Virtual Screening of Novel CB2 Ligands Using a Comparative Model of the Human Cannabinoid CB2 Receptor

Salo, Outi M. H.; Raitio, Katri H.; Savinainen, Juha R.; Nevalainen, Tapio; Lahtela‐Kakkonen, Maija; Laitinen, Jarmo T.; Järvinen, Tomi; Poso, Antti

doi:10.1021/jm050565b

Cited by 61 publications

(68 citation statements)

References 57 publications

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“…27 Therefore, this site was speculated to be the CB2 agonist binding pocket. The defined CB2 agonist binding pocket shows certain differences with the model reported by Salo et al 15 The correspondent structure-based database search results for novel CB2 agonist leads will be reported elsewhere, whereas the current study is mainly focused on developing a virtual screening protocol for a CB2 antagonist search.…”

Section: Resultsmentioning

confidence: 74%

“…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.…”

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confidence: 99%

“…Structure-based virtual screening was conducted previously in order to discover the structure-diverse agonists for the CB2 receptor. 15 However, biochemical studies indicated that the agonist and antagonist of the cannabinoid receptors might bind at the different active sites of the CB2 receptor. 33,34 Therefore, our present CB2 structure-based antagonist virtual screening studies will allow us to establish an alternative avenue for the novel lead discovery of a CB2 antagonist.…”

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confidence: 99%

“…[6][7][8][9][10][11][12][13][14][15] However, the relatively low global sequence identity between the structure-unknown GPCRs and bovine rhodopsin is generally considered to be insufficient for reliable homology modeling. 6 The different conformation states of GPCRs could also be induced by various types of bound ligand.…”

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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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Section: Resultsmentioning

confidence: 74%

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confidence: 99%

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confidence: 99%

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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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“…In some GPCR SBVS studies docking based screening simulations have been guided by pharmacophore constraints Evers and Klebe 2004;Sirci et al 2012). Pharmacophore models and/or exclusion constraints derived from structural models of receptor-ligand complexes (or the receptor alone) can be used as an alternative structure-based virtual screening approach to molecular docking simulations, as demonstrated in several successful GPCR SBVS campaigns to discover ligands of CA3R (Klabunde et al 2009), CNR2 (Salo et al 2005), FFAR1 (Tikhonova et al 2008), FPR1R (Edwards et al 2005), MCHR1 (Cavasotto et al 2008), HRH3 (Sirci et al 2012). …”

Section: Hierarchical Workflow For Gpcr Structure-based Ligand Discoverymentioning

confidence: 99%

From Three-Dimensional GPCR Structure to Rational Ligand Discovery

Kooistra¹,

Leurs²,

Esch³

et al. 2013

Advances in Experimental Medicine and Biology

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This chapter will focus on G protein-coupled receptor structure-based virtual screening and ligand design. A generic virtual screening workflow and its individual elements will be introduced, covering amongst others the use of experimental data to steer the virtual screening process, ligand binding mode prediction, virtual screening for novel ligands, and rational structure-based virtual screening hit optimization. An overview of recent successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family. The current chapter will discuss several challenges in rational ligand discovery based on GPCR structures including: (i) structure-based identification of ligands with specific effects on GPCR mediated signaling pathways, and (ii) virtual screening and structure-based optimization of fragment-like molecules.

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Small Molecule Inhibitors: Design and Selection

Che

Liu

Gray

2008

Wiley Encyclopedia of Chemical Biology

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The discovery and optimization of small‐molecule inhibitors for the treatment of human disease has been the major focus of the pharmaceutical industry for more than a century. In the last decade, they have also attracted increased attention from academia because they provide a powerful means to interrogate biologic systems in an acute fashion. Although pharmaceutical companies have identified many small‐molecule inhibitors for drug targets, to find a desirable small‐molecule inhibitor for a specific target is still a major challenge for drug discovery and for chemical biology. Here, we present an overview of the computational approaches that can be exploited to discover small‐molecule inhibitors for protein targets of interest.

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Virtual Screening of Novel CB2 Ligands Using a Comparative Model of the Human Cannabinoid CB2 Receptor

Cited by 61 publications

References 57 publications

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

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

From Three-Dimensional GPCR Structure to Rational Ligand Discovery

Small Molecule Inhibitors: Design and Selection

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