Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.

中文翻译：

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通过高通量分子动力学模拟改善蛋白质-配体对接结果。

基于结构的虚拟筛选依赖于经典的评分功能，这些评分功能通常无法可靠地将活页夹与非活页夹区分开。在这项工作中，我们提出了一个高通量蛋白质-配体复杂分子动力学（MD）模拟，该模拟使用AutoDock Vina的输出来改善对接结果，从而在有用的诱饵增强（DUD-E）目录中区分诱饵配体和活性物质数据集。MD轨迹通过使用均方根偏差评估配体结合稳定性来处理。我们从初始值0.68（AutoDock）中选择了56个蛋白质靶标（共7种不同的蛋白质类别）和560个配体（280个活性成分，280个诱饵），并显示ROC AUC（曲线下面积，接收器工作特征曲线）提高了22％。 Vina）至0.83的最终值。MD模拟显示了在所有七个不同蛋白质类别中的强大性能。另外，在MD模拟过程中适当地改进了一些预测的配体结合模式。这些结果系统地验证了基于物理学的评估蛋白质-配体结合相互作用的方法的可靠性。