Computational studies of interactions between endocrine disrupting chemicals and androgen receptor of different vertebrate species

Bing Wu, Timothy Ford, Ji Dong Gu, Xu Xiang Zhang, Ai Min Li, Shu Pei Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Homology modeling and molecular docking were used to in silico analyze the interactions between six endocrine disrupting chemicals (EDCs) and 11 androgen receptors (ARs) of different vertebrate species. The MODELLER 9V7 program was employed to construct the homology models of AR ligand binding domains (LBDs) from birds, amphibians, bony fishes and cartilaginous fishes. The Surflex-Dock program was applied to calculate and analyze the binding affinities between the six EDCs and AR LBDs. The docking experiment showed that AR LBDs had high affinities with nonyl phenol (NP) and butyl benzyl phthalate (BBP), but low affinities with the 2,2',4,4',5,5'-hexabromodiphenyl ether (BDE153). The results of cluster analysis suggested that predicted binding affinities were species-specific, which was consistent with the phylogenetic analysis of AR LBDs. The difference of binding affinities could be mainly due to the different hydrogen bonds and the orientation of ligands in the binding pockets. Our results suggest that integrated methods of phylogenetic analysis, homology modeling and molecular docking might be a potential tool to predict the different interactions between contaminants and associated receptors in different trophic levels.

Original languageEnglish
Pages (from-to)535-541
Number of pages7
JournalChemosphere
Volume80
Issue number5
DOIs
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Androgen receptor
  • Endocrine disrupting chemicals
  • Homology modeling
  • Molecular docking

Fingerprint

Dive into the research topics of 'Computational studies of interactions between endocrine disrupting chemicals and androgen receptor of different vertebrate species'. Together they form a unique fingerprint.

Cite this