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In silico predictive studies of mAHR congener binding using homology modelling and molecular docking.

Abstract：

:The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic, nuclear receptor that is responsible for the early events involved in the transcription of a complex set of genes comprising the CYP450 gene family. In the present computational study, homology modelling and molecular docking were carried out with the objective of predicting the relationship between the binding efficiency and the lipophilicity of different polychlorinated biphenyl (PCB) congeners and the AHR in silico. Homology model of the murine AHR was constructed by several automated servers and assessed by PROCHECK, ERRAT, VERIFY3D and WHAT IF. The resulting model of the AHR by MODWEB was used to carry out molecular docking of 36 PCB congeners using PatchDock server. The lipophilicity of the congeners was predicted using the XLOGP3 tool. The results suggest that the lipophilicity influences binding energy scores and is positively correlated with the same. Score and Log P were correlated with r = +0.506 at p = 0.01 level. In addition, the number of chlorine (Cl) atoms and Log P were highly correlated with r = +0.900 at p = 0.01 level. The number of Cl atoms and scores also showed a moderate positive correlation of r = +0.481 at p = 0.01 level. To the best of our knowledge, this is the first study employing PatchDock in the docking of AHR to the environmentally deleterious congeners and attempting to correlate structural features of the AHR with its biochemical properties with regards to PCBs. The result of this study are consistent with those of other computational studies reported in the previous literature that suggests that a combination of docking, scoring and ranking organic pollutants could be a possible predictive tool for investigating ligand-mediated toxicity, for their subsequent validation using wet lab-based studies.

journal_name

Toxicol Ind Health

journal_title

Toxicology and industrial health

authors

Panda R,Cleave AS,Suresh PK

doi

10.1177/0748233712463774

subject

Has Abstract

pub_date

2014-09-01 00:00:00

pages

765-76

issue

eissn

0748-2337

issn

1477-0393

pii

0748233712463774

journal_volume

pub_type

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In silico predictive studies of mAHR congener binding using homology modelling and molecular docking.