Open Journal of Genetics, 2012, 2, 11-17 OJGen
Published Online December 2012 (h ttp://
Published Online March 2012 in SciRes.
A brief review on the evolution of GPCR: conservation and
Zaichao Zhang1, 2,†, Jiayan Wu1,†, Jun Yu1, Jingfa Xiao1,*
1CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing,
2College of Life Science, Graduate University of Chinese Academy of Sciences, Beijing, China
Received 2012
G-protein couple receptors (GPCR) possess diversi-
fied functio ns and they comprise a large protein su-
perfamily in cellular signaling. Numerous identifica-
tion methods for GPCR have been employed and
versatile GPCR types are discussed. Although they
share conserved transmembrane structural topology,
alignment results of all GPCR show no significant
sequence similarities. Each GPCR type distributes
diversely in different evolutionary hierarchies of eu-
karyotes, but it has a distinctive boundary in the era
of metazoan. The common ancestor of GPCR meta-
botropic glutamate receptor include s
7-transmembrane structure and venus flytrap mod-
ule, which is probably evolved from a compound of
bacteriorhodopsin and periplasmic binding protein.
Many investigations focus on fine structure shaping
and GPCR classification. Here, we briefly discuss
evolutionary dynamic mechanism of GPCR from the
perspective of classification, diversification and con-
Keywords: GPCR; Evolution; Classificatio n;
Diver sification; Conservation
G-protein couple receptors (GPCR) form the largest su-
perfamily of transmembrane proteins in cell signaling
mechanism. They vary dramatically in sequence align-
ment but share an identical structural topology [1]. The
primary function of GPCR is signal transduction by
sensing molecules from extracellular (e.g. hormones and
neurotransmitters) and mediating intracellular signaling
through coupling to specific G proteins [2 ]. They are
also essential targets for nearly 50% of all currently used
therapeutic drugs [3]. GPCR contain receptors for
amines, peptides, amino acids, glycoproteins,
prostanoids, phospholipids, fatty acids, nucleosides,
nucleotides, Ca2+ ions as well as sensory receptors for
different exogenous ligands as odorants, bitter and sweet
tastants, pheromones, and photons of light and so forth
[4]. Currently thousands of GP CR have been found in
human genome, about 350 of them detect hormones,
growth factors, and other endogenous ligands, but about
150 of them are still unknown [5].
Studies on GPCR evolution have been done in several
eukaryotic species, which provide insights from different
perspectives [5-12]. However, our understanding of
GPCR evolution is merely based on extant genome se-
quences since most ancient eukaryotic species ever lived
on earth are extinct. With an increasing number of
GPCR sequences, they could be concluded into different
categories by different classification systems. Because
their sequences are dramatically multiform while a bar-
rel structure is shared by all GPCR. Here, we share a
specific evolutionary view of GPCR on their classifica-
tion, diversification and co nservat io n.
2.1. GPCR prediction approaches
Although many GPCR prediction approaches have been
proposed during past two decades, a great number of
GPCR types are still vexed. The previous common me-
thodology is seque nce similarity searching in protein
databases (e.g. NCBI, ExPASy, PIR, UniProt), which is
mainly based on pairwise sequence alignme nt such as
BLAST and BLAT [13, 14]. But it is difficult to identify
GPCR successfully because there is no significant se-
quence similarities shared. To solve this problem, some
statistical and machine learning approaches have been
developed for GPCR prediction, such as HMM [15-17],
statistical analysis method [18], covariant discriminant
algorithm [19, 20], support vector machine method[21,
22], bagging classification tree [23] and SVM-DWT ap-
proach [24]. Online tools have been developed as well.
These authors contribute equally.
*Corresponding author.
Z. Zhang et al. / Open Journal of Genetics 2 (2012) 11-17
Copyright © 2012 SciRes. OJGen
Table 1. Primary classification systems.
Leading Author
Ye ar Metho ds Description
Kolakowski L.F.
Jr 1994 Integral component of the design of GCRDb A-F classification system (A-C for multicellular animals)
Lars Josefsson 1999 PS I-BLAST searching method One large clade and two smaller ones
Richard C. Grau 2001 BLAST and separate position-specific matrices 34 distinct clusters as GPCR groups
Rachel Karchin 2002 Support vector machines approach A-E classes with various subclasses
Lapinsh.M 2002 Alignment-independent extraction of chemicals Set up a data set of 929 rhodopsin-like receptor
Robert Fre-
driksson 2003 Alignment, bootstrapping and Fingerprint GRAFS in human GPCR
Huang Ying 2 004 Using a bagging classification tree algorithm
An accuracy of 91.1% for sub-family and 82.4% for
sub-sub -fa mi ly
Thora Bjarna-
dottir 2006 BLAST, BLAT, and HMM searches GPCR varies between the main GRAFS families
For instance, GPCRTree is an online hierarchical classi-
fications webserver [25, 26]. In recent, a domain evalua-
tion model for GPCR classification was also launched
[27]. Each method has its own advantages and short-
comings, but HMM method is generalized from a mix-
ture model and has been widely used, compared with
other algorithms. The hidden variables that control the
mixture component to be selected for each observation
are related through a Markov process rather than inde-
pendent of each other.
2.2. GPCR classifications
Based on different prediction approaches, several classi-
fication systems (Table 1) and GPCR databases (Table 2)
have been established. The first GPCR database with
A-F classification system has been constructed and
adopted for almost a decade [1, 28]. With GPCR data
accumulation, recently a novel GRAFS classification
system [29] has been established and extensively used
by latest studies [12, 30 ]. However, most classification
systems still consist of three primary families and other
mini-types that are still arguable [31-34]. These three
primary families in all are classified mainly based on
structure and functional similarity: rhodopsin-like re-
ceptor, secretin receptor and metabotropic glutamate
receptor. In brief, rhodopsin-like receptor family ac-
counts for 85% GPCR, which plays physiological roles
of visual and smell sense, and these receptors distrib ute
wid ely in mammalian genomes [30]. Rhodopsin-like
receptor also represents a widespread protein family that
includes hormones, neurotransmitters and light receptors;
secretin receptor exists in many mammalians and a few
are found in fungi. Receptors in this family mainly act
for hormones and neuropeptides; metabotropic gluta-
mate receptor performs a variety of functions in beha ve-
ioral and mood regulations, as well as in the central and
peripheral nervous systems [35]; other GPCR mini-types
like fungal mating pheromone, frizzled/smoothened and
orphan receptors combine a minority of GPCR, and are
charged with their significantly specific duty ind ivid u -
ally. The r e are still some GPCR types are disputable. For
example, cyclic AMP receptor (cAMP) is recognized as
a second messen ger and important in many biological
processes. Some scientists define them as GPCR class E
category [26, 28 , 36] while others clarify cAMP as class
F [1]. GPCRDB listed eight sequences of cAMP as a
main category in Version 10.12.1 while it is no more
identified as GPCR in Version 11.3.4. A recent research
explains that cAMP receptor family is found in inverte-
brates and lost in vertebrates [12].
2.3. Signatures of GPCR
GPCR are evolutionary old and evidence shows that
specific GPCR signatures can be found in all eukaryotic
species [37]. T he y arrange t hemsel ves into a tertiary
structure resembling a barrel in cellular membrane with
two extracellular terminuses (Figure 1).
Figure 1. A general structure of GPCR. (sample sequence is
metabotropic glutamate receptor d1lx28_sacko metabotropic
Z. Zhang et al. / Open Journal of Genetics 2 (2012) 11-17
Copyright © 2012 SciRes. OJGen
glutamate receptor from GPCRDB at Its
transmembrane structure was predicted by TMHMM at and figure was
drawn at
Table 2.
Current GPCR databases with main features.
Databa s es We bs i t e Specific feature s
GCRDb No more in service First GPCR database with A-F classification system
GPCRDB 7t m/ Using HMM and provides GPCP sequences and 3D structur es
IUPH AR www.iup har-db .org/ index. jsp Mainly focusing on drug target design
GPCR RD zhanglab.ccmb. med .u mich . edu / GPCRR D/ Primarily building GPCR 3D structure models
The GDS dataset Part of the BIASPROFS project
GPC R-SSFE www.fmp -berlin .i nfo/ ssfa0/ databa se-gpcr -ssfe/
Storing template predictions, identifying sequence and motifs and
homology of rhodopsin-like receptor
The 7-transmembrane is ancient with highly conserved
structure as well as a length of 200-300 amino acids. The
length and specific sites of both terminuses vary greatly,
and N-ter mi n u ses of different GPCR contain numerous
diversified motifs and domains [4]. Each GPCR family
has its own features but it is still not obviously to tell
their classifications by observing these features [38].
Most GPCR metabotropic glutamate receptors have
longer N-terminus with specific motifs or domains where
ligand -binding site is localized on to receive signals from
extracellular [39]. This is not the case for most rhodop-
sin-like receptors but some disputing ones like hormone
receptor, which has long terminus. Rhodopsin-like re-
ceptors also share small molecule ligands, which may
reduce the structural constraints for ligands binding and
enhance the evolutionary survival of duplicated genes,
especially after the appearance of metazoan around 500
million years ago [40].
All GPCR 7-transmembranes contain tm1~7 topology
while families with long extracellular N-termi nus consist
of different motifs or domains including cystein-box,
hormone binding domain, Arg-Gl y-Asp motif, immu-
noglo b ulin mucin like stalk and so forth. Domains on
N-terminus of GPCR families would mediate cell-to-cell
adhesion or cell migration either by binding to compo-
nents from extracellular environment or by interacting
with membrane proteins from other cells [4]. It is also
reported recently that at least 30 GPCR types with long
N-terminus containing Ser/Thr-rich motifs found in hu-
man genome [41].
3.1. Current deductions for GPCR evolution
Many studies have provided insights on GPCR evolution
focusing on a certain type of GPCR across different spe-
cies or on populations within only one species [40]. In
2001, Graul and Sadee presumed that a refined GPCR
ancestry evolution may facilitate database annotation for
GPCR orphan receptors [42]. Simultaneously, Fredriks-
son and Schioth claimed the repertoire of trace amine of
GPCR would be one of most ancient GPCR [43]. The
first structure signature of GPCR rhodopsin-like recep-
tors in eukaryotic species was found in several protos-
tome around 700 Mya [44]. As for secretin receptor,
Cardoso and colleagues put forward a hypothesis that the
putative ancestral receptors of this rhodopsin-like recep-
tor is proposed to be more like the deuterostome
CAL/CGRP/CRF receptors and evolved into other types
~500 Mya [7]. The ancestor of metabotropic glutamate
receptor was proposed to be found in slime molds and
sponges [35]. By means of mining GPCR evolutionary
data from fossils, Torsten Schoneberg provided several
clues that the phylogenetically oldest GPCR might in-
clude fungal pheromone receptors, cAMP and gluta-
mate-receptor-like receptors [10-12]. A schematic pres-
entation of GPCR evolution superfamily shows that ad-
hesion and frizzled as well as large rhodopsin family are
children of the cAMP. Besides, rhodopsin family is par-
ent to sensory family, taste2 and vomeronasal type1 as
well as the nematode chemoreceptor family [11].
3.2. GPCR distribution in eukaryotes
The subfamilies of GPCR consist of various types in
different evolutionary period and they have evolved in
distinct protein superfamilies since the appearance of
metazoan. Protists, thought to be the most ancient euka-
ryotes, contain all GPCR metabotropic glutamate recep-
tors [45] and part of rhodopsin-like receptors that have
longer N-terminus. Longer N-terminus is likely to be
more ancient because none of short N-terminus is found
in protists. No one could tell exactly what events brought
the period of metazoan, but evidence by fossil studies
shows that protists and fungi appeared before the ap-
pearance of metazoan [46]. However, whole genome
duplication event took place after that era, which moti-
vated GPCR expeditiously evolved into more various
types acting specific signaling roles. The more advanced
a species is, the more diversified GPCR the species
might have. This is because GPCR play essential role in
Z. Zhang et al. / Open Journal of Genetics 2 (2012) 11-17
Copyright © 2012 SciRes. OJGen
advanced species that need more complicated signal
connections in versatile cells and tissues. Furthermore,
evidences also substantiate that the subfamilies of rho-
dopsin-like receptor contain 35.5% introns and these in
secretin receptor are highly conserved in their position
whi le introns in metabotropic glutamate receptor seldom
exist [8]. We make a conclusion that GPCR with longer
N-terminus would be more likely the ancestor of GPCR.
Besides, subfamilies of rhodopsin-like receptor explo-
sively expanded after the occurrence of metazoan.
3.3. The origin of 7TM and VFTM
Metabotropic glutamate receptors are found in more an-
cient species than metazoan [12] and this family sym-
bolizes the earliest eukaryotic origin because of longer
N-terminus and fewer introns. Numerous evidences have
demonstrated that bacteriorhodopsin, an ancient light
energy related protein widely presenting in prokaryotes,
shares crystal structure and conserved positions with
GPCR 7-transmembrane topology albeit sequences
alignment of GPCR 7-transmembrane and bacteriorho-
dopsin is quite low [47-51].
It has already been identified that 7-transmembrane
has a similar structural topology with structures in pro-
karyote genomes such as light-sensitive, proteo-, bacte-
rio- and halorhodopsins [52, 53]. Interestingly, we find
that 75% bacteriorhodopsin sequences contain intact
seven transmembrane topology and the phylogenetic tree
of bacteriorhodopsin and 7-transmembrane shows meta-
botropic glutamate receptor much closer with bacterior-
hodopsin. We infer that the origin of 7-transmembrane is
possibly evolved from bacteriorhodopsin topology.
Periplasmic binding proteins (PBP), an important sig-
naling receptor in bacteria, is identified highly resemble
with a specific structure entitled venus flytrap module
(VFTM) [54]. PBP consists of two large lobes close the
bound ligands (possibly cys-riched domains), resembling
a similar structure like VFTM [9, 55]. The li-
gand -binding domain in N-terminus of metabotropic
glutamate receptor is homology to PBP in sequence
alignment [56]. Functional divergence plays an essential
role in characterizing the functions of VFTM, which are
also been shaped in the evolution of metabotropic gluta-
mate receptor [9]. The N-terminus of metabotropic glu-
tamate receptor mostly perhaps evolved from ancient
PBP and afterwards combined with bacteriorhodopsin
via cystein-rich region to form the prototype of metabo-
tropic glutamate receptor.
The recent advance in next generation sequencing and
genome sequences analysis methods has greatly reshaped
our understanding of GPCR. We aim to describe the re-
pertoire, feature and distribution as well as prototype of
GPCR protein superfamily. With the accumulation of
eukaryotic genome data, a huge amount of work is being
under taken to annotate and clarify the relationship of
GPCR for different species from advanced species to
inferior organisms to obtain a comprehensive overview
of the entire GPCR evolutionary process. We believe it is
essential to understand the particular details affecting the
rapidly evolution GPCR subclasses after the appearance
of metazoan. Completely understanding GPCR evolution
might not only help us predict some potentially impor-
tant features of GPCR but also bring a horizon for un-
classified GPCR in fut ur e.
We are deeply appreciated to grant (2012AA020409)
from the National Programs for High Technology Re-
search and Development (863 Program), the Ministry of
Science and Technology of the People’s Republic of
China for supporting this work.
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