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苹果蠹蛾性信息素结合蛋白2介导的潜在活性信息化学物质预测的结构见解

Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals.

作者信息

Tian Zhen, Liu Jiyuan, Zhang Yalin

机构信息

Key Laboratory of Plant Protection Resources &Pest Management of the Ministry of Education, College of Plant Protection, Northwest A&F University, Yangling 712100, Shaanxi, China.

出版信息

Sci Rep. 2016 Mar 1;6:22336. doi: 10.1038/srep22336.

Abstract

Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses.

摘要

鉴于行为干扰应用在害虫防治中的优势以及苹果蠹蛾造成的危害,在寻找苹果蠹蛾活性信息素方面尚未取得显著进展。在本研究中,通过模拟对接对31种候选信息素与苹果蠹蛾性信息素结合蛋白2(CpomPBP2)的结合潜力进行了排序,并通过竞争性结合试验证实了该排序结果。虚拟筛选的高预测准确性导致构建了一种快速可行的信息素搜索方法。通过参考结合模式分析,氢键和疏水相互作用被认为是决定配体亲和力的两个关键因素,分子链长度也是如此。因此得出结论,具有适当链长且一端带有羟基或羰基的信息素往往更受CpomPBP2青睐。还指出了与每个配体结合的残基,并通过计算丙氨酸扫描诱变进行了验证。本研究取得的进展有助于建立一种预测潜在活性化合物的有效方法,并为通过深入了解结合模式分析将高通量虚拟筛选应用于信息素搜索做好准备。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dabe/4772377/0dd1c240ae15/srep22336-f1.jpg

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