Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States.
Division of Nonclinical Science, Office of Science, Center for Tobacco Products, U.S. Food and Drug Administration, 11785 Beltsville Drive, Calverton, Maryland 20705, United States.
J Chem Inf Model. 2020 Apr 27;60(4):2396-2404. doi: 10.1021/acs.jcim.0c00139. Epub 2020 Mar 24.
Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.
尽管众所周知吸烟会对健康造成不良影响，但烟草制品中所含的尼古丁会导致使用者难以戒烟。烟碱型乙酰胆碱受体（nAChRs）是尼古丁的生理靶点，促进了对烟草制品的成瘾。nAChR-α7 亚型在成瘾中起着重要作用；因此，预测烟草成分与 nAChR-α7 的结合活性是评估烟草成分成瘾潜力的重要组成部分。我们基于从 PubChem 和 ChEMBL 中提取的 843 种具有人类α7 结合活性数据的化学物质的大型训练数据集，开发了一种α7 结合活性预测模型。该模型使用来自相同数据库的 1215 种具有大鼠α7 结合活性数据的化学物质进行了测试。根据竞争对接结果，对接分数被分配到对受体-配体结合起重要作用的关键残基上。决策树森林用于基于对接分数的划分来训练人类α7 结合活性预测模型。进行了五重交叉验证以估计决策树模型的性能。该开发的模型用于预测 5275 种烟草成分的潜在人类α7 结合活性。对 5275 种烟草成分中的 84 种进行了实验测量以确认和经验验证预测结果。预测准确性、敏感性和特异性分别为 64.3%、40.0%和 81.6%。开发的人类α7 预测模型可能是高通量筛选潜在成瘾性烟草成分的有用工具。
