Structure prediction and molecular docking studies of aromatic hydrocarbon sensing proteins TbuT, HbpR and PhnR to detect priority pollutants. Issue 2 (28th January 2020)

• Record Type: Journal Article
• Authors: Patel, Rushika; Chudasama, Rajesh; Solanki, Rutujaben; Patel, Priya; Parmar, Krupali; Munshi, Nasreen S.
• Is Part Of: Journal of environmental science and health. Volume 55:Issue 2(2020)
• Page Start: 126
• Page End: 141
• Publication Date: 2020-01-28
• Subjects: Aromatic hydrocarbon -- priority pollutant -- protein structure prediction -- regulatory protein -- hydrocarbon sensing promoter-operator; Environmental engineering -- Periodicals; Environmental sciences -- Periodicals; Ecology -- periodicals; Hazardous Substances -- periodicals; 628
• DOI: 10.1080/10934529.2019.1672457
• Languages: English
• ISSNs: 1093-4529

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.