Centre de Biochimie Structurale, CNRS, INSERM, Univ Montpellier, 34090 Montpellier, France.
Bioinformatics. 2020 Jan 1;36(1):160-168. doi: 10.1093/bioinformatics/btz538.
Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).
VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted.
Supplementary data are available at Bioinformatics online.
如今，虚拟筛选（VS）在药物开发过程中起着重要作用。然而，在所有阶段都至关重要的结合亲和力的准确估计并不简单，可能需要针对特定目标的精细调整。此外，药物设计还需要改进对假定的次要靶标（包括雌激素受体 alpha（ERα））的预测。
基于结构的虚拟筛选（SBVS）和基于配体的虚拟筛选（LBVS）的组合的 VS 正在获得动力，以提高 VS 的性能。在这项研究中，我们提出了一种使用配体对接多个结构集合的综合方法来反映受体的灵活性。然后，我们使用随机森林算法研究了两种不同类型的特征（基于结构和基于配体的分子描述符）对亲和力预测的影响。我们发现，基于配体的特征的预测能力较低（rP = 0.69，R2 = 0.47），而基于结构的特征（rP = 0.78，R2 = 0.60）。它们的组合在内部测试集上保持了高准确性（rP = 0.73，R2 = 0.50），但在外部数据集上表现出更高的稳健性。进一步的改进和扩展训练数据集以包括外源性化合物，导致了一种针对 ERα 配体的新型高通量亲和力预测方法（rP = 0.85，R2 = 0.71）。该预测工具已作为@TOME 服务器的专用卫星提供给社区，用户可以在其中以 mol2 格式上传配体数据集，并获得配体对接和亲和力预测。
补充数据可在 Bioinformatics 在线获得。
