GPCRDB: an information system for G protein-coupled receptors

Ísberg, Vignir; Vroling, Bas; Kant, Rob van der; Li, Kang; Vriend, Gert; Gloriam, David E.

doi:10.1093/nar/gkt1255

Cited by 135 publications

(157 citation statements)

References 20 publications

Supporting

Mentioning

156

Contrasting

Unclassified

Order By: Relevance

“…This crystallographic structure was chosen in accordance with a total sequence similarity of 68%, as calculated with BLAST (10). In addition each TM was then aligned to the TMs of the template, which numeration was obtained at the GPCRdatabase (11). The TMs were checked for sequence similarity and a average value of 77 % was attained for this more relevant and conserved helical bundle.…”

Section: Modellingmentioning

confidence: 99%

Creating a valid in silico Dopamine D2-receptor model for small molecular docking studies

Bueschbell

Preto

Barreto

et al. 2017

Proceedings of MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd Edition

View full text Add to dashboard Cite

Due to the clinical importance of the Dopamine D2-receptor (D2R) in several brain dysfunctions, the utilization of in silico models for drug development is a growing field of investigation. We provided a transparent and reproducible pipeline for creating a valid D2R model for small molecular docking studies. Furthermore, we suggested a binding pocket for the endogenous ligand of D2R, which was attained upon careful consideration of the available experimental data. Molecular docking studies with Dopamine, Quinpirole and Raclopride allowed also a better understanding of the binding pocket characteristics.

show abstract

Section: Modellingmentioning

confidence: 99%

Creating a valid in silico Dopamine D2-receptor model for small molecular docking studies

Bueschbell

Preto

Barreto

et al. 2017

Proceedings of MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd Edition

View full text Add to dashboard Cite

show abstract

Section: Ligand Binding: Extracellular Viewmentioning

confidence: 99%

“…In the case of the ß1 adrenergic receptor, as shown in Figure 10, the ligand carazolol (Moukhametzianov et al, 2011), bound entirely within the major pocket (panel A) alters signal exclusively to the G protein pathways, without influence on arrestin pathways. By contrast the larger ligand carvedilol, bound in both major and minor pockets of ß1 adrenergic receptor ( Figure 10, panel (Isberg et al, 2014).Representation modes that are effective for the extracellular view include both the schematic wheel/box diagrams and several three-dimensional representation modes. The twodimensional schematic wheel/box is effective to depict the contribution of specific amino acid residues in the context of each helix.…”

mentioning

confidence: 99%

The Science Behind G Protein-Coupled Receptors (GPCRs) and Their Accurate Visual Representation in Scientific Research

Sojka¹,

Brennan²,

Maizels³

et al. 2017

JBC

View full text Add to dashboard Cite

IntroductionG Protein-Coupled Receptors (GPCRs) are transmembrane (TM) proteins that span the cell membrane seven times, and contain intracellular and extracellular domains comprised of connecting loops as well as terminal extension sequences. GPCRs bind ligands within their transmembrane and/or extracellular domains. Ligand binding elicits conformational changes that initiate downstream intracellular signaling events through arrestins and G proteins ( Figure 1; Katritch et al., 2013). GPCRs play central roles in many physiological processes from sensory to neurological, cardiovascular, endocrine, and reproductive functions. GPCRs represent one of the largest gene families in the human genome, encoding approximately 800 unique proteins (Fredriksson et al., 2003). GPCRs' unique structure and cell surface location make them ideal targets for various drug therapies, assuring interest from the pharmaceutical and clinical medicine communities (Vischer et al., 2011). It is estimated that roughly 40% of the pharmaceuticals currently marketed, target GPCRs (Vischer et al., 2011). BI-167107, bovine Gs;Rasmussen et al., 2011) Urbana-Champaign (Humphreys et al., 1996). This image was made with Visual Molecular Dynamics (VMD). VMD is developed with NIH support by the Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois atThe elucidation of x-ray crystallographic structures of GPCRs has been monumental to the research in understanding the function and conformational flexibility of GPCRs (Costanzi 2014;Venkatakrishnan et al., 2014). Understanding how ligand binding alters the structure and function of GPCRs to mediate signaling has undergone an expansion in recent years (Katritch et al., 2013). Concepts such as ligands acting as functionally selective biased agonists to elicit a specific subset of signaling responses are now at the forefront of GPCR research (Andresen 2011). Development of allosteric ligands extends the repertoire of GPCR regulators (Smith 2010). GPCRs were originally considered to be monomeric, but increasing evidence indicates that they can form dimers and oligomers as well (Ferre et al., 2014 Piscitelli et al., 2015), has been described as a "crystallization boom" (Costanzi 2014), with rapid growth emerging due to recent technological advances in both crystallization and structure detection. The availability of growing numbers of experimentally-determined GPCR structures, as well as improved homology-modeling of unknown target proteins based on a wider field of experimentally determined GPCR template structures, allows visual representation of GPCRs to be built upon the basis of known or modeled three-dimensional structures. Nevertheless, several challenges to accurate communication of GPCR structure remain.The goal of this paper is to provide strategies to address common visual representation challenges for GPCRs, and to identify errors that may arise from an incomplete understanding of GPCR structure. GPCRs comprise a subclass of the alpha-helical membrane p...

show abstract

“…In this paper, a new dataset was built from the latest version (updated at September 26, 2013) of GPCRDB [9]. The newly update GPCRDB classify all the protein sequences into six main families, (1) Class A Rhodopsin like, (2) Class B Secretin like, (3) Class C Metabotropic glutamate/pheromone family, (4) Class D cAMP receptors family, (5) Class E Vomeronasal receptors (V1R and V3R) family and (6) Class F Taste receptors T2R family.…”

Section: Datasetmentioning

confidence: 99%

“…In the area of GPCR, the GPCRDB is a molecular classspecific information system that collects, combines, validates and disseminates large amounts of heterogeneous data on GPCRs [8]. According to the latest release of GPCRDB, the data are grouped into six families based on the pharmacological classification of GPCRs [9]. These GPCRs families and their structural features are closely correlated with their function [1], it would be significant to develop a powerful computational method to classify GPCRs into particular families for the purpose of understanding their biological function and their potential as future drug targets.…”

Section: Introductionmentioning

confidence: 99%

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Nie

Wang

et al. 2015

BME

View full text Add to dashboard Cite

Abstract. G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

show abstract

GPCRDB: an information system for G protein-coupled receptors

Cited by 135 publications

References 20 publications

<strong>Creating a valid <em>in silico</em> Dopamine D2-receptor model for small molecular docking studies</strong>

<strong>Creating a valid <em>in silico</em> Dopamine D2-receptor model for small molecular docking studies</strong>

The Science Behind G Protein-Coupled Receptors (GPCRs) and Their Accurate Visual Representation in Scientific Research

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Contact Info

Product

Resources

About