MOLECULAR MIMICRY OF RECEPTORS FOR ALKALOID DRUGS

长期目标是了解各种生物受体是如何 系统与麻醉性生物碱药物相互作用。这项研究项目寻求 初步了解主宰地球的物理化学规律 吗啡、尼古丁及相关药物的特异性识别和结合 使用现代分子生物学，生物物理测量和 超分子计算化学。这一目标将会实现 使用理论和实验分子模型研究，在这些研究中，我们 将使用抗体-配体结合位点作为受体-配体的范例 --互动。在以下方面模仿生物受体位置的抗体 它们对配体的立体特异性识别和结合可以作为一种 研究配体和分子间相互作用成分的模型 受体。利用定量结构-活性配基结合研究， 配体诱导的荧光猝灭，mRNA/cDNA测序技术， 计算机辅助建模、动力学模拟和X射线结晶学 在衍射研究中，我们将广泛地表征 结合生物碱药物吗啡和吗啡的单抗 尼古丁。抗体结合部位已经在许多地方进行了检测 调查，但这项研究是第一次使用理论 以及表征麻醉剂结合部位的实验方法 毒品。我们必须了解受体药物的基础 相互作用，使治疗药物滥用的战略和新的 药物的开发可以是理性的和可预见的。

The long term objective is to understand how various biological receptor systems interact with narcotic alkaloid drugs. This research project seeks to gain a preliminary insight into the physiochemical rules governing specific recognition and binding of morphine, nicotine and related drugs of abuse using modern molecular biology, biophysical measurements and supramolecular computational chemistry. The objective will be accomplished using theoretical and experimental molecular modelling studies in which we will use antibody-ligand binding sites as a paradigm of receptor-ligand -interactions. Antibodies which mimic biological receptor sites in terms of their stereospecific recognition and binding of ligands can serve as a model for the study of interactive constituents of both the ligand and receptor. Using quantitative structure-activity ligand binding studies, ligand induced fluorescence quenching, mRNA/cDNA sequencing techniques, computer-aided modelling and dynamics simulations and x-ray crystallography diffraction studies, we will extensively characterize the binding sites of monoclonal antibodies (mAb) which bind the alkaloid drugs morphine and nicotine. Antibody binding sites have been examined in numerous investigations, but this study is the first of its kind to use theoretical and experimental approaches to characterize the binding sites for narcotic drugs. It is imperative that we understand the basis of receptor-drug interactions so that strategies in treating drug abuse and new pharmaceuticals may be developed in a rational and predictive manner.