Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Chemosphere. 2020 Mar;242:125135. doi: 10.1016/j.chemosphere.2019.125135. Epub 2019 Oct 22.
The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-phenylboronic acids with hTTR was determined and analyzed by competitive fluorescence displacement assay integrated with computational methods. The laboratorial results indicated that the three test groups of model compounds exhibited a distinct binding affinity to hTTR. All the tested halo-phenylboronic acids, some of the tested halo-benzoic acids and halo-benzenesulfonic acids/sulfates were shown to be inactive with hTTR. Other halo-benzoic acids and halo-benzenesulfonic acids/sulfates were moderate and/or weak hTTR binders. The binding affinity of halo-benzoic acids and halo-benzenesulfonic acids/sulfates with hTTR was similar. The low distribution ability of the model compounds from water to hTTR may be the reason why they exhibited the binding potency observed with hTTR. By introducing other highly hydrophobic compounds, we observed that the binding affinity between compounds and hTTR increased with increasing molecular hydrophobicity. Those results indicated that the highly hydrophobic halo-benzoic acids and halo-benzenesulfonic acids/sulfates may be high-priority hTTR disruptors. Finally, a binary classification model was constructed employing three predictive variables. The sensitivity (S), specificity (S), predictive accuracy (Q) values of the training set and validation set were >0.83, indicating that the model had good classification performance. Thus, the binary classification model developed here could be used to distinguish whether a given ionizable compound is a potential hTTR binder or not.
先前已经观察到卤代酚类物质与人甲状腺素运载蛋白（hTTR）之间的阴离子形式依赖性结合相互作用。这表明可电离化合物应成为筛选潜在 hTTR 破坏剂的主要关注点。在这里，通过竞争荧光置换测定法结合计算方法，测定并分析了卤代苯甲酸、卤代苯磺酸/硫酸盐和卤代苯硼酸与 hTTR 的潜在结合效力。实验室结果表明，三组测试模型化合物均表现出与 hTTR 明显的结合亲和力。所有测试的卤代苯硼酸、部分测试的卤代苯甲酸和卤代苯磺酸/硫酸盐均对 hTTR 无活性。其他卤代苯甲酸和卤代苯磺酸/硫酸盐是 hTTR 的中等和/或弱结合剂。卤代苯甲酸和卤代苯磺酸/硫酸盐与 hTTR 的结合亲和力相似。模型化合物从水中分配到 hTTR 的能力较低，可能是它们表现出与 hTTR 观察到的结合效力的原因。通过引入其他疏水性较高的化合物，我们观察到化合物与 hTTR 之间的结合亲和力随分子疏水性的增加而增加。这些结果表明，高疏水性的卤代苯甲酸和卤代苯磺酸/硫酸盐可能是高优先级的 hTTR 破坏剂。最后，构建了一个采用三个预测变量的二元分类模型。训练集和验证集的灵敏度（S）、特异性（S）和预测准确率（Q）值均>0.83，表明该模型具有良好的分类性能。因此，这里开发的二元分类模型可用于区分给定的可电离化合物是否是潜在的 hTTR 结合剂。
