Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors.

Area of Science:

• Toxicology
• Computational Chemistry
• Biochemistry

Background:

• The Ames mutagenicity assay is a global regulatory standard for assessing chemical mutagenicity.
• Computational modeling, particularly chemical categorization, aids Ames assay predictions but can yield false positives.
• Electrophilic compounds reacting with DNA are key to mutagenicity, but not all Michael acceptors are mutagenic.

Purpose of the Study:

• To refine computational Ames assay predictions by investigating the reactivity of α,β-unsaturated carbonyls with DNA.
• To establish a computational threshold for direct DNA binding based on activation energy.
• To reduce false positive rates in in silico mutagenicity assessments.

Main Methods:

• Utilized density functional theory (DFT) transition state modeling to explore covalent bond formation.
• Calculated activation energies for the reaction between α,β-unsaturated carbonyls and a guanine nucleobase model.
• Identified a critical activation energy threshold for DNA binding.

Main Results:

• Determined that α,β-unsaturated carbonyls with activation energies ≥ 25.7 kcal/mol do not form covalent bonds with the guanine model.
• Established a computational criterion to distinguish between potentially mutagenic and non-mutagenic compounds.
• Demonstrated a method to decrease false positive predictions in computational mutagenicity assays.

Conclusions:

• The calculated activation energy threshold (25.7 kcal/mol) effectively differentiates compounds that can directly bind to DNA.
• This DFT-based approach enhances the accuracy of computational Ames assay predictions.
• The methodology is applicable to studying other toxicological outcomes involving covalent bond formation.