GPCRDB: information system for G protein-coupled receptors

Vroling, Bas; Sanders, Marijn P. A.; Baakman, Coos; Borrmann, Annika; Verhoeven, Stefan; Klomp, Jan P.G.; Oliveira, Laerte; Vlieg, Jacob de; Vriend, Gert

doi:10.1093/nar/gkq1009

Cited by 126 publications

(131 citation statements)

References 45 publications

(42 reference statements)

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“…Because of its specificity, data were taken from GPCRDB, which is defined [36] as a molecular-class information system that collects, combines, validates and disseminates large amounts of heterogenous data on GPCRs. GPCRDB divides the GPCR superfamily in 5 families: the class A Rhodopsin like, the class B Secretin like, the class C Metabotropic glutamate/pheromone, Vomeronasal receptors (V1R and V3R) and Taste receptors T2R.…”

Section: Methodsmentioning

confidence: 99%

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Cruz-Barbosa

Vellido

Giraldo

2014

Med Biol Eng Comput

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G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

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Section: Methodsmentioning

confidence: 99%

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Cruz-Barbosa

Vellido

Giraldo

2014

Med Biol Eng Comput

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“…Whereas the identity of these residues is not absolutely conserved, the hydrophobic nature and helical compatibility of these residues is strictly conserved among all chemokine receptors (Fig. 5D) and other GPCRs (10). Two of these residues were deemed critical based on substitution to proline (L244 6.40 P and L246 6.42 P).…”

Section: Significancementioning

confidence: 99%

Signal transmission through the CXC chemokine receptor 4 (CXCR4) transmembrane helices

Wescott

Kufareva

Paes

et al. 2016

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

112

183

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The atomic-level mechanisms by which G protein-coupled receptors (GPCRs) transmit extracellular ligand binding events through their transmembrane helices to activate intracellular G proteins remain unclear. Using a comprehensive library of mutations covering all 352 residues of the GPCR CXC chemokine receptor 4 (CXCR4), we identified 41 amino acids that are required for signaling induced by the chemokine ligand CXCL12 (stromal cell-derived factor 1). CXCR4 variants with each of these mutations do not signal properly but remain folded, based on receptor surface trafficking, reactivity to conformationally sensitive monoclonal antibodies, and ligand binding. When visualized on the structure of CXCR4, the majority of these residues form a continuous intramolecular signaling chain through the transmembrane helices; this chain connects chemokine binding residues on the extracellular side of CXCR4 to G proteincoupling residues on its intracellular side. Integrated into a cohesive model of signal transmission, these CXCR4 residues cluster into five functional groups that mediate (i) chemokine engagement, (ii) signal initiation, (iii) signal propagation, (iv) microswitch activation, and (v) G protein coupling. Propagation of the signal passes through a "hydrophobic bridge" on helix VI that coordinates with nearly every known GPCR signaling motif. Our results agree with known conserved mechanisms of GPCR activation and significantly expand on understanding the structural principles of CXCR4 signaling.T he CXC chemokine receptor 4 (CXCR4) belongs to the G protein-coupled receptor (GPCR) superfamily of proteins, the largest class of integral membrane proteins encoded in the human genome, comprising greater than 30% of current drug targets. Deregulation of CXCR4 expression in multiple human cancers, its role in hematopoietic stem cell migration, and the utilization of CXCR4 by HIV-1 for T-cell entry, make this receptor an increasingly important therapeutic target (1). One FDA-approved drug against CXCR4 is currently on the market (Mozobil, for hematopoietic stem cell mobilization), and multiple additional drugs against this target are in development for oncology and other indications (2).The crystal structures of class A GPCR superfamily members in their active and inactive conformations (reviewed in refs. 3 and 4) provide unprecedented insight into the structural basis of ligand binding, G protein coupling, and activation of GPCRs via rearrangements of transmembrane (TM) helices. GPCR helices V and VI in particular, and in some cases III and VII, are known to undergo significant conformational changes upon activation (5-7). However, static images alone have not been able to explain the residue-level mechanisms underlying the dynamic helical shifts that mediate GPCR signal transduction. Additionally, only inactive state structures have been solved for CXCR4 and most other GPCRs (8,9). Over the last two decades, extensive mutagenesis studies of GPCRs in general [collectively describing >8,000 mutations (gpcrdb.org)] and of CX...

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“…Employing PLIF was proven to increase the quality of SBVS to identify ADRB2 ligands [12,18]. Interestingly, systematic filtering on the PLIF bitstrings could also pinpoint the important molecular determinants in the ADRB2-ligand binding [18,19]. By correlating the results from the systematic filtering on PLIF bitstrings [18] to the site-directed mutation data stored in GPCRDB [19], the important molecular determinants in the ADRB2-ligand binding were identified, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…By correlating the results from the systematic filtering on PLIF bitstrings [18] to the site-directed mutation data stored in GPCRDB [19], the important molecular determinants in the ADRB2-ligand binding were identified, i.e. D113, S203 and N293 [18,19]. Notably, other machine learning methods, e.g.…”

Section: Introductionmentioning

confidence: 99%

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

Istyastono¹

2016

AIP Conference Proceedings

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GPCRDB: information system for G protein-coupled receptors

Cited by 126 publications

References 45 publications

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Signal transmission through the CXC chemokine receptor 4 (CXCR4) transmembrane helices

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

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