Graul RC and Sadee W Evolutionary Relationships Among G Protein-Coupled Receptors Using a Clustered Database Approach AAPS PharmSci 2001;
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article 12
(https://www.pharmsci.org/scientificjournals/pharmsci/journal/01_12.html).
Evolutionary Relationships Among G Protein-Coupled Receptors Using a Clustered Database Approach
Submitted: October 10, 2000; Accepted: April 16, 2001; Published: May 4, 2001
Richard C. Graul1 and Wolfgang Sadee2
1Incyte Genomics, Palo Alto, CA 94304
2Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94143
|
Correspondence to:
Wolfgang Sadee
Telephone: 415-476-1947
Facsimile: 415-476-0464
E-mail: sadee@cgl.ucsf.edu
|
Keywords:
GPCR
Clustered Database
Peptides
Genetics
|
Abstract
Guanine nucleotide-binding protein-coupled receptors (GPCRs) comprise large
and diverse gene families in fungi, plants, and the animal kingdom. GPCRs appear
to share a common structure with 7 transmembrane segments, but sequence
similarity is minimal among the most distant GPCRs. To reevaluate the question
of evolutionary relationships among the disparate GPCR families, this study
takes advantage of the dramatically increased number of cloned GPCRs. Sequences
were selected from the National Center for Biotechnology Information (NCBI)
nonredundant peptide database using iterative BLAST (Basic Local Alignment
Search Tool) searches to yield a database of ~1700 GPCRs and unrelated membrane
proteins as controls, divided into 34 distinct clusters. For each cluster,
separate position-specific matrices were established to optimize sequence
comparisons among GPCRs. This approach resulted in significant alignments
between distant GPCR families, including receptors for the biogenic
amine/peptide, VIP/secretin, cAMP, STE3/MAP3 fungal pheromones, latrophilin,
developmental receptors frizzled and smoothened, as well as the more distant
metabotrobic glutamate receptors, the STE2/MAM2 fungal pheromone receptors, and
GPR1, a fungal glucose receptor. On the other hand, alignment scores between
these recognized GPCR clades with p40 (putative GPCR) and pm1 (putative GPCR),
as well as bacteriorhodopsins, failed to support a finding of homology. This
study provides a refined view of GPCR ancestry and serves as a reference
database with hyperlinks to other sources. Moreover, it may facilitate database
annotation and the assignment of orphan receptors to GPCR families.
Introduction
Guanine nucleotide-binding protein-coupled receptors (GPCRs) represent a
large class of membrane proteins with diverse functions. Also termed serpentine
receptors, GPCRs are polytopic membrane proteins that share a common structure
with 7 transmembrane segments (7-TMSs)1 . These can be
identified by hydropathy analysis and are predicted to be a-helical structures, usually consisting of 20 to 24 amino
acids each. Online structural representations for the human µ opioid receptor,
for example, exist as a two-dimensional (2D) schematic (https://www.gpcr.org/7tm/seq/vis/OPRM_HUMAN/OPRM_HUMAN.html ).
For more online information about GPCRs, see the GPCR database, GPCRDB (https://www.gpcr.org/7tm/ .
The literature contains references to several distinct GPCR families, all
sharing a 7-TMS topology. Kolakowski2 and Horn et al3 have classified these GPCRs into several major families that
share only minimal sequence similarity, as follows: family A: rhodopsin,
olfactory, biogenic amine, peptide receptors; family B: vasoactive intestinal
peptide (VIP), calcitonin, glucagon, secretin receptors; family C: metabotropic
glutamate, Ca2+ -sensing, g-aminobutyric acid
(GABA), apical vomeronasal receptors; family D: fungal pheromone P- and a-factor (STE2/MAM2); family E: fungal pheromone A- and
M-factor (STE3/MAP3) receptors; family F: cyclic adenosine monophosphate (cAMP)
receptors of Dictyostelium . In addition, a number of new putative GPCR
families have been discovered with varying degrees of similarity to the
established families, including frizzled, smoothened, basal vomeronasal
receptors, and bride of sevenless (BOSS) of Drosophila and mammals,
latrophilin, several plant GPCRs, another yeast GPCR, GPR1, as well as other
mammalian sequences, p40 and pm1. Lastly, the complete sequencing of the
Caenorhabditis elegans genome yielded ~1100 putative GPCR genes,
including numerous chemoreceptors4-9 .
GPCRs recognize a variety of ligands and stimuli (eg, light, ions, biogenic
amines, nucleosides, lipids, amino acids, and peptides). Signal transduction is
accomplished by coupling via guanine nucleotide-binding proteins (G proteins) to
various secondary pathways involving ion channels, adenylyl cyclases, and
phospholipases. Furthermore, GPCRs may also couple to other proteins - for
example, those containing PDZ domains9 . We have recently
shown that opioid receptors interact with calmodulin at the same domain required
for G protein coupling10 . Hence, GPCRs may have many more
protein signaling partners than we currently realize. Finally, experimental
evidence has yet to be provided for many of the newly cloned sequences presumed
to be GPCRs that they indeed couple via G proteins. Thus, the GPCR families are
extremely diverse in function and primary structure, but adhere to a common
topology of 7-TMSs. This feature and certain conserved motifs that do not
pervade all families serve to classify a newly identified sequence as a possible
GPCR.
Despite compelling similarity in their overall structures, the lack of
statistically significant sequence similarity among several GPCR families raises
the question whether all GPCRs arose through common ancestry. Thus, the
VIP/secretin receptors and the metabotropic glutamate receptors are seemingly
unrelated to other peptide and biogenic amine receptors. Furthermore, until
recently no significant sequence identities had been established between
mammalian GPCRs and the fungal pheromone receptors.
Several other families share the prevalent 7-TMS architecture, most notably
bacteriorhodopsin, photoreceptors-proton pumps of archebacteria, for which
direct molecular structure information is available. While the 7-TMS topology of
the bacteriorhodopsins and GPCRs is similar1 , the relative
spatial arrangements of the TMSs differ in some details. Moreover, the
bacteriorhodopsin ligand, retinal, is covalently attached to a Lys residue in
the seventh TMS at a location identical to that for the retinal attachment site
in rhodopsin, a true GPCR. Yet, any evolutionary relationship between
bacteriorhodopsin and GPCRs is unproven because bacteriorhodopsin and GPCR share
minimal sequence similarity1 .
The seeming lack of reported significant sequence similarities - despite
similar architecture and function - may be explained by 1) structural
convergence of unrelated families, 2) genetic drift (divergence), 3)
difficulties in analyzing polytopic membrane proteins, 4) difficulties in
analyzing large protein families, and 5) limitations inherent in common sequence
analysis methods.
Sequence analysis of polytopic membrane proteins poses particular challenges.
The restricted amino acid composition of hydrophobic TMSs, the periodicity of
amphipathic a-helices, and the overall serpentine
structure combine to impede distinguishing homology from homoplasy1, 11-13 . Further, many polytopic membrane
protein families are large, composed of hundreds, and even thousands of
sequences, in the nonredundant peptide database at the National Center for
Biotechnology Information (NCBI https://www.ncbi.nlm.nih.gov )14 . This makes it difficult to sift relevant alignments from the
vast amount of data produced by a single Basic Local Alignment Search Tool
(BLAST https://www.ncbi.nlm.nih.gov/BLAST/ )
run. Specifically, the border between sequence similarities caused by chance or
convergence rather than true homology is blurred.
Another difficulty in evaluating alignments between polytopic membrane
proteins arises from the different physical environments and constraints to
which the TMS domains and loops/tails of GPCRs are subjected. The BLAST
heuristic rapidly identifies database sequences similar to a given query
sequence; however, a major simplification of BLAST is the use of a single
substitution matrix irrespective of residue position in a sequence14,15 . This could introduce errors in
estimating the similarity among protein sequences on the basis of a single
substitution frequency for each amino acid pair. Position Specific
Iterated-BLAST (PSI-BLAST https://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-2.html )
overcomes this limitation by using a substitution vector for each residue
position of a query sequence16 . Therefore, we employed
PSI-BLAST (in addition to BLAST) in the present study to evaluate sequence
similarities between loop/tail and TMS on a residue-by-residue basis. A similar
approach recently reported by Josefsson17 has yielded
significant alignments between many GPCRs, suggesting only 3 distinct clades
(superfamilies) - the huge rhodopsin-like family including many neurotransmitter
and hormone receptors and 2 smaller clades of the BOSS-metabotropic glutamate
receptors (MGR), and the STE2 fungal pheromone receptors. Our studies differ in
some details from the approach taken by Josefsson17 but generally support
Josefsson's finding of homology among the many distant GPCRs contained in the
rhodopsin-like superfamily. Moreover, our study finds significant sequence
similarity between the rhodopsin-like, BOSS-MGR-like, and STE2-like GPCRs, thus
supporting the hypothesis that all GPCRs arose from a common ancestor.
To find distant relationships, we took advantage of the rapidly growing
database of cloned sequences. If evolutionary relationships do exist, one would
expect sequences to emerge that provide convincing alignments between distant
families. In a first step, we used iterative BLAST (Iterative Neighborhood
Cluster Analysis, INCA https://itsa.ucsf.edu/~gram/home/inca/ )
18 analyses to extract ~1700 putative GPCR and other
membrane protein sequences from the public databases and establish a separate
database for our analyses (Table 1 ).
Another key aspect of our approach is the use of clusters of highly related
GPCRs to generate PSI-BLAST-constructed position-specific score matrices (PPSMs)
for each cluster individually16 . This avoids generating
problematic PPSMs that can arise from including spurious sequence alignments19 . Such alignment models are useful in detecting distant
evolutionary relationships16 . Further, our study implements
these PPSMs as a database using the IMPALA (Integrating Matrix Profiles And
Local Alignments) software package20 , also available from
NCBI (ftp://ftp.ncbi.nlm.nih.gov/Home.html ),
along with the standard BLAST distribution. Finally, our study compares the
IMPALA results with those using the corresponding Pfam (https://pfam.wustl.edu/ ) models for HMMER (https://hmmer.wustl.edu/ ). Pfam is a large
collection of hand-curated multiple sequence alignments and hidden Markov models
covering many common protein domains. Profile hidden Markov models (profile
HMMs) can be used to do sensitive database searching using statistical
descriptions of a sequence family's consensus. Commonly used PSI-BLAST searches
generate a PPSM that originates from a chosen seed sequence and therefore varies
with each alignment model. Both IMPALA and HMMER allow the user to search a
database of predetermined alignment models with a query sequence and thus to
draw inferences regarding the query sequence's structure or function.
The results of this study confirm the heterogeneity of the GPCR families but
also provide statistically significant alignments between distant families with
questionable ancestry. The relevance of these alignments is further tested by
asking whether equivalent domains of two sequences are aligned and what the key
conserved residues are. We also demonstrate that we can build PPSMs in a
semi-automated fashion that are comparable in sensitivity to the corresponding
Pfam models. Lastly, this database of GPCRs serves as a starting point for
addressing the evolution of GPCR structure and function in detail.
Materials and Methods
Experimental approach
We first established a separate GPCR database to develop appropriate
alignment models for each of the GPCR and unrelated control membrane protein
families. The database was established with the use of selected seed sequences
and the iterative BLAST program, INCA (https://itsa.ucsf.edu/~gram/home/inca/ ).
Using a single linkage clustering algorithm, INCA exhaustively retrieves all
related sequences better than a selected E-value from the entire NCBI (https://www.ncbi.nlm.nih.gov/ )
nonredundant database. This yields nonoverlapping clusters, each containing
closely related sequences. Subsequently, each cluster is further subdivided into
subclusters with high similarity, again using a single linkage clustering
algorithm, but with increased stringency. Both the clusters and subclusters are
used to calculate PPSMs (checkpoint files, alignment models). This establishes a
separate PPSM for each subfamily of closely related receptors. Each of these
PPSMs is used to query the database using a single iteration of PSI-BLAST
(https://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-2.html ).
This strictly avoids contamination of the substitution matrix by excluding the
possibility that any unrelated sequences spuriously contribute to the matrix.
For comparison to the PSI-BLAST results, we also used gapped BLAST (even though
the BLOSUM62 substitution matrix is less appropriate for membrane proteins) to
evaluate threshold alignments. Further, we developed a database of PPSMs using
the IMPALA package and compared results for these PPSMs with the results from
relevant hidden Markov (HMMER https://hmmer.wustl.edu/ )21 Pfam (https://pfam.wustl.edu/ ) models22 . Built from multiple alignments, profile HMMs capture
position-specific information regarding amino acid frequencies, as well as those
of insertions and deletions, whereas PPSMs are built from multiple pairwise
alignments, discarding information regarding insertions or deletions with
respect to a "master" query sequence. To establish benchmark criteria for
significant alignments, we include in the database a number of clusters
containing unrelated polytopic membrane proteins (ie, those containing multiple
TMSs). Last, alignments among distinct clusters are further evaluated by viewing
the location of the alignments and the presence of any conserved motives or
signature residues. No attempt was made to remove the highly variable N- and
C-termini; rather, alignments in these regions served as further criteria to
establish homology or evolutionary paths of added protein modules.
This approach differs from that taken by Josefsson17 ,
who adopted the following process. First, seed GPCR sequences were truncated at
their N- and C-termini. Second, selected seed sequences were used to scan the
NCBI nonredundant databases directly using only PSI-BLAST, with one or more
iterations, rather than performing PSI-BLAST on a defined GPCR database. Third,
only suspected GPCR sequences were analyzed; this makes it difficult to assess
threshold alignment values for establishing homology - arguably the most
difficult step in the interpretation. Thus, our current results - obtained with
yet more sequences that have become available since the Josefsson study17 - serve to further evaluate and refine the links between
distant GPCR families.
Sequence database and cluster analysis
The database used in this study is a subset of the NCBI nonredundant peptide
database consisting of approximately 460 000 sequences. Using INCA, we extracted
GPCRs and unrelated control sequences by selecting seed sequences, thus
establishing clusters of closely related proteins. Starting with 34 seed query
sequences, INCA identified 1720 sequences in 34 distinct clusters in ~500 BLAST
searches. INCA parameters were adjusted so that the resultant clusters do not
merge (ie, a sequence meets the alignment criteria for only 1 cluster in the
database searched). The seed sequences selected were true or putative GPCRs (28
sequences), bacteriorhodopsins (1 sequence), fungal opsins (1 sequence), and
other polytopic membrane proteins (4 sequences), thought to be unrelated to
GPCRs (er21, patc, pet1, and psn1). The resultant clusters are divided between
experimentals and controls, denoted with an asterisk (Table 1 ).
In some instances, we selected a manageable number of representative
sequences from each of the groups to be analyzed. Thus, sequences may be limited
to certain databases or individual species to reduce the number analyzed. The
5h1a cluster was seeded using a human serotonin receptor23 .
To limit the size of this cluster, the INCA search was restricted to human
sequences in the SwissProt database, resulting in 168 sequences. This cluster
represents what may be considered the main family of GPCRs (rhodopsin,
olfactory, biogenic amine, peptide, etc.). Because the C elegans genome
is fully sequenced, we established several clusters of putative C elegans
GPCRs. Thus, the 5ht cluster was created by starting with a serotonin receptor
from nematode24 and restricting the INCA search to C
elegans in the NCBI nonredundant database, resulting in 64 sequences. In
other cases we identified several clusters unique to nematode, without the need
for restricting the INCA search to that organism - for example, mgr, a cluster
of mgr-like proteins35 ; odr and odr10, chemoreceptors of
nematode38 ; and sra1, srab6, srd1, sre1, srg1, and sro1,
several serpentine receptors of nematode47 (Table 1 ).
Selection of control sequences of unrelated proteins
A number of 7-TMS proteins appear to be unrelated to GPCRs, both by function
and primary sequence, for example, the KDEL/HDEL family that serves as shuttle
vector for protein translocation between the Golgi apparatus and the endoplasmic
reticulum28 . Furthermore, there is no evidence that the
GPCRs may be related to other classes of polytopic membrane proteins with a
distinct TMS number. To provide for a negative control against which our GPCR
alignments can be compared, we selected the sequences of 4 distinct groups of
polytopic membrane proteins thought to be unrelated to GPCRs (Table 1 ). These are KDEL/HDEL with 7-TMS (er21
cluster, 21 sequences), H+/dipeptide transporters with ~12 TMSs (pet1, 76
sequences), the regulatory membrane protein family patched (patc, 52 sequences;
patched is a ~12-TMS membrane protein in the signaling pathway between sonic
hedgehog and smoothened - the latter considered a true GPCR [30]), and the
presenilins with ~9 TMSs (psn1, 43 sequences; presenilins are thought to be
involved in the etiology of Alzheimer's disease [45]). The best score of any of
these sequences aligned with any GPCR served as an arbitrary cutoff point for
considering the possible significance of an alignment among GPCRs themselves.
Bacteriorhodopsins served as a test case because of their structural and
functional similarity to true GPCRs. Last, fungal opsins from S
cerevisiae (YRO2) are included because of significant alignments with the
bacteriorhodopsins18, 56 .
Database subclustering
Sequence comparisons of clusters were performed using the gapped BLAST
program, blastpgp, version 2.1.2. Comparisons of sequences within the same
cluster were used to compute subclusters using a single linkage algorithm.
Sequentially, every sequence is used as a blastpgp query against a subset
database containing every sequence in its own cluster. Using the "l" option, it
was possible to restrict database searches to the subset specified by the list
of gi codes in a given cluster. We used the "z" flag to establish the effective
database size as 106. This allows for direct comparison of E-values resulting
from different subsets of the database. This also permits sequences to be added
to, or removed from, our analysis as needed without affecting our statistical
measure. The actual total number of letters in the database was 800 367. We did
not filter the query sequence for low complexity. Sequences with E-values better
than 10-40 are merged into the same subcluster. This serves to establish a set
of highly related sequences for developing individual PPSMs. Clusters have the
suffix 0, while subclusters are numbered 1 to n (Table 2 ).
Alignments between protein sequences in different clusters
Each sequence in each cluster was compared with each sequence or model from
all other clusters, using gapped BLAST, PSI-BLAST, IMPALA, and HMMER. For
further evaluation, we considered only the best pairwise alignment between any 2
clusters. Alignments between clusters of membrane proteins thought to be
unrelated to each other served as controls to establish a threshold of possible
significance. Any alignment with an E-value better than those involving any of
the controls was considered potentially significant within our database. The
number of controls was not strictly matched with the number of experimentals;
however, many of the experimentals have known evolutionary relationships and may
thus serve as positive controls. We did not filter the query sequence for low
complexity; however, we manually removed several low complexity, hydrophilic
alignments.
Gapped BLAST analysis
Sequentially, every sequence in 1 cluster is used as a blastpgp query against
a subset database containing every sequence in every other cluster. The highest
scoring pairwise comparisons are tabulated in Table 3 . These can be viewed by clicking on
the respective cell in the table.
PSI-BLAST analysis
For PSI-BLAST analysis, we first ran blastpgp as before. We then generated
PPSMs for each of the clusters and subclusters. The optimal scoring sequence is
selected from each cluster/subcluster, and PSI-BLAST is iteratively run against
that cluster/subcluster subset database until convergence occurs. The resultant
PPSM represents that cluster/subcluster. We used each PPSM as a blastpgp query
against a subset database containing every sequence in every other cluster. The
highest scoring pairwise comparisons are tabulated in Table 4 . Due to a limitation of the blastpgp
program, only clusters and subclusters with more than one sequence are used to
generate PPSMs.
IMPALA analysis
The PPSMs generated during the PSI-BLAST analysis were assembled into an
IMPALA database. Sequentially, every sequence in one cluster is used as an
IMPALA (version 1.1) query against a database containing the PPSMs derived from
the other cluster. The highest scoring pairwise comparisons are tabulated in Table 5 . An advantage of IMPALA is that one
may search a database of alignment models with a query sequence in a single
search, whereas in PSI-BLAST, one would have to perform searches for each of the
models. Also, IMPALA uses an improved scoring technique that considers the
composition of the query sequence.
Pfam/HMMER analysis
As a PPSM is a simplified HMM, we wanted to see how our semi-automatically
generated PPSMs would compare with the hand-curated counterparts contained in
Pfam. We selected HMMER Pfam models representative of the clusters under
analysis. Sequences from 1 cluster are used as an hmmsearch (version 2.1.1)
query against each Pfam model. We set the database size to 2000 sequences. The
highest scoring pairwise comparisons for standard Pfam models are tabulated in
Table 6 .
TMS annotation
Selected alignments are annotated with TMSs to aid alignment evaluation. When
available, the SwissProt annotation is used, accessible through the Entrez (https://www.ncbi.nlm.nih.gov/Entrez/ )
GenPept display at the NCBI. Otherwise, TMSs are predicted using the hidden
Markov model topology prediction Web application, hmmtop (https://www.enzim.hu/hmmtop/ ) (default
settings)57 . Generally, we consider the SwissProt
annotations to be more reliable, as they have been curated. Occasionally, hmmtop
over- or underpredicts TMSs. Also, hmmtop identifies signal peptides as TMSs.
Signal peptides may be present at the N-terminus, particularly when there is a
large extracellular domain present. Finally, not all analyzed sequences are full
length. Our dataset includes fragment proteins. We eliminated 24 putative GPCRs
with more than 9 predicted TMSs from C elegans because we suspected that
they may be chimeric proteins.
Error versus coverage plots
We compared the performance of BLAST, IMPALA, and HMMER/Pfam. We used the
5h1a and vipr clusters as well as the corresponding PPSMs and Pfam models to
analyze our database sequences. We considered the 1285 sequences from the
following 19 clusters as GPCRs, or true positives: 5h1a, 5ht, car1, friz, latr,
oa1, odr, odr10, olf1, pe22, sra1, srb6, srd1, sre1, srg1, sro1, ste3, vipr, and
vn1. We considered the 192 sequences from the following 4 clusters as non-GPCRs,
or false positives: er21, patc, pet1, and psn1. Each of these 1477 sequences was
used as a query against both the 5h1a and vipr clusters. We collected selected
Expect values (E-values). For BLAST, the
top sequence hit was used. For IMPALA, the top scoring PPSM hit was used. For
HMMER/Pfam, a single profile HMM hit was used. Error per query sequence (ie,
identification of a negative control alignment as a false positive) is a measure
of selectivity. Coverage of true positives is a measure of sensitivity. We
plotted error versus coverage58 , shown in Figure 1 .
Evolutionary dendrograms
Data from BLAST (Table 3 ), PSI-BLAST (Table 4 ), and IMPALA (Table 5 ) were used to compute cluster
dendrograms. For those clusters connected by significant E-values, we considered
the correlation between E-value and evolutionary distance and computed a
"distance" matrix from the E-values. This matrix was read into the Fitch program
of the PHYLIP (https://evolution.genetics.washington.edu/phylip.html )
package59 . We set all branches to uniform length, so as not
to imply that the dendrograms represent actual evolutionary distance. We used
Drawtree, also from the Phyllip package, to generate the graphical dendrograms, shown in Figure 2 .
Results
Details of the 34 seed sequences for iterative BLAST (INCA) are provided in
Table 1 . Minimal and maximal E-values were
selected to provide nonoverlapping sequence clusters of highly related
sequences. These were further divided into subclusters as described (Table 2 ). The most abundant residues at each
position for clusters and subclusters can be viewed by clicking on the
respective cluster name. Subsequently, a gapped BLAST analysis was performed
with each sequence in each cluster against all other sequences. While the
default substitution matrix (BLOSUM62) may not be optimal for comparing membrane
proteins, it achieves an estimate of relatedness among sequence clusters that
can serve for further analysis and for comparison with the results obtained with
the PSI-BLAST approach discussed below.
The results of the gapped BLAST analysis are listed in Table 3 , providing the scores of the best
alignment between any sequences in 2 different clusters. Significant E-values
were determined as follows. The best E-value involving a negative control
provided a first approximation of significance, 8e-5, 5h1a vs. patc . The level of significance was
made more stringent for inexplicable alignments involving noncorresponding TMSs
among putative GPCRs. For the BLAST result, this alignment was 5h1a vs. friz and resulted in the value of
1e-5. Therefore, we considered BLAST alignments with E-values < 1e-5 as
indicating probable homology (colored green) and those with E-values = 8e-5 as
possible homology (colored yellow). (Note that in the NCBI nonredundant
database, these terms would be defined differently due to the differing sizes of
the datasets.) To view the actual alignment, click on the highlighted cells in
Table 3 . Overall, the rather sparse
distribution of significant alignments among GPCR families provides a striking
picture of how diverse these sequences are, despite similar inferred
architecture and function. These BLAST results do suggest a relationship not
previously recognized, that the fungal pheromone receptor ste2 may be related to
the mammalian mgr-like pheromone receptors (mgr1 vs. ste2 ).
In evaluating sequence alignments between potentially related GPCR proteins
(highlighted in green if E-value < 1e-5), we also considered the location of
the alignment in the 7-TMS structure. Thus, the alignments have been annotated
to show the estimated location of the TMSs (click on the cells in Table 3 ). Alignments between corresponding
TMSs (eg, TMS1 versus TMS1) provide additional support for a finding of
homology, whereas alignments of noncorresponding TMSs would weaken the
argument.
The clusters and subclusters then served to establish PPSMs for each cluster.
Thus, the PPSM was strictly confined to a set of clearly related proteins,
without possibility of introducing bias, because of the acquisition of
questionable sequences into the substitution matrix. A single iteration of
PSI-BLAST was performed using each PPSM to query a database of sequences, and
the best scores between each cluster are listed in Table 4 . The results differ in some details
with those obtained by gapped BLAST (Table
3 ), but they agree with each other overall.
For the PSI-BLAST result, the best E-value involving a negative control was
0.001, vipr vs. pet1.0 . The level of
significance was made more stringent for inexplicable alignments involving
noncorresponding TMSs among putative GPCRs. For the PSI-BLAST result, this
alignment was l vipr vs. sre1.0 and
resulted in the value of 2e-5. For PSI-BLAST, we considered alignments with
E-values < 2e-5 as probable homology (colored green) and those with E-values
= 0.001 as possible homology (colored yellow). To view the actual alignment,
click on the highlighted cells in Table
4 .
Scores between E-values 10-3 and 10-4 obtained with gapped BLAST for several
alignments between negative controls and GPCRs deteriorated to above 10-3 upon
using PSI-BLAST (eg, er21 vs. 5ht, from 2e-4 to 0.022). This is consistent with
the notion that these sequences are probably unrelated and that the use of the
restricted PPSM is more discriminate than gapped BLAST in the same setting. The
best score for an alignment between the negative controls and GPCRs (vipr vs.
pet1, 0.001, glucagon receptor vs. oligopeptide transporter) can be analyzed
further by comparing the reverse score for pet1 vs. vipr, which gave an E-value
of only 0.94 (Table 4 ). This discrepancy
arises because the PPSMs are based on different clusters. If these clusters were
related, the E-values would not be expected to differ substantially, as the
PPSMs would tend to converge. This result underscores the relevance of choosing
an appropriate substitution matrix.
Table 4 contains numerous significant
alignments that suggest common ancestry between certain clusters. This is
further buttressed by the fact that all optimal alignments between clusters are
among corresponding positions in the 7-TMS structure. For further evaluation of
these alignments, we have provided hyperlinks in Table 2 to the conserved residues within the
various clusters and subclusters, annotated as to their location in the 7-TMS
structure. Using our database, one can thus address the question of whether
alignments between sequences in different clusters occur in the most highly
conserved regions.
Our results confirm those of Josefsson17 and extend the
results of that study by providing additional links between gene families
thought to be unrelated. Specifically, the VIP/secretin receptor family - and
latrophilin receptors - yielded significant alignments with the cAMP receptors
(car1 vs. vipr , car1 vs. latr ) and the main GPCR rhodopsin
family A (vipr vs. 5h1a , latr vs. 5h1a ) (E-values 10-5 to 10-10). The
frizzled proteins showed similarity to VIP (friz vs. vipr ), cAMP (car1 vs. friz ), and latrophilin receptors (friz vs. latr ). Similarly the ste3 fungal
pheromone receptors scored well against VIP (ste3 vs. vipr ), cAMP (car1 vs. ste3 ), and 5-ht receptors (5h1a vs. ste3 , ste3 vs. 5ht ).
The metabotropic glutamate receptors, including GABA-B and raig receptors,
and boss yielded good alignments with each other. The mgr1 group members possess
large N-termini, apparently related to the periplasmic binding proteins of
gram-negative bacteria60 ). Even though the N-termini showed
significant sequence similarities, further similarities extended into the 7-TMS
structure. This suggests that these receptors are related not just because they
share a common N-terminus, but also because of a homologous 7-TMS structure. In
comparison to other GPCR families, 2 alignments between the mgr1 cluster and
clusters in the rhodopsin-like superfamily reached values indicative of probable
homology. The best E-values were attained in comparing mgr1 vs. odr (8e-7) and mgr1 vs. 5h1a (2e-5). The second alignment
involves a pheromone receptor from the mgr1 cluster with 8 assigned TMSs. It
appears likely that the first assigned TMS may be either a signal peptide or
part of the large extracellular N-terminus typical of this receptor family;
therefore, the alignments shown are between TMSs 1-7 and 2-8, respectively,
considered to be corresponding TMSs. These alignments provide evidence of
probable homology among the 2 large GPCR superfamilies, rhodopsin-like and
mgr-like.
Although ste2 and mgr1 scored well in the BLAST analysis, they scored poorly
in the PSI-BLAST analysis. Similarly, the ste2 pheromone receptors scored poorly
against the rhodopsin-like receptors. The putative GPCR p40 also failed to show
any sequence similarity to known GPCRs. A separate PSI-BLAST analysis of p40 on
the NCBI nonredundant database revealed significant similarities to a cluster of
sequences containing, for example, the nisin biosynthesis protein nisC. This
protein is thought to be involved in the synthesis of the lantibiotic nisin61 . P40 was classified as a putative GPCR on the basis of its
hydropathy profile and conserved cysteine residues40 . As
the nisC protein is not thought to be a GPCR, but rather is involved in the
biosynthesis or translocation of nisin, this casts doubt on the notion that p40
is a GPCR. The putative GPCR pm1 also failed to show any sequence similarity to
GPCRs (data not shown). A separate PSI-BLAST analysis of pm1 on the NCBI
nonredundant database did not reveal significant similarities to any other
sequences (data not shown).
Applying the principle of transitive closure to the PSI-BLAST results in Table 4 indicative of probable homology, we
conclude that our analyses support common ancestry of the following clusters:
Group 1: 5h1a, 5ht, car1, friz, latr, oa1, odr, odr10, olf1, pe22, sra1, srb6,
srd1, sre1, srg1, sro1, ste3, vipr, vn1, and boss, gabab, mgr, mgr1, raig; Group
2: ste2; Group 3: gpr1; Group 4: bacr, yro2; Group 5: p40; Group 6: pm1.
Using IMPALA, we generated a database of PPSMs. Each sequence was compared to
the PPSM database, and the best scores between each cluster are listed in Table 5 . Although IMPALA uses an improved
alignment strategy relative to the currently available versions of PSI-BLAST20 , the results generally agree with those obtained by PSI-BLAST
(Table 4 ). For the IMPALA results, the
best E-value involving a negative control was 0.016, mgr1 vs. patc.1 . The level of significance
was made more stringent for inexplicable alignments involving noncorresponding
TMSs among putative GPCRs. For the IMPALA results, this alignment was odr10 vs. 5ht.2 and resulted in the value of
0.006. Therefore, we considered IMPALA alignments with E-values < 0.006 as
probable homology (colored green) and those with E-values = 0.016 as possible
homology (colored yellow). These IMPALA results do suggest a relationship not
previously recognized, that the yeast glucose receptor gpr1 may be related to
the odorant receptor of C elegans (gpr1
vs. odr10 ).
For the Pfam/HMMER result, the best E-value involving a negative control was
0.044, psn1 vs. 7tm_3 , further lowering
the threshold value for alignments among apparently unrelated sequences. The
level of significance was made more stringent for inexplicable alignments
involving noncorresponding TMSs. For the Pfam/HMMER result, this alignment was
5h1a vs. Bac_rhodopsin and resulted in the
value of 0.031. For Pfam/HMMER, we considered alignments with E-values <
0.031 as probable homology (colored green) and those with E-values = 0.044 as
possible homology (colored yellow). To view the actual alignment, click on the
highlighted cells in Table 6 . A separate
analysis using fragment Pfam models, allowing the query sequence to match only a
part of the alignment model, revealed fewer findings of probable homology than
those using the standard models (data not shown). The fact that most of the
sequences under our analysis are full-length sequences may account for this. The
7tm_1 model corresponds with the clusters
5h1a, 5ht, olf1, and pe22. The 7tm_2 model
corresponds with the latr and vipr clusters. The 7tm_3 model corresponds with the mgr1 and
gabab cluster. The 7tm_4 model corresponds
with the odr10 cluster. The 7tm_5 model
corresponds with the odr cluster. The STE3
receptors remain unlinked with the rhodopsin-like GPCRs. Likewise, the STE2 receptor is not linked to other GPCRs.
Finally, the Sra serpentine receptors of
C elegans also appear as an isolated family.
Generally, we were able to demonstrate more distant relationships using our
PPSMs with PSI-BLAST or IMPALA than using the standard Pfam models with HMMER. A
possible explanation for this is that a single Pfam model may correspond to
several clusters/subclusters that are represented by a set of PPSMs. In this
way, our PPSMs may be able to capture more of the variation associated with a
clade. A source of variability in the Pfam/HMMER analysis stems from the fact
that the standard Pfam 5.4 release consists of both hmmls (global) and hmmfs
(local) models. The hmmls models are the default for hmmbuild and specify a
global alignment with respect to the model, but a (multiply) local alignment
with respect to the sequence. The hmmfs models are built using the -f flag and
specify a multi-hit local alignment. The PSI-BLAST and IMPALA PPSMs specify
single-hit local alignments.
We evaluated the performance differences between the various methods. We
considered all possible E-value cutoffs and plot error versus coverage for
BLAST, IMPALA, and HMMER (Figure 1 ). Two
distinct HMMER analyses were performed, those using models built with the
default parameters, specifying a global alignment with respect to the model
(hmmls) and those built with the -f flag specifying a local alignment with
respect to the model (hmmfs). In some cases, our PPSMs outperformed the HMMER
Pfam models (Figure 1A ), while in other
cases, the HMMER Pfam models outperformed our PPSMs (Figure 1B ). Generally, BLAST was the most
sensitive method over the range of E-values. The hmmfs (local) Pfam models
performed worse than the hmmls (global) models, probably because allowing a
sequence to match part of a model increases the background noise. Our results
stand in contrast to those of Park et al62 , who found that
profiles clearly outperform pairwise methods. Our improved BLAST results may be
because we use only the top-hit to classify a sequence.
BLAST (Table 3 ), PSI-BLAST (Table 4 ), and IMPALA (Table 5 ) results were used to generate a
cluster dendrogram on the basis of E-values. Shown in Figure 2 , the GPCRs analyzed in this study
appear to cluster into 4 large branches. The first branch consists of the main
family of GPCRs (rhodopsin, biogenic amine, peptide receptor, etc.). A second
branch consists of vipr-like GPCRs. The third branch consists of mgr-like
receptors. The fourth branch appears to be a radiation confined to nematode. The
ste2, ste3, and gpr1 clusters from yeast may form the roots of the dendrogram,
connecting all the branches, but a finding of direct homology among the ste2,
ste3, and gpr1 clades remains to be verified.
Conclusion
In understanding the origins of GPCRs, we may examine their occurrence in
yeast, the simplest organism in which they are known to occur. The currently
known yeast GPCRs consist of 2 pheromone receptors (ste2 and ste3) and a glucose
receptor (gpr1) that show no significant similarities with one another. The ste3
receptor shows significant sequence similarity to the main family of GPCRs
(rhodopsin, biogenic amine, peptide receptor, etc.). According to our BLAST
analysis, the ste2 receptor shows low sequence similarity to the mgr-like
receptors in higher organisms. A separate PSI-BLAST analysis of ste2
(STE2_SACKL) on the NCBI nonredundant database also finds links to many mgr-like
mammalian pheromone receptors, thus reinforcing the theory that ste2 is related
to the mgr-like GPCRs (data not shown). Finally, the gpr1 receptor appears to
share some sequence similarity with the odorant receptors of nematode, according
to our PSI-BLAST and IMPALA analyses.
As yeast expresses 2 disparate pheromone receptors, the mammalian vomeronasal
organ similarly expresses 2 disparate pheromone GPCR families, apical
(mgr1-like) and basal (vn1) receptors. Both ste3 and vn1 are rhodopsin-like,
whereas the ste2 family and apical vomeronasal receptors appear mgr-like. Thus
it appears possible that GPCRs are composed of 2 superfamilies, present in
eukaryotes, arising from divergent yeast pheromone receptors. Perhaps as the
number of available ste2 and gpr1 sequences grows (currently only 4 and 2
sequences, respectively), it will be possible to demonstrate homology between
the yeast receptors, ste2, ste3, and gpr1 with a greater degree of
confidence.
Our analyses support the hypothesis of a common origin for the many disparate
families of GPCRs. The GPCRs analyzed in this study fall into 2 broad
superfamilies: 1) rhodopsin-like: 5h1a, 5ht, car1, friz, latr, oa1, odr, odr10,
olf1, pe22, sra1, srb6, srd1, sre1, srg1, sro1, ste3, vipr, and vn1; and 2)
mgr-like: boss, gabab, mgr, mgr1, raig, and ste2. Our analyses provide evidence
for a finding of probable or possible homology between these 2 broad
superfamilies. The yeast glucose receptor, gpr1, appears distinct from these 2
superfamilies, although it does show low but significant similarity to 1 cluster
of the rhodopsin-like superfamily. Its exact relationship to other GPCRs is
unclear. It is possible that it represents a primordial GPCR or that it evolved
from either ste2 or ste3 or that it represents an entirely separate branch of
GPCRs. The common ancestry of bacr and yro2 has already been addressed18 . Our
analyses do not support the hypothesis that bacteriorhodopsins and GPCRs share a
common ancestor. Lastly, the putative GPCRs p40 and pm1 show no similarity to
any of the sequences analyzed and, in fact, may not represent true GPCRs at all.
The prediction that p40 is not a GPCR is supported by a recent report that p40
serves as a peripheral membrane protein related to the lantibiotic synthetase
component C, rather than a GPCR63 .
These results confirm and extend those of Josefsson17 . Our studies support
Josefsson's finding of a large superfamily (rhodopsin-like) consisting of the
following families: family A: rhodopsin, olfactory, biogenic amine, peptide
receptors; family B: VIP, calcitonin, glucagon, secretin receptors; family E:
fungal pheromone A- and M-factor (STE3/MAP3) receptors; family F: cAMP
receptors; Arabidopsis thaliana receptors; frizzled and smoothened
receptors; basal vomeronasal receptors; and ocular albinism receptors. Our
studies also support Josefsson's finding of a smaller second superfamily
(mgr-like) consisting of the following families: family C: metabotropic
glutamate, Ca2+-sensing, GABA, apical vomeronasal receptors; and BOSS. In
contrast to the results of Josefsson, our studies suggest that STE2 (family D)
is not a distinct third superfamily, but is rather a distant member of the
superfamily of MGR-like GPCRs. Our studies also examined known or putative GPCR
families in addition to those in the Josefsson study. The retinoic acid induced
gene (raig) belongs to the MGR-like superfamily.The fungal glucose receptor,
gpr1, a known GPCR, represents a distinct superfamily of GPCRs. Our results
predict that the putative GPCRs p40 and pm1 are not actual GPCRs. Moreover, our
study finds significant sequence similarity between the rhodopsin-like,
MGR-like, and gpr1 GPCRs, thus supporting the hypothesis that all GPCRs arose
from a common ancestor.
The BLAST and PSI-BLAST/IMPALA methods complement each other, as we were able
to demonstrate relationships with one where we could not with the other.
Overall, BLAST demonstrated better performance over a range of E-values than did
the profile-based methods. This result is in contrast to that of Park et al62 , who found that profile methods performed better. This difference may be
accounted for by the fact that Park et al considered all BLAST hits, while we
considered only the top BLAST hit for any query. We found that BLAST performance
decreased substantially by considering other than the top-hit (data not shown).
While it seems counterintuitive that BLAST outperforms profile-based methods, an
explanation may be as follows. Profile-based methods (as well as the BLAST
all-hits method) tend to capture the average, while the BLAST top-hit method is
able to capture the outlier. Thus, divergence and chance work to the advantage
of top-hit methods. These results demonstrate the necessity of using both
profile- and pairwise-based methods to avoid false conclusions about
evolutionary relationships.
In this study, we constructed a database of PPSMs. The performance of our
PPSMs was comparable with the corresponding Pfam models when analyzing distant
relationships. On the other hand, our library of PSI-BLAST profiles can also be
used in database annotation, for example, to assign an orphan receptor by
highest sequence similarity to one of the many families of GPCRs. This is a
particularly important issue, as the completion of the sequencing of the human
genome will generate an abundance of orphan receptors without known functions or
ligands. Within a narrowly defined family of GPCRs, we expect to find common
sequence motifs that are reflected in our scoring matrices. Orphan receptors
with similar functions would be expected to share these motifs, which would be
weighted more heavily in assigning an orphan receptor to a subcluster.
Therefore, sequence alignments of orphan GPCRs with the use of our clustered
database and cluster-specific PPSMs could help identify the relevant subfamilies
with characteristic conserved residues and point the way to potential ligands
and physiological functions. The approach taken in this study is particularly
useful in analyzing large gene families and distant evolutionary
relationships.
Acknowledgements
We acknowledge Incyte Genomics, Palo Alto, CA, for the use of their
computers. This study was supported in part by grant GM43102 from the National
Institutes of Health.
Glossary
BLAST - basic local alignment search tool
Entrez - sequence server
GPCR
- G protein-coupled receptor
HMM - hidden Markov model
HMMER - HMM
software package
IMPALA - integrating matrix profiles and local alignments;
profile software package
INCA - iterative neighborhood cluster analysis;
iterated BLAST
NCBI - National Center for Biotechnology Information
PDZ -
protein interaction domain
Pfam - protein family; hand-curated HMMER
models
PPSM - PSI-BLAST-constructed position-specific score matrix
PSSM - position-specific score matrix
PSI-BLAST - position specific iterated
BLAST
SwissProt - annotated protein sequence database
TMS - transmembrane
segment
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