GPCR Pipeline | Background Page

G-Protein Coupled Receptors

G-Protein Coupled Receptors (GPCRs) are a large and diverse superfamily of membrane receptor proteins [1]. GPCRs are characterized by a seven transmembrane structure with an external N-terminus and an internal C-terminus [2]. This leads to the superfamily sometimes being referred to as 7-transmembrane receptors [2]. The cytoplasmic face couples to a heterotrimeric G-protein complex [3]. When the receptor ligand binds to the external face, the G-protein complex dissociates from the receptor [2]. The G-protein alpha-subunit is activated by exchange of GDP for GTP and proceeds to initiate the signaling cascade [4].
GPCR Structure
Figure 1: Dissociation of the G-protein subunits and activitation of the alpha-subunit[15]

GPCRs are of great interest to researchers as it is estimated that 50-60% of all pharmacological drugs target GPCRs [5]. For example, psuedoephedrine interacts with the adregenergic receptors in order to act as a decongestant [6]. Another example is haldol, which affects dopamine and serotonin receptors and is used in the treatment of schizophrenia [7]. G-protein coupled receptors are prevalent in vision, neurotransmission, immunology, and many other systems [1,2,3].
GPCR Structure
Figure 2: Bovine Rhodopsin structure, PDB-ID: 4X1H

GPCR Classification

GPCRs can be classified into several distinct classes. The GRAFS system places the receptors into 5 distinct classes: Glutamate, Rhodopsin-like, Adhesion, Frizzled/Taste2, and Secretin [8]. The GRAFS system applies specifically to vertebrate GPCRs [8]. An alternative classification which applies to both vertebrate and invertebrate GPCRs is the IUPHAR system [9]. The IUPHAR classification is split into the Class A (Rhodopsin-like), Class B (secretin-like), Class C (metabotropic glutamate), Class D (fungal mating pheromone receptors), Class E (cAMP receptors), Class F (frizzled/smoothened) [9]. The classes D and E are unique to invertebrates [9].
GPCR Structure
Figure 2: Distribution of GPCR classes in the SwissProt database
Data retrieved: January 3, 2016

GPCR Pipeline

Our pipeline utilizes sequence similarity, transmembrane structure, and dipeptide composition to determine if a protein sequence is a GPCR. A BLAST [10] search determines if a given sequence bears close sequence similarity to known sequences (from the curated Uniref [11] database), operating under the assumption that sequence similarity may indicate sequence homology. PFAM utilizes protein domain profiles in order to identify families and functional domains [12]. PFAM can also help to determine class as there are several class-specific 7-transmembrane profiles available [12]. 7tm_1 denotes the Rhodopsin-like class, 7tm_2 denotes Secretin-like, 7tm_3 denotes the Glutamate class and several other profiles denote other classes [12]. Additionally, we utilize the conserved 7-transmembrane structure of GPCRs using TMHMM [13]. TMHMM predicts the number of transmembrane helices in a protein sequence using a hidden Markov model [13]. We use this prediction to check that the transmembrane structure of a predicted protein is congruent with GPCR 7-transmembrane structure. Additionally, we use GPCRpred, a SVM which uses the dipeptide composition of a protein sequence to predict whether a protein is a GPCR and which class it belongs to [14]. By combining these various tools and approaches we can make a more accurate prediction as to whether or not a query protein sequence is a GPCR.


  1. Bjarnadóttir, T. K., et al. (2006). Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse. Genomics, 88(3), 263-273. PMID: 16753280
  2. Trzaskowski, B., et al. (2012). Action of molecular switches in GPCRs-theoretical and experimental studies. Current medicinal chemistry, 19(8), 1090. PMID: 22300046
  3. Rosenbaum, D. M., et al. (2009). The structure and function of G-protein-coupled receptors. Nature, 459(7245), 356-363. PMID: 19458711
  4. Wettschureck, N. and Offermanns, S. (2005). Mammalian G proteins and their cell type specific functions. Physiological reviews, 85(4), 1159-1204. PMID: 16183910
  5. Lundstrom, K. (2009). An overview on GPCRs and drug discovery: structure-based drug design and structural biology on GPCRs. In G Protein-Coupled Receptors in Drug Discovery (pp. 51-66). Humana Press. PMID: 19513641
  6. Vansal, S. S., and Feller, D. R. (1999). Direct effects of ephedrine isomers on human ?-adrenergic receptor subtypes. Biochemical pharmacology, 58(5), 807-810. PMID: 10449190
  7. Tollefson, G. D., et al (1997). Olanzapine versus haloperidol in the treatment of schizophrenia and schizoaffective and schizophreniform disorders: results of an international collaborative trial. American Journal of Psychiatry, 154(4), 457-465. PMID: 9090331
  8. Schiöth, H. B. and Fredriksson, R. (2005). The GRAFS classification system of G-protein coupled receptors in comparative perspective. General and comparative endocrinology, 142(1), 94-101. PMID: 15862553
  9. G protein-coupled receptors | G protein-coupled receptors | IUPHAR/BPS Guide to PHARMACOLOGY. (n.d.). Retrieved January 3, 2016, from
  10. Altschul, S. F., et al. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research,25(17), 3389-3402. PMID: 9254694
  11. Suzek, B. E., et al. (2007). UniRef: comprehensive and non-redundant UniProt reference clusters.Bioinformatics, 23(10), 1282-1288. PMID: 17379688
  12. Finn, R.D., et al (2015). The Pfam protein families database: towards a more sustainable future. Nucleic acids research, gkv1344. PMID: 26673716
  13. Krogh, A., et al. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of molecular biology, 305(3), 567-580. PMID: 11152613
  14. Bhasin, M. and Raghava, G. P. S. (2004). GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors.Nucleic Acids Research, 32(suppl 2), W383-W389. PMID: 15215416
  15. Li, J., et al (2002) The molecule pages database. Nature, 420(6916), 716-717. PMID: 12478304

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This work is supported by the USDA-NIFA grant 2012-38422-19910, and the NIMHD grant 2G12MD007592
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