The Molecular Basis Of Pesticide Rational Design

Agriculture accounts for a great proportion in the national economy of our country and the pesticide is an important "weapon" to ensure its production. However, resistance is increasingly popular, which may bring the pesticide to disadvantage. It is an arduous task how to antagonize resistance and how to do anti-resistance pesticide design. Rational design of pesticide molecule is a way to solve the problem. However, rational design is a systematic work, which includes many components, for example, pesticide likeness, molecular mechanism of pesticide, pesticide resistance prediction, novel pesticide design, and so on. The key of rational design is how to select effective methods based on the above components to finally discover potentially novel lead compound. Improving the lead compound discovering rate can reduce the period of pesticide discovering and render free of the resistance mechanism. With the development of structural biology and computational chemistry, computational simulation, complementary to other experimental methods, plays more and more important role in the process of new pesticide discovery and development.In this thesis, computational simulation method was used to explain "the molecular basis of pesticide rational design". We systematically studied several important components in the process of pesticide rational design, including pesticide likeness, the molecular mechanism of representative pesticide, the methodology of pesticide resistance prediction, and fragment based pesticide design. The rule of pesticide likeness was raised, the molecular mechanisms of several important targets in pesticide were understood, some new computational methods for pesticide rational design were developed, and some high bioactivity molecules were discovered.First of all, the pesticide likeness study was based on the marketed pesticides. We systematically analyzed the distribution of physiochemical properties of 788 marketed pesticides:molecular weight (MW), CLogP, hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), rotational bond (ROB), and aromatic bond (ARB). For the first time, the index of photostability (aromatic bond (ARB)) was introduced to explain pesticide likeness. We found the key physiochemical properties to differentiate different kinds of pesticide and analyzed the variability of physiochemical properties along with the market time. Based on the above, the rule for pesticide likeness was that "molecular weight≤435 Da, ClogP≤6, H-bond acceptor≤6, H-bond donor≤2, the number of rotatable bond≤9, and the number of aromatic bond≤17".Secondly, we explored the molecular interaction mechanism by following one of the most important discoveries in the area of pesticide—the receptors of auxin and gibberellin. In the auxin receptor (TIR1 protein), the co-factor InsP6 acted as a'conformational stabilizer'; auxin and its analogs not only played a role of "molecular glue", but also induced the sidechain of Phe82 to undergo a conformational change in order to accommodate the subsequent binding of the substrate Aux/IAA. The change of the sidechain of Phe351 was also important for the recognition of Aux/IAA. Besides, in the gibberellin receptor (GED1 protein), we discovered a new channel for the entering and leaving of gibberellin, which theoretically refuted the gibberellin induced allosteric transformation mechanism. We proved that the regulation of gibberellin was not by inducing the allosteric transformation of N-terminal a-helix, but by stabilizing the hydrogen bonding interaction between GID1 and DELLA protein. This is a new mechanism of the interaction between gibberellin and its receptor. The research in this chapter provides a good foundation for the rational design of novel phytohormone and can be useful for other pesticide mechanism studies.Thirdly, as for the pesticide resistance, novel prediction method was developed. At first, multiple molecular modeling methods were used to uncover the mechanism of a peculiar mutation (Gly210 deletion in A. tuberculatus PPO) which is able to induce broad-spectrum resistance. This was due to the weakness of the hydrogen bonds between PPO herbicides and Arg128, which was caused by a subtle change of the local structure of the active site because of the loss of a key hydrogen bond between Gly210 and Ser424. Moreover, a new resistance prediction method—Computational Mutation Scanning (CMS) was developed to offset disadvantages of the current method, in which the computational mutation was realized on a wild-type complex MD trajectory to improve the prediction rate. Compared with the traditional method—Computational Alanine Scanning (CAS), the calculation accuracy of the binding free energy was improved by introducing a rapid entropy calculation. In the testing system, the prediction accuracy rate of CMS was about 80%, so it could be used as a quick and effective computational tool for resistance prediction. It is worth pointing out that we found some resistance mutations in PPO system based on the CMS results for some marketed PPO herbicides. All of these can supply the theoretical basis for the resistance prediction in other pesticide system.Finally, we developed novel fragment based pesticide design strategies based on the characteristics of two pesticide systems (i.e., fungicide targeted to cytochrome bcl complex and herbicide targeted to PPO enzyme). In the first place, pesticide fragment library was constructed by decomposing the structures of marketed pesticides according to the fragment rules. Then, in the system of cytochrome bcl complex, common pharmacophore structure of the current inhibitors was fixed and fragments were grafted on it to optimize the hydrophobic interaction with Phe128 and Phe274. Based on this strategy, we successfully discovered several inhibitors with potent bioactivity (the highest bioactivity was in double pM level). At the same time, we also successfully discovered new PPO inhibitors (the bioactivity was in double nM level) with novel binding mode and potential anti-resistance ability by using several anti-resistance strategies, such as reducing the interaction between the inhibitor and resistance position, introducing the interaction with the unchanged position of the target (the cofactor FAD in PPO), and focusing to screen fragments that have similar binding mode with the substrate. These strategies can provide theoretical references for the novel pesticide design in other systems.