Structure prediction and molecular docking studies of aromatic hydrocarbon sensing proteins TbuT, HbpR and PhnR to detect priority pollutants

Rushika V. Patel, Rajesh Chudasama, Rutujaben Solanki, P. Patel, K. Parmar, N. Munshi
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引用次数: 7

Abstract

Abstract On-line detection of aromatic hydrocarbon pollutants in aqueous environments can be achieved by biosensing strains having fusion of gene responsible for pollutant sensing protein with a reporter gene. Regulatory proteins TbuT, HbpR and PhnR are such proteins for recognizing one-, two-and three-ring aromatic hydrocarbon pollutants respectively, for which the structure is not known till date. Aim of the present study was to predict the structure of proteins and to determine their in-silico interaction with array of pollutants. Structure prediction of proteins was performed using I-TASSER and Phyre2 and refined with ModRefiner and 3DRefine. Total 14 models were obtained for each protein and the best model had more than 95% coverage in Ramachandran plot region. After successful structure prediction, molecular interaction of proteins with respective aromatic hydrocarbon pollutants categorized by United States Environmental Protection Agency was studied using AutoDockVina where the binding energy was found to fall in range of −4.6 to −8.4 kcal/mol. The types of protein-pollutant interaction were analyzed by LigPlus and Discovery Studio 2017 R2 Client which were found to be similar for standard and pollutant compounds. This study enables us to predict the range of pollutants possible to be detected using these regulatory protein-based biosensors.
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芳烃传感蛋白TbuT、HbpR和PhnR的结构预测及分子对接研究
摘要通过融合污染物传感蛋白基因和报告基因的生物传感菌株,可以实现对水环境中芳香烃污染物的在线检测。调控蛋白TbuT、HbpR和PhnR分别是识别一环、二环和三环芳烃污染物的蛋白,其结构至今尚不清楚。本研究的目的是预测蛋白质的结构,并确定它们与一系列污染物的硅相互作用。使用I-TASSER和Phyre2进行蛋白质结构预测,并用ModRefiner和3DRefine进行细化。每种蛋白共获得14个模型,最佳模型在Ramachandran样区覆盖率达95%以上。在结构预测成功后,利用AutoDockVina软件研究了蛋白质与美国环境保护署分类的各芳烃污染物的分子相互作用,发现结合能在−4.6 ~−8.4 kcal/mol之间。通过LigPlus和Discovery Studio 2017 R2 Client分析蛋白质-污染物相互作用的类型,发现标准化合物和污染物化合物相似。这项研究使我们能够预测使用这些基于调节蛋白的生物传感器可能检测到的污染物范围。
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