An integrated molecular docking and rescoring method for predicting the sensitivity spectrum of various serine hydrolases to organophosphorus pesticides. (3rd August 2015)

• Record Type: Journal Article
• Authors: Yang, Ling‐Ling; Yang, Xiao; Li, Guo‐Bo; Fan, Kai‐Ge; Yin, Peng‐Fei; Chen, Xiang‐Gui
• Is Part Of: Journal of the science of food and agriculture. Volume 96:Number 6(2016)
• Page Start: 2184
• Page End: 2192
• Publication Date: 2015-08-03
• Subjects: scoring function -- food safety -- AutoDock Vina -- organophosphorus (OP); Food -- Periodicals; Agriculture -- Periodicals; 664
• DOI: 10.1002/jsfa.7335
• Languages: English
• ISSNs: 0022-5142

Abstract

Abstract: BACKGROUND: The enzymatic chemistry method is currently the most widely used method for the rapid detection of organophosphorus (OP) pesticides, but the enzymes used, such as cholinesterases, lack sufficient sensitivity to detect low concentrations of OP pesticides present in given samples. Serine hydrolase is considered an ideal enzyme source in seeking high‐sensitivity enzymes used for OP pesticide detection. However, it is difficult to systematically evaluate sensitivities of various serine hydrolases to OP pesticides by in vitro experiments. This study aimed to establish an in silico method to predict the sensitivity spectrum of various serine hydrolases to OP pesticides. RESULTS: A serine hydrolase database containing 219 representative serine hydrolases was constructed. Based on this database, an integrated molecular docking and rescoring method was established, in which the AutoDock Vina program was used to produce the binding poses of OP pesticides to various serine hydrolases and the ID‐Score method developed recently by us was adopted as a rescoring method to predict their binding affinities. In retrospective case studies, this method showed good performance in predicting the sensitivities of known serine hydrolases to two OP pesticides: paraoxon and diisopropyl fluorophosphate. The sensitivity spectrum of the 219 collected serine hydrolases to 37 commonly used OP pesticides was finally obtained using this method. CONCLUSION: Overall, this study presented a promising in silico tool to predict the sensitivity spectrum of various serine hydrolases to OP pesticides, which will help in finding high‐sensitivity serine hydrolases for OP pesticide detection. © 2015 Society of Chemical Industry