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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
DFT study on the structure-toxicity relationship of dioxin compounds using PLS analysis.
1State Key Laboratory of Soil and Sustainable Agriculture, Nanjing Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, PR China.
SAR and QSAR in Environmental Research
|July 27, 2007
Summary
Quantitative structure-activity relationship (QSAR) modeling revealed key factors influencing dioxin binding to the aryl hydrocarbon receptor (AhR). Dispersion interactions were found to be more critical than electrostatic interactions for dioxin toxicity.
Area of Science:
- Computational Chemistry
- Toxicology
- Molecular Modeling
Background:
- Dioxins, including polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), are persistent organic pollutants with significant toxicological impact.
- Understanding the molecular mechanisms of dioxin binding to the aryl hydrocarbon receptor (AhR) is crucial for assessing their toxicity.
Purpose of the Study:
- To develop a quantitative structure-activity relationship (QSAR) model predicting the binding affinities of various dioxin compounds to the aryl hydrocarbon receptor (AhR).
- To identify the key molecular descriptors that govern the variance in binding affinities and, consequently, dioxin toxicity.
Main Methods:
- Density Functional Theory (DFT) calculations at the B3LYP/6-311G** level were used to optimize the structures of 25 PCDDs/PBDDs and 34 PCDFs.
- Three groups of descriptors related to chemical reactivity, charge distribution, and thermochemical properties were computed.
- Partial Least Squares (PLS) analysis and Variable Importance in Projection (VIP) were employed to refine the QSAR model by removing insignificant descriptors.
Main Results:
- An improved and predictive QSAR model was established with a cumulative cross-validation coefficient (Q(2)(cum)) of 0.827.
- Both dispersion and electrostatic interactions were identified as significant contributors to the total binding affinities of dioxins to AhR.
- Dispersion interactions were found to contribute more significantly to binding affinity than electrostatic interactions, with long-range dispersion interactions being minimal.
Conclusions:
- The developed QSAR model provides a better understanding of the factors influencing dioxin-AhR binding and toxicity.
- The findings highlight the critical role of dispersion forces in the toxicological mechanisms of dioxins.
- This study offers valuable insights for predicting the toxicity of dioxin-like compounds and for designing safer chemical alternatives.
