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KA Wreggett and A De Lean
Agonists and antagonists interact with the pituitary D2-dopamine receptor in a complex fashion that has been accounted for by proposing that the receptor exists in two interconvertible affinity states [De Lean et al., Mol. Pharmacol. 22:290-297 (1982)]. These two states appear to be modulated by guanine nucleotides such that the state existing in the presence of excess guanine nucleotide has low affinity for agonists and high affinity for antagonists. These observations, together with several lines of evidence from other laboratories, have suggested the interaction of the receptor with a guanine nucleotide- binding protein and a model describing the reversible interaction of the receptor (R) with an additional membrane component (X) was studied [De Lean et al., J. Biol. Chem. 255:7108-7117 (1980)]. Several properties of this ternary complex model are presented and discussed in terms of the interpretation of the analysis of simulated binding data using the mass-action model. Computer modeling of experimental binding data obtained from membrane homogenates of bovine anterior pituitary glands indicated that a ternary complex model will fit only under conditions where, in the absence of any ligand, there is a tight interaction or "precoupling" of R with X, with the latter being in stoichiometrically limiting amounts; antagonists and guanine nucleotides would tend to destabilize this interaction, whereas agonists would serve to stabilize the coupled form. These results, for a receptor system that inhibits adenylate cyclase activity, are notably different from those observed for the beta-adrenergic receptor, which stimulates the enzyme and may be a reflection of differences in the molecular mechanisms of the interaction of the two receptor systems with their ligands and their effector. Some features of the model are not compatible with the experimental data and have indicated the need to consider extensions of the model, in light of recent advancements in our understanding of these regulatory components. Our results stress the importance of verifying the properties of proposed models and of cautiously testing these proposed models by their direct application to experimental data.
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