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Agonist antagonist interactions at the rapidly desensitizing P2X3 receptor.

Helms N, Kowalski M, Illes P, Riedel T - PLoS ONE (2013)

Bottom Line: Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data.In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R.Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.

View Article: PubMed Central - PubMed

Affiliation: Rudolf Boehm Institute for Pharmacology and Toxicology, University of Leipzig, Leipzig, Germany.

ABSTRACT
P2X3 receptors (P2XRs), as members of the purine receptor family, are deeply involved in chronic pain sensation and therefore, specific, competitive antagonists are of great interest for perspective pain management. Heretofore, Schild plot analysis has been commonly used for studying the interaction of competitive antagonists and the corresponding receptor. Unfortunately, the steady-state between antagonist and agonist, as a precondition for this kind of analysis, cannot be reached at fast desensitizing receptors like P2X3R making Schild plot analysis inappropriate. The aim of this study was to establish a new method to analyze the interaction of antagonists with their binding sites at the rapidly desensitizing human P2X3R. The patch-clamp technique was used to investigate the structurally divergent, preferential antagonists A317491, TNP-ATP and PPADS. The P2X1,3-selective α,β-methylene ATP (α,β-meATP) was used as an agonist to induce current responses at the wild-type (wt) P2X3R and several agonist binding site mutants. Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data. The P2X3R-induced currents could be fitted correctly with the help of this Markov model allowing identification of amino acids within the binding site which are important for antagonist binding. In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R. Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.

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The Markov model for competitive antagonism consists of 3 different receptor states, closed (C; yellow), open (O; purple) and desensitized (D; green), which are connected by the specific transition rates for each state.Because every state can bind up to 3 ligands, which are either agonists (red spheres) or antagonists (blue cones), there are 23 states in this model. Starting at C1, an additional agonist is bound rightwards and an additional antagonist upwards. Contrary to this, the unbinding of agonists and antagonists proceeds in opposite directions. k1, k-1, association and dissociation rates of the antagonist; a1, a-1, association and dissociation rates of the agonist; d1, d-1, transition rates of the desensitized state. Insets: structures of the antagonists used in this study (Tocris).
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pone-0079213-g001: The Markov model for competitive antagonism consists of 3 different receptor states, closed (C; yellow), open (O; purple) and desensitized (D; green), which are connected by the specific transition rates for each state.Because every state can bind up to 3 ligands, which are either agonists (red spheres) or antagonists (blue cones), there are 23 states in this model. Starting at C1, an additional agonist is bound rightwards and an additional antagonist upwards. Contrary to this, the unbinding of agonists and antagonists proceeds in opposite directions. k1, k-1, association and dissociation rates of the antagonist; a1, a-1, association and dissociation rates of the agonist; d1, d-1, transition rates of the desensitized state. Insets: structures of the antagonists used in this study (Tocris).

Mentions: P2X3Rs have 3 binding sites, and each one can be vacant, agonist-bound or antagonist-bound (Figure 1). This allows 10 possible combinations for the occupancy of the 3 binding sites; therefore, the model has 10 closed, and 10 desensitized states. In contrast, the model has only 3 open states, because at least two agonist molecules have to be bound to induce opening. Agonist and antagonist association and dissociation rates were calculated stoichiometrically, i.e. rate constants were multiplied by the number of available binding sites (see Table S1.)


Agonist antagonist interactions at the rapidly desensitizing P2X3 receptor.

Helms N, Kowalski M, Illes P, Riedel T - PLoS ONE (2013)

The Markov model for competitive antagonism consists of 3 different receptor states, closed (C; yellow), open (O; purple) and desensitized (D; green), which are connected by the specific transition rates for each state.Because every state can bind up to 3 ligands, which are either agonists (red spheres) or antagonists (blue cones), there are 23 states in this model. Starting at C1, an additional agonist is bound rightwards and an additional antagonist upwards. Contrary to this, the unbinding of agonists and antagonists proceeds in opposite directions. k1, k-1, association and dissociation rates of the antagonist; a1, a-1, association and dissociation rates of the agonist; d1, d-1, transition rates of the desensitized state. Insets: structures of the antagonists used in this study (Tocris).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815160&req=5

pone-0079213-g001: The Markov model for competitive antagonism consists of 3 different receptor states, closed (C; yellow), open (O; purple) and desensitized (D; green), which are connected by the specific transition rates for each state.Because every state can bind up to 3 ligands, which are either agonists (red spheres) or antagonists (blue cones), there are 23 states in this model. Starting at C1, an additional agonist is bound rightwards and an additional antagonist upwards. Contrary to this, the unbinding of agonists and antagonists proceeds in opposite directions. k1, k-1, association and dissociation rates of the antagonist; a1, a-1, association and dissociation rates of the agonist; d1, d-1, transition rates of the desensitized state. Insets: structures of the antagonists used in this study (Tocris).
Mentions: P2X3Rs have 3 binding sites, and each one can be vacant, agonist-bound or antagonist-bound (Figure 1). This allows 10 possible combinations for the occupancy of the 3 binding sites; therefore, the model has 10 closed, and 10 desensitized states. In contrast, the model has only 3 open states, because at least two agonist molecules have to be bound to induce opening. Agonist and antagonist association and dissociation rates were calculated stoichiometrically, i.e. rate constants were multiplied by the number of available binding sites (see Table S1.)

Bottom Line: Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data.In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R.Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.

View Article: PubMed Central - PubMed

Affiliation: Rudolf Boehm Institute for Pharmacology and Toxicology, University of Leipzig, Leipzig, Germany.

ABSTRACT
P2X3 receptors (P2XRs), as members of the purine receptor family, are deeply involved in chronic pain sensation and therefore, specific, competitive antagonists are of great interest for perspective pain management. Heretofore, Schild plot analysis has been commonly used for studying the interaction of competitive antagonists and the corresponding receptor. Unfortunately, the steady-state between antagonist and agonist, as a precondition for this kind of analysis, cannot be reached at fast desensitizing receptors like P2X3R making Schild plot analysis inappropriate. The aim of this study was to establish a new method to analyze the interaction of antagonists with their binding sites at the rapidly desensitizing human P2X3R. The patch-clamp technique was used to investigate the structurally divergent, preferential antagonists A317491, TNP-ATP and PPADS. The P2X1,3-selective α,β-methylene ATP (α,β-meATP) was used as an agonist to induce current responses at the wild-type (wt) P2X3R and several agonist binding site mutants. Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data. The P2X3R-induced currents could be fitted correctly with the help of this Markov model allowing identification of amino acids within the binding site which are important for antagonist binding. In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R. Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.

Show MeSH
Related in: MedlinePlus