the rhodopsin-like family of G-protein-coupled receptors (GPCRs). 1 ORs are composed of a core domain of 7 transmembrane (TM) a -helices and an adjacent, peripheral helix 8 (IL4), are connected by 3 extracellular (EL1, EL2, EL3) and 3 intracellular (IL1, IL2, IL3) loops, and contain glycosylated N-terminal and palmitoylated C-terminal domains of different sizes. ORs demonstrate high sequence identity in their TM domain (73%-76%) and in ILs (63%-66%) and large divergence in N-and C-terminal domains and ELs (34%-40% identity). ORs are activated by either endogenous peptides o...

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“…Recently, we have reported a de novo GPCR modeling approach called PREDICT, which does not rely on the rhodopsin structure and can be applied to any GPCR (14,[19][20][21]. In the present paper, we report the successful application of PREDICT GPCR models in blind high-throughput in silico screening for the discovery of new chemical entities that bind to five different GPCRs.…”

mentioning

confidence: 99%

“…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).Recently, we have reported a de novo GPCR modeling approach called PREDICT, which does not rely on the rhodopsin structure and can be applied to any GPCR (14,[19][20][21]. In the present paper, we report the successful application of PREDICT GPCR models in blind high-throughput in silico screening for the discovery of new chemical entities that bind to five different GPCRs.…”

mentioning

confidence: 99%

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

Becker¹,

Marantz²,

Shacham³

et al. 2004

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

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164

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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Modeling the 3D structure of GPCRs from sequence

Cited by 64 publications

References 46 publications

Modeling of mammalian olfactory receptors and docking of odorants

Modeling of mammalian olfactory receptors and docking of odorants

Homology modeling of opioid receptor-ligand complexes using experimental constraints

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

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