The μ-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and the primary target of opioid drugs. Understanding the relationships among the ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal-mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationship (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ESs) computed from ligand–receptor complexes. We train and validate the methodology using a data set of 179 MOR ligands with experimentally measured efficacies split into strictly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contact-only LASSO model based on ligand–protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU s per ligand–receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the μ-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics.

中文翻译：

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通过复杂结构的定量动力学分析了解和预测 μ-阿片受体中的配体功效


μ-阿片受体 （MOR） 是一种参与伤害感受的 G 蛋白偶联受体，是阿片类药物的主要靶点。了解配体结构、受体动力学和激活 MOR 的功效之间的关系对于药物发现和开发至关重要。在这里，我们使用粗粒度正态分析来预测配体诱导的受体动力学变化，使用定量动力学活动关系 （QDAR） DynaSig-ML 方法，在从配体-受体复合物计算的熵特征 （ES） 上训练 LASSO 回归模型。我们使用包含 179 个 MOR 配体的数据集来训练和验证该方法，实验测量的功效被分成化学上严格不同的交叉验证集。通过分析 ES LASSO 模型的系数，我们确定了参与 MOR 激活的关键残基，其中一些残基有突变数据支持它们在 MOR 激活中的作用。此外，我们还探索了一种基于配体-蛋白质相互作用的仅接触 LASSO 模型。虽然该模型显示出预测能力，但它未能预测与训练集结构相似性较低的配体的疗效，强调了受体动力学对于预测配体诱导的受体激活的重要性。此外，我们方法的计算成本低，每个配体-受体复合物 3 个 CPU，为其在大规模虚拟筛选环境中的应用打开了大门。我们的工作有助于更好地了解 μ-阿片受体中的动力学-功能关系，并为根据配体诱导的受体动力学变化预测配体功效提供了一个框架。