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Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

机译:激动剂与化学感受器的结合:系统的生物信息学分析。

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摘要

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.
机译:人G蛋白偶联受体(hGPCR)构成了一个庞大且高度相关的膜相关受体超家族。 hGPCR的家族成员中约有一半是化学感觉受体,与苦味和嗅觉以及其他各种生理过程有关。因此,这些受体构成药物干预的有希望的靶标。迄今为止,分子建模一直是了解激动剂结合和受体激活的最重要工具。在这里,我们通过针对所有苦味和气味受体的基于生物信息学的预测来研究这两个方面,针对这些信息可以获得定点诱变数据。首先,我们观察到最先进的同源性建模与先前使用的对接程序相结合,结果只能再现实验得出的有限比例的配体/受体相互作用。这很可能是由于与可用结构模板的序列同一性低所致,这限制了蛋白质模型的准确性,尤其是侧链方向的准确性。超越对接的构象空间的有限采样的方法可以改善预测。作为证实这一点的一个例子,我们在这里回顾了实验室的多尺度模拟,结果表明,对于到目前为止研究的三个复合物,它们显着增强了计算方法的预测能力。其次,我们的生物信息学分析为先前的主张提供了支持,这些主张包括受体1.50、2.50和7.52位置上的多个残基。

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