Fierro Fabrizio, Suku Eda, Alfonso-Prieto Mercedes, Giorgetti Alejandro, Cichon Sven, Carloni Paolo
Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum JülichJülich, Germany.
Department of Biotechnology, University of VeronaVerona, Italy.
Front Mol Biosci. 2017 Sep 6;4:63. doi: 10.3389/fmolb.2017.00063. eCollection 2017.
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.
人类G蛋白偶联受体(hGPCRs)构成了一个庞大且在药学上具有高度相关性的膜受体超家族。hGPCRs家族成员中约有一半是化学感应受体,参与苦味和嗅觉,以及各种其他生理过程。因此,这些受体是药物干预的有希望的靶点。到目前为止,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,我们通过基于生物信息学的预测,对所有有定点诱变数据的苦味和气味受体进行研究,探讨这两个方面。首先,我们观察到,结合先前使用的对接程序的最新同源建模,只重现了实验推断的有限部分配体/受体相互作用。这很可能是由于与可用结构模板的低序列同一性,这限制了蛋白质模型的准确性,特别是侧链方向的准确性。超越对接构象空间有限采样的方法可能会改善预测。作为证实这一点的一个例子,我们在这里回顾了我们实验室的多尺度模拟,并表明,对于目前研究的三种复合物,它们显著提高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一些说法提供了支持,即包括1.50、2.50和7.52位的几个残基参与受体激活。