Computational Prediction of Alanine Scanning and Ligand Binding Energetics in G-Protein Coupled Receptors

Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand binding modes. However, systematic approaches for accurate calculations of the corresponding binding free energies are still lacking. Here, we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones.

定点诱变（site-directed mutagenesis）结合结合亲和力测定，被广泛用于探究配体（ligand）与G蛋白偶联受体（G protein-coupled receptors, GPCRs）的相互作用本质。此类实验以及一系列配体的构效关系（structure-activity relationships），通常借助计算衍生的配体结合模式模型进行阐释。然而，目前仍缺乏可精准计算对应结合自由能的系统性方法。本研究报道了一种基于分子动力学自由能模拟（molecular dynamics free energy simulations）的计算策略，可定量预测丙氨酸扫描诱变（alanine scanning）与配体修饰所产生的效应。本研究推导得到一种平滑分步的自由能微扰（free energy perturbation）计算方案，并将其应用于人体神经肽Y1受体的13组丙氨酸突变体以及8组类似拮抗剂的分析。该方法的稳健性与准确性，可实现对已有诱变及结合数据的明确阐释。本研究展示了如何通过此类计算验证结构模型，并证实其可区分最优与次优结构模型的能力。