Homology Modeling of G-Protein-Coupled Receptors and Implications in Drug Design

Patny, Akshay; Desai, Prashant; Avery, Mitchell A.

doi:10.2174/092986706777442002

Cited by 67 publications

(42 citation statements)

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“…SIGNALING IN THE CNS G-protein-coupled receptors (GPCRs) are seventransmembrane receptors, constitute the largest class of membrane proteins, and are targeted by 25-50% of prescription drugs available (Drews, 2000;Jacoby et al, 2006;Patny et al, 2006). GPCRs and G-proteins are widely distributed throughout the CNS and play a major role in the regulation of neuronal activity and behavior (Karasinska et al, 2003;Maudsley et al, 2007).…”

Section: Strategies For Studying G-proteinmentioning

confidence: 99%

Mechanisms of dominant negative G‐protein α subunits

Barren

Artemyev

2007

J of Neuroscience Research

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G-protein-coupled receptors (GPCRs) represent the largest class of membrane proteins and are the targets of 25-50% of drugs currently on the market. Dominant negative mutant Galpha subunits of heterotrimeric G-proteins have been extensively utilized to delineate G-protein signaling pathways and represent a promising new tool to study GPCR-dependent signaling in the CNS. There are different regions in various types of Galpha subunits in which mutations can give rise to a dominant negative phenotype. Such a mutant Galpha would compete with wild-type Galpha for binding to other proteins involved in the G-protein cycle and either block or reduce the response caused by wild-type Galpha. To date, there are three different mechanisms described for dominant negative Galpha subunits: sequestration of the Gbetagamma subunits, sequestration of the activated GPCR by the heterotrimeric complex, and sequestration of the activated GPCR by nucleotide-free Galpha. This review focuses on the development of dominant negative Galpha subunits, the different mechanisms used by various mutant Galpha subunits, and potential structural changes underlying the dominant negative effects.

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Section: Strategies For Studying G-proteinmentioning

confidence: 99%

Mechanisms of dominant negative G‐protein α subunits

Barren

Artemyev

2007

J of Neuroscience Research

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“…Thus, structure-function studies of GPCRs relied heavily on sequence and phylogenetic analyses, rhodopsin-based homology modeling, and docking studies supported by site-directed mutagenesis and ligand structure-activity relationships (SAR) data [5,6]. In this context, experimentally validated GPCR homology models have proven to be valuable tools for lead identification, and the scientific literature flourished with successful rational drug design and virtual screening examples [7][8][9][10][11]. In 2007 Kobilka and coworkers unveiled the 3D crystal structure of the human β 2 -adrenergic receptor (β 2 -AR), finally providing the long awaited proof that the structure of GPCRs generally resembles that of rhodopsin [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Costanzi

Tikhonova

Harden

2008

J Comput Aided Mol Des

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SummaryAccurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

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“…In this study we carried out the modeling study of P2Y12 and the docking-based virtual screening of potential antagonists against P2Y12. Homology modeling is based on the reasonable assumption that two homologous proteins will share very similar structures [45]. Because the sequence identity between P2Y12 and those GPCRs with known 3D structures is relatively low, modeling of P2Y12 is highly challenging [46].…”

Section: Discussionmentioning

confidence: 99%

Docking-based virtual screening of potential human P2Y12 receptor antagonists

Chen¹,

Dong²,

Zhou³

et al. 2011

ABBS

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Platelet plays essential roles in hemostasis and its dysregulation can lead to arterial thrombosis. P2Y12 is an important platelet membrane adenosine diphosphate receptor, and its antagonists have been widely developed as anticoagulation agents. The current P2Y12 inhibitors available in clinical practice have not fully achieved satisfactory antithrombotic effects, leaving room for further improvement. To identify new chemical compounds as potential anticoagulation inhibitors, we constructed a three-dimensional structure model of human P2Y12 by homology modeling based on the recently reported G-protein coupled receptor Meleagris gallopavo b1 adrenergic receptor. Virtual screening of the modeled P2Y12 against three subsets of small molecules from the ZINC database, namely lead-like, fragment-like, and drug-like, identified a number of compounds that might have high binding affinity to P2Y12. Detailed analyses of the top three compounds from each subset with the highest scores indicated that all of these compounds beard a hydrophobic bulk supplemented with a few polar atoms which bound at the ligand binding site via largely hydrophobic interactions with the receptor. This study not only provides a structure model of P2Y12 for rational design of anti-platelet inhibitors, but also identifies some potential chemicals for further development.

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Homology Modeling of G-Protein-Coupled Receptors and Implications in Drug Design

Cited by 67 publications

References 0 publications

Mechanisms of dominant negative G‐protein α subunits

Mechanisms of dominant negative G‐protein α subunits

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Docking-based virtual screening of potential human P2Y12 receptor antagonists

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