College of New Energy and Environment, Jilin University, Changchun 130012, China.
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
Int J Environ Res Public Health. 2020 Dec 15;17(24):9398. doi: 10.3390/ijerph17249398.
Quinolone (QN) antibiotics are widely used, which lead to their accumulation in soil and toxic effects on ryegrass in pasture. In this study, we employed ryegrass as the research object and selected the total scores of 29 QN molecules docked with two resistant enzyme structures, superoxide dismutase (SOD, PDB ID: 1B06) and proline (Pro, PPEP-2, PDB ID: 6FPC), as dependent variables. The structural parameters of QNs were used as independent variables to construct a QN double-activity 3D-QSAR model for determining the biotoxicity on ryegrass by employing the variation weighting method. This model was constructed to determine modification sites and groups for designing QNs molecules. According to the 3D contour map of the model, by considering enrofloxacin (ENR) and sparfloxacin (SPA) as examples, 23 QN derivatives with low biotoxicity were designed, respectively. The functional properties and environmental friendliness of the QN derivatives were predicted through a two-way selection between biotoxicity and genotoxicity before and after modification; four environmentally friendly derivatives with low biotoxicity and high genotoxicity were screened out. Mixed toxicity index and molecular dynamics methods were used to verify the combined toxicity mechanism of QNs on ryegrass before and after modification. By simulating the combined pollution of ENR and its derivatives in different soils (farmland, garden, and woodland), the types of combined toxicity were determined as partial additive and synergistic. Binding energies were calculated using molecular dynamics. The designed QN derivatives with low biotoxicity, high genotoxicity, and environmental friendliness can highly reduce the combined toxicity on ryegrass and can be used as theoretic reserves to replace QN antibiotics.
喹诺酮(QN)类抗生素被广泛应用,导致其在土壤中积累并对牧场中的黑麦草产生毒性作用。在本研究中,我们以黑麦草为研究对象,选择与两种耐药酶结构(超氧化物歧化酶(SOD,PDB ID:1B06)和脯氨酸(Pro,PPEP-2,PDB ID:6FPC))对接的 29 种 QN 分子的总分数作为因变量。将 QN 的结构参数作为自变量,采用变权重法构建 QN 双重活性 3D-QSAR 模型,用于确定 QN 对黑麦草的生物毒性。该模型的构建旨在确定设计 QN 分子的修饰位点和基团。根据模型的 3D 等高线图,以恩诺沙星(ENR)和司帕沙星(SPA)为例,分别设计了 23 种低生物毒性的 QN 衍生物。通过修饰前后的生物毒性和遗传毒性的双向选择,预测了 QN 衍生物的功能特性和环境友好性;筛选出了 4 种低生物毒性和高遗传毒性的环境友好型衍生物。采用混合毒性指数和分子动力学方法,验证了修饰前后 QN 对黑麦草的联合毒性作用机制。通过模拟不同土壤(农田、花园和林地)中 ENR 及其衍生物的混合污染,确定了联合毒性的类型为部分加性和协同性。采用分子动力学计算了结合能。设计出的低生物毒性、高遗传毒性和环境友好型的 QN 衍生物可显著降低 QN 对黑麦草的联合毒性,可作为替代 QN 抗生素的理论储备。