Yan Yuna, Wang Weijun, Sun Zhaoxi, Zhang John Z H, Ji Changge
Shanghai Engineering Research Center for Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University , Shanghai 200062, China.
State Key Laboratory of Precision Spectroscopy, East China Normal University , Shanghai 200062, China.
J Chem Inf Model. 2017 Aug 28;57(8):1793-1806. doi: 10.1021/acs.jcim.7b00017. Epub 2017 Jul 18.
A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.
经验评分函数的一个主要缺点是它们常常无法准确预测结合亲和力。去除对接结果中的假阳性是基于结构的虚拟筛选中最具挑战性的工作之一。对接后过滤器利用各种实验结构和活性信息，可能有助于解决这个问题。我们描述了一种基于详细的蛋白质-配体相互作用分解和机器学习的新方法。蛋白质-配体经验相互作用成分（PLEIC）用作支持向量机学习的描述符，以开发一个分类模型（PLEIC-SVM）来区分假阳性和真阳性。实验得出的活性信息用于模型训练。我们对来自DUD-E数据库的36个不同数据集进行了广泛的基准研究，以评估新方法的性能。结果表明，在基于结构的虚拟筛选中，新方法的表现比标准经验评分函数要好得多。训练后的PLEIC-SVM模型能够捕捉一个特定靶点的配体与蛋白质残基之间的重要相互作用模式，这有助于在对接后过滤中丢弃假阳性。
