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.
Ecotoxicol Environ Saf. 2018 Aug 15;157:128-133. doi: 10.1016/j.ecoenv.2018.03.064. Epub 2018 Apr 1.
Muscle protein was one of critical accumulation protein for anthropogenic chemicals. However, few predictive models were constructed for muscle protein up to now. In addition, some ionizable chemicals classes e.g. sulfonates were not successfully modeled in previously models, indicating considerable work would be needed. The major objective of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting the muscle protein-water partition coefficient (logK) of chicken and fish. In the modeling, the n-octanol/water distribution coefficient (logD), functional groups, atom-centred fragments and chemical form adjusted descriptors were used to construct the models. The application domain of the derived models was defined by the Euclidean distance-based method and Williams plot. The modeling results indicated that the determination coefficient (R), leave-one out cross validation Q (Q) and bootstrapping coefficient (Q) of the QSAR models for chicken and fish were 0.882 and 0.929, 0.844 and 0.906, 0.779 and 0.792, respectively, implying the models had good goodness-of-fit and robustness. The coefficient determination (R) and external validation coefficient (Q) of the validation set for the two models were 0.874 and 0.937, 0.869 and 0.915, respectively, indicating the models had good predictive ability. The predictor variables selected to construct the logK models of chicken and fish included logD, the function groups, and the fraction of the ionized species (δ). Considering the molecular descriptors used here can be calculated from their molecular structures directly, the developed models could be easily used to fill the logK data gap for other chemicals within the applicability domain.
肌肉蛋白是人为化学物质的关键蓄积蛋白之一。然而，迄今为止，很少有预测模型被构建出来。此外，一些可电离的化学物质类别，如磺酸盐，在以前的模型中没有被成功建模，这表明需要做大量的工作。本研究的主要目的是建立定量构效关系（QSAR）模型，以预测鸡和鱼的肌肉蛋白-水分配系数（logK）。在建模中，使用正辛醇/水分配系数（logD）、官能团、原子中心片段和化学形式调整描述符来构建模型。所得到的模型的应用域通过基于欧几里得距离的方法和威廉姆斯图来定义。建模结果表明，鸡和鱼的 QSAR 模型的决定系数（R）、留一法交叉验证 Q（Q）和自举系数（Q）分别为 0.882 和 0.929、0.844 和 0.906、0.779 和 0.792，表明模型具有良好的拟合度和稳健性。两个模型的验证集的系数确定（R）和外部验证系数（Q）分别为 0.874 和 0.937、0.869 和 0.915，表明模型具有良好的预测能力。用于构建鸡和鱼的 logK 模型的预测变量包括 logD、官能团和电离态分数（δ）。考虑到这里使用的分子描述符可以直接从它们的分子结构中计算出来，因此可以很容易地将所开发的模型用于填补可应用域内其他化学物质的 logK 数据空白。
