Franco Rafael, Casadó Vicent, Mallol Josefa, Ferrada Carla, Ferré Sergi, Fuxe Kjell, Cortés Antoni, Ciruela Francisco, Lluis Carmen, Canela Enric I
Dept. Bioquimica i Biologia Molecular, Universitat de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain.
Mol Pharmacol. 2006 Jun;69(6):1905-12. doi: 10.1124/mol.105.020685. Epub 2006 Feb 24.
Nonlinear Scatchard plots are often found for agonist binding to G-protein-coupled receptors. Because there is clear evidence of receptor dimerization, these nonlinear Scatchard plots can reflect cooperativity on agonist binding to the two binding sites in the dimer. According to this, the "two-state dimer receptor model" has been recently derived. In this article, the performance of the model has been analyzed in fitting data of agonist binding to A(1) adenosine receptors, which are an example of receptor displaying concave downward Scatchard plots. Analysis of agonist/antagonist competition data for dopamine D(1) receptors using the two-state dimer receptor model has also been performed. Although fitting to the two-state dimer receptor model was similar to the fitting to the "two-independent-site receptor model", the former is simpler, and a discrimination test selects the two-state dimer receptor model as the best. This model was also very robust in fitting data of estrogen binding to the estrogen receptor, for which Scatchard plots are concave upward. On the one hand, the model would predict the already demonstrated existence of estrogen receptor dimers. On the other hand, the model would predict that concave upward Scatchard plots reflect positive cooperativity, which can be neither predicted nor explained by assuming the existence of two different affinity states. In summary, the two-state dimer receptor model is good for fitting data of binding to dimeric receptors displaying either linear, concave upward, or concave downward Scatchard plots.
激动剂与G蛋白偶联受体结合时，常出现非线性Scatchard图。由于有明确的受体二聚化证据，这些非线性Scatchard图可反映激动剂与二聚体中两个结合位点结合时的协同性。据此，最近推导得出了“双态二聚体受体模型”。在本文中，对该模型在拟合激动剂与A(1)腺苷受体结合数据方面的性能进行了分析，A(1)腺苷受体是显示向下凹形Scatchard图的受体实例。还使用双态二聚体受体模型对多巴胺D(1)受体的激动剂/拮抗剂竞争数据进行了分析。虽然对双态二聚体受体模型的拟合与对“两个独立位点受体模型”的拟合相似，但前者更简单，且判别测试选择双态二聚体受体模型为最佳模型。该模型在拟合雌激素与雌激素受体结合数据方面也非常稳健，其Scatchard图是向上凹的。一方面，该模型可预测已证实存在的雌激素受体二聚体。另一方面，该模型可预测向上凹的Scatchard图反映正协同性，而假设存在两种不同亲和力状态既无法预测也无法解释这一点。总之，双态二聚体受体模型适用于拟合与显示线性、向上凹或向下凹Scatchard图的二聚体受体结合的数据。
