PREDICT modeling and in‐silico screening for G‐protein coupled receptors

Shacham, Sharon; Marantz, Yael; Bar-Haim, Shay; Kalid, Ori; Warshaviak, Dora Toledo; Avisar, Noa; Inbal, Boaz; Heifetz, Alexander; Fichman, Merav; Topf, Maya; Naor, Zvi; Noiman, Silvia; Becker, Oren M.

doi:10.1002/prot.20195

Cited by 108 publications

(126 citation statements)

References 173 publications

(228 reference statements)

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“…In this case, enrichment factors were obtained by virtually screening a random 10,000 drug-like compound library to which a small number of known ligands were added. The results consistently show, for a range of GPCRs, including biogenic amine, peptide, and chemokine receptors, that the PREDICT 3D models yield enrichment factors ranging from 10-to 350-fold better than random (14,21). These enrichment factors are similar to, and sometimes even better than, enrichment factors reported for in silico screening by using high-resolution crystal structures for non-GPCR targets (17,(35)(36)(37).…”

Section: Methodssupporting

confidence: 66%

“…This C␣ rms value is comparable with the rms obtained by Vaidehi et al (32) when modeling rhodopsin based on the rhodopsin x-ray structure. PREDICT also modeled correctly the unusual helical kinks observed in the x-ray structure of rhodopsin (21). Both the degree of the helical kinks and the twist angles were successfully reproduced in the PREDICT rhodopsin model, including the inwardbent Pro kink in TM1 and the Thr-Gly kink in TM2 (33).…”

Section: Methodsmentioning

confidence: 79%

“…The PREDICT algorithm and methodology were recently reported elsewhere (14,21). Here, we shall give only a brief overview of the method.…”

Section: Methodsmentioning

confidence: 99%

“…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: 97%

“…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%

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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

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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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Section: Methodssupporting

confidence: 66%

Section: Methodsmentioning

confidence: 79%

“…The PREDICT algorithm and methodology were recently reported elsewhere (14,21). Here, we shall give only a brief overview of the method.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

See 3 more Smart Citations

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

Becker¹,

Marantz²,

Shacham³

et al. 2004

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

Self Cite

164

158

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GPCRHomology Model Development and Application

Garland

Blaney

2010

Burger's Medicinal Chemistry and Drug Discovery

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G‐protein‐coupled receptors (GPCRs) are a very important class of proteins for drug discovery. Exciting progress has been made recently in the determination of X‐ray crystal structures. However, despite this, structural coverage of the receptor family is still thin. We lack direct structural knowledge of many important subfamilies, we have only modest detail of the conformational changes associated with receptor activation and we lack robust crystallographic systems for the rapid determination of ligand binding modes. As a consequence, the ability to build and apply homology models for drug discovery at these targets is very valuable. The accuracy of the models has improved in parallel with the increase in available structural data and the models have been successfully applied to a wide range of lead generation and optimization problems.

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Recent Advances in Drug Discovery Research Using Structure‐Based Virtual Screening Techniques: Examples of Success for Diverse Protein Targets

Ghosh

Nie

et al. 2006

Drug Discovery Research

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PREDICT modeling and in‐silico screening for G‐protein coupled receptors

Cited by 108 publications

References 173 publications

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

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

GPCRHomology Model Development and Application

Recent Advances in Drug Discovery Research Using Structure‐Based Virtual Screening Techniques: Examples of Success for Diverse Protein Targets

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