Department of Physiology and Biophysics, Mount Sinai School of Medicine, One Gustave Levy Place, New York, NY 10029, USA
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Abstract |
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Keywords: correlated mutation analysis/dimerization/G-protein coupled receptors/interface
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Introduction |
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Recent reports on heterodimerization (Jordan and Devi, 1999; Devi, 2001
) of closely and distantly related members of the GPCR family suggest potential roles for this phenomenon in modulating agonist affinity, efficacy and/or trafficking properties. Heterodimerization seems to be selective, so that GPCRs will interact with one type of receptors, but not another. Heterodimerization between closely related members of the GPCR family has been observed for GABABR1GABABR2 (Jones et al., 1998
; Kaupmann et al., 1998
; White et al., 1998
), M2M3 muscarinic (Maggio et al., 1999
; Sawyer and Ehlert, 1999
),
opioid (Jordan and Devi, 1999
), µ
opioid (George et al., 2000
; Gomes et al., 2001
), 5HT1B5HT1D serotonin (Xie et al., 1999
), SSTR1SSTR5 somatostatin (Rocheville et al., 2000b
), SSTR2ASSTR3 somatostatin (Pfeiffer et al., 2001
) and CCR2CCR5 chemokine (Mellado et al., 2001
) receptors. Recent examples of suggested heterodimerization between more distantly related members of the GPCR family are adenosine A1D1 dopamine (Gines et al., 2000
), angiotensin AT1bradykinin B2 (AbdAlla et al., 2000
), somatostatin SSTR5D2 dopamine (Rocheville et al., 2000a
), ß2-adrenergic
opioid (Jordan et al., 2001
), ß2-adrenergic
opioid (Jordan et al., 2001
) and metabotropic glutamate 1
-adenosine A1 (Ciruela et al., 2001
) receptors. Finally, examples of GPCR subtypes that have been shown not to produce heterodimers are µ opioid with
opioid receptors (Jordan and Devi, 1999
), somatostatin SSTR5 with SSTR4 (Rocheville et al., 2000b
) and chemokine CCR2 with CXCR4 (Mellado et al., 2001
) receptors.
As the effect that GPCR heterodimerization has in vivo on the modulation of receptor function is not yet known, molecular models of interacting GPCRs can be used to advance knowledge in this field. A model of receptor interaction involving swapping of TM domains has been proposed for homodimers and symmetric chimeric heterodimers (Gouldson et al., 2001), but its validity has been put into question by experimental evidence (Lee et al., 2000
; Schulz et al., 2000
). In any case, even the original authors propose that receptor heterodimers are more likely to contain only contact dimers (Gouldson et al., 2001
). These dimers derive from the association of 1:1 stoichiometric molecular complexes of receptors and may involve their extracellular, transmembrane and/or C-terminal regions. In addition, this association may be due to a combination of both covalent (disulfide) and non-covalent interactions.
To obtain a structural model of a dimerization complex involving the transmembrane domains of GPCRs, the goal would be to pack the bundles of seven transmembrane segments against one another. There are at least 49 different configurations in which the bundles can be packed next to each other. The computational approach presented here offers to reduce the number of possible configurations to a limited number of the most likely interfaces for specific GPCR heterodimerization.
It has recently been demonstrated that oligomer interfaces are significantly conserved with respect to the protein surface (Valdar and Thornton, 2001). Moreover, correlated mutations have been shown to contain information about inter-domain contacts (Oliveira et al., 1993
; Pazos et al., 1997
). The correlation has been interpreted as a result of the tendency of positions in proteins to mutate in a coordinated manner if the interface has to be preserved for structural or functional reasons. Thus, sequence changes occurring during evolution at the interface of dimerization of a given monomer A would be compensated by changes in the interacting monomer B in order to preserve the interaction interface. Based on these observations and the computational methods for identifying correlated mutations (Olmea and Valencia, 1997
), we have developed a new subtractive correlated mutation (SCM) method aimed at the identification of the most likely heterodimerization interfaces between interacting proteins that are structurally similar to each other, such as individual GPCRs in subfamilies of these receptors. The prediction of the interface is further refined by filtering the residues that are identified at the heterodimerization interface of interacting GPCRs by application of the SCM method, based on structural models of the individual GPCRs. Thus, a list of putative interface residues is pruned based on a criterion of solvent accessibility that identifies the residues on the outer (lipid-facing) surface of the transmembrane bundle. The resulting interfaces can be used in the consideration of alternatives for packing the GPCRs in dimers. The method and resulting predictions are illustrated for GPCRs in the class opioid receptors and evaluated for a protein dimer of known structure.
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Methods |
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![]() | (1) |
In order finally to identify the residues that are at the heterodimerization interface of A and B, the resulting set (I) obtained from the subtractive correlated mutation method is further pruned based on solvent accessibility values calculated for each residue of A and B from the atomic coordinates of their three-dimensional structures. Specifically, the intermolecular pairs where either one or both residues are completely or partially inaccessible to the solvent are eliminated from the list. The remaining residues of each monomer are then considered to be candidates for the interface of heterodimerization between the two proteins.
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Results |
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(a) Selection of structures for the test set. To carry out a validation test of the new SCM method, crystallographic structures of dimeric complexes were retrieved from the Protein Quaternary Structure File Server (PQS; http://pqs.ebi.ac.uk) and considered for inclusion in the analysis if they fulfilled the following criteria: (1) sequences must contain >50 amino acids; this requirement excludes peptides from the test set; (2) the two proteins in the complex must have <80% amino acid sequence identity; this will ensure elimination of homodimers from the test set; (3) the mean loss of accessible surface area per chain upon assembly formation compared to the isolated chains must be >400 Å; (4) the two monomers must have similar 3D structures (r.m.s.d. <3.0 Å); this condition is required since meaningful 3D models of GPCRs (Ballesteros et al., 2001; Visiers et al., 2002
) are currently built using the same rhodopsin crystal structure as a template; and (5) the sequences of the corresponding proteins in at least five species must be available for the sequence alignments.
(b) Prediction of interfaces. Among the initial 883 heterodimeric complexes retrieved from the PQS server on December 11, 2001, only four structures satisfied all the criteria listed above. Application of the SCM method to these four structures demonstrated the ability of the method to predict residues at the interface between structurally related proteins. As an example of the predictive ability of the SCM method, we report here the results obtained for one (PDB code: 15C8) of these four dimeric complexes.
The heterodimer corresponding to the 15C8 PDB code consists of two proteins (A and B) that share a 23% sequence identity and a 2.6 Å structural similarity. Twenty-seven corresponding species of A and B were appended to each other as described in Methods and treated as if they were one protein in order to identify the intra- and intermolecular pairs of correlated residues derived by their multiple sequence alignment. For the analysis, the dimerization interface was defined by residues in A and B that had C atoms within 8 Å distance.
Application of the SCM method identified likely intermolecular residues for both the A and B monomer. Figure 1 shows the three-dimensional structures of monomers A and B in the 15C8 complex (Figure 1a
), as well as separated entities (Figure 1b
). The interface of heterodimerization between the monomers A and B, as it appears in the crystal structure of 15C8, is represented in green in Figure 1b
. On these ribbon representations, CPK elements were used to indicate the residues predicted with the SCM method to be at the heterodimerization interface of the monomers. Specifically, the method predicted correctly 36% of the residues of A (from a total of 36) appearing at the heterodimerization interface in the crystal structure and 44% of those of B (from a total of 34). Of the total number of residues predicted to be at the heterodimerization interface of the A monomer, 35% were exact and 47% were within (i + 7) and hence considered correct. For the B monomer, the corresponding correct predictions totaled 65%. In Figure 1b
, predictions that corresponded exactly to residues at the heterodimerization interface in the 15C8 crystal structure are shown as green CPK objects and those that were close (<i + 7) are rendered in light-blue CPKs. False positives are reported in magenta CPK objects on monomer A and red CPKs on monomer B.
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(a) The µ heterodimer. The five available sequences of the
opioid receptor from human, rat, mouse, pig and zebra fish were appended to the corresponding sequences of the µ opioid receptor and arranged in a multiple sequence alignment. Application of the SCM method to the
µ opioid receptor heterodimer identified more than one heterodimerization interface. As shown in Figure 2a
, most of the residues of
opioid receptor predicted to be at the interface of the heterodimer with the µ opioid receptor are within TM4, TM5 and TM6. In contrast, the interface residues predicted for the µ opioid receptor (Figure 2b
) appear to involve mainly TM1 in the heterodimerization with
opioid receptor. Based on these results, the number of alternatives for positioning the bundles of seven TM domains of
and µ opioid receptors next to each other in a heterodimer is reduced to a small number of seemingly equally possible configurations. Specifically, the most likely heterodimerization interfaces of the
µ pair involve TM4, TM5 and TM6 of the
opioid receptor with TM1 of the µ opioid receptor. Given the common template of the GPCRs defined by the bundle of transmembrane helices and their orientation in the membrane, any geometrically feasible combination of interfaces involving these TMs is identified as a possible configuration of the heterodimer. The number of such possible configurations is small.
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Discussion |
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An association of 1:1 stoichiometric molecular complexes of receptors is required to achieve contact dimers in GPCR heterodimerization. This association may also involve extracellular, transmembrane and/or C-terminal regions, but our analysis has been limited to the seven TM regions of GPCRs because at the present stage of the research, meaningful 3D models of interacting GPCRs can only contain the TM helices that have sufficiently high homology with the corresponding regions of the only GPCR crystallographic structure known to date, rhodopsin. In fact, detailed analyses of the rhodopsin structure together with the results of both sequence analysis and molecular modeling (Ballesteros et al., 2001; Visiers et al., 2002
) support the use of the crystal structure of rhodopsin (Palczewski et al., 2000
) as a template to model only the transmembrane domain of other rhodopsin-like GPCRs. Using such models, the SCM method was shown here to predict dimerization interfaces that significantly limit the choice of possible configurations from the
49 different configurations in which two interacting bundles of seven TM domains of GPCRs can be positioned next to each other. That correlated mutation analysis of lipid-facing residues may be used in an attempt to identify dimerization interfaces has recently been demonstrated (Gouldson et al., 2001
). The new SCM method identifies the most likely heterodimerization interfaces of GPCRs amongst the different alternatives. Importantly, the method recognizes subtype specificity in GPCR heterodimerization, as demonstrated by the control case of µ
opioid receptors that had be shown experimentally not to dimerize (Jordan and Devi, 1999
) and were correctly predicted with SCM to have no residues likely to be at the heterodimerization interface.
In the present application of the method to µ opioid receptors (Figure 2
), most of the correlated residues of
opioid receptor that have been identified on the outer (lipid-facing) surface of the receptor bundle are in TM4, TM5 and TM6, whereas in the µ opioid receptor TM1 is the helix that is likely to be involved in the heterodimerization with
. The structural interpretation is feasible even with low-resolution models of the TM region of the receptors. Here it was based on models derived from the rhodopsin structure (Palczewski et al., 2000
) as a template. The results indicate that there are at least two mutually exclusive configurations of the
µ heterodimeric complex that can be formed on this basis. Specifically, TM1 of the µ opioid receptor cannot interact simultaneously with both TM6 and TM4 of the
opioid receptor. These predictions are testable experimentally and have additional implications for the study of GPCR interactions. Thus, if experimental evidence were to implicate both TM4 and TM6 in the heterodimerization of
opioid receptor with µ, then our results would indicate that oligomerization, rather than dimerization, is occurring between these opioid receptors.
Finally, it is important to emphasize that the predictive ability of the method can be influenced by many factors. First, the analysis requires multiple sequence alignments of the same GPCR cloned from different organisms. Based on the small but growing body of evidence on subtype specificity in GPCR heterodimerization, the sequence alignment has to be limited strictly to the specific receptor for which dimerization is considered. Second, only a few sequences from different organisms are known for each GPCR. As a result, the number of sequences in the multiple sequence alignments is often inadequate for a statistical analysis of the data. Third, predictions are limited to the TM regions of the GPCRs under study, owing to the low sequence identity of extracellular and intracellular loops among GPCRs. Fourth, the validation efforts for the SCM approach could achieve statistical significance only upon availability of more structures of heterodimeric complexes of structurally similar proteins that are eligible by the criteria we have defined.
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Notes |
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Acknowledgments |
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References |
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Received March 9, 2002; revised June 15, 2002; accepted August 3, 2002.