乙酰羟酸合成酶突变体对除草剂阔草清抗性的预测研究
收稿日期: 2021-11-20
网络出版日期: 2021-12-27
基金资助
国家自然科学基金(21837001); 国家自然科学基金(21740002)
Prediction on the Resistance of Acetohydroxyacid Synthase Mutants to Herbicide Flumetsulam
Received date: 2021-11-20
Online published: 2021-12-27
Supported by
National Natural Science Foundation of China(21837001); National Natural Science Foundation of China(21740002)
本工作测定了商品化除草剂阔草清对大肠杆菌乙酰羟酸合成酶II及其突变体的抑制常数. 利用生物大分子的定量构效关系方法(MB-QSAR)建立了定量、可视的突变体性质与结构关系模型. 实验数据与计算数据之间显示了良好的相关性: MB-QSAR/CoMFA (comparative molecular field analysis)模型的交叉验证系数q2=0.691, 非交叉验证相关性系数r2=0.947, 外部预测能力r2pred=0.759; MB-QSAR/CoMSIA (comparative molecular similarity indices analysis)模型的相应值分别为0.625, 0.960和0.619. 模型的三维结构-性质关系图为突变体抗性提供了直观阐释, 为抗性规避性农药的设计提供了有价值的信息. 同时, 该模型也在一定程度上显示了除草剂种属选择性的预测能力.
王白帆 , 何寅武 , 文欣 , 牛聪伟 , 席真 . 乙酰羟酸合成酶突变体对除草剂阔草清抗性的预测研究[J]. 化学学报, 2022 , 80(2) : 141 -149 . DOI: 10.6023/A21110526
Flumetsulam is a widely used herbicide that targets the acetohydroxyacid synthase (AHAS). Mutations in AHAS have caused serious herbicide resistance which threatened the field application of this herbicide. We have recently established a mutation-dependent biomacromolecular quantitative structure-activity relationship method, called MB-QSAR, which could be used to quantitatively predict the mutational drug resistance in molecular level and elucidating the three dimensional structure-resistance relationships for the design of resistance evading inhibitors. In this work, we employ MB-QSAR method to predict the molecular drug resistance of AHAS mutants towards flumetsulam, and to depict the structure resistance relationships in AHAS mutants. A series of AHAS mutants concerned with the herbicide resistance were constructed, and the inhibitory properties of flumetsulam against these mutants were measured. Then the structures of these mutants were constructed, optimized and aligned for the subsequent MB-QSAR modelling. The CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) molecular field values (steric, electrostatic, hydrophobic, hydrogen-bond donor and hydrogen-bond acceptor descriptors) were calculated in the flumetsulam binding pocket for these mutants. The CoMFA and CoMSIA molecular field values were used as independent variables, while the pKi values for each mutants were used as dependent variables in the partial least squares (PLS) regression analyses to derive the MB-QSAR models. The built MB-QSAR model showed excellent correlation between experimental and computational data (MB-QSAR/CoMFA model: q2=0.691, r2=0.947, r2pred=0.759; MB-QSAR/CoMSIA model: q2=0.625, r2=0.960, r2pred=0.619), indicating the good prediction for the inhibition properties of flumetsulam against AHAS mutants. The comparison of the molecular interaction diagrams from MB-QSAR models provides information about which positions in the polypeptide chain could have a higher propensity to acquire herbicide resistant mutations, which in turn provides guidelines for modifying the existing herbicide as well as for designing new resistance-evading herbicides. The obtained MB-QSAR model also showed reasonable predictive power toward AHAS from various species, indicating the potential application of our MB-QSAR method in the prediction of the selectivity of drugs towards targets from different species.
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