### 乙酰羟酸合成酶突变体对除草剂阔草清抗性的预测研究

1. 南开大学元素有机化学国家重点实验室 化学生物学系 天津 300071
• 投稿日期:2021-11-20 发布日期:2021-12-23
• 通讯作者: 牛聪伟, 席真
• 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

Baifan Wang, Yinwu He, Xin Wen, Congwei Niu(), Zhen Xi()

1. State Key Laboratory of Elemento-Organic Chemistry, Department of Chemical Biology, Nankai University, Tianjin 300071, China
• Received:2021-11-20 Published:2021-12-23
• Contact: Congwei Niu, Zhen Xi
• Supported by:
National Natural Science Foundation of China(21837001); National Natural Science Foundation of China(21740002)

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.