Using Transition State Modeling to Predict Mutagenicity for Michael Acceptors

Timothy E.H. Allen, Matthew N. Grayson, Jonathan M. Goodman, Steve Gutsell, Paul J. Russell

Research output: Contribution to journalArticle

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Abstract

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,β-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

LanguageEnglish
Pages1266-1271
Number of pages6
JournalJournal of Chemical Information and Modeling
Volume58
Issue number6
Early online date30 May 2018
DOIs
StatusPublished - 25 Jun 2018

Fingerprint

Covalent bonds
activation
Assays
DNA
energy
system model
Activation energy
Chemical compounds
Guanine
Density functional theory
methodology
Testing

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Using Transition State Modeling to Predict Mutagenicity for Michael Acceptors. / Allen, Timothy E.H.; Grayson, Matthew N.; Goodman, Jonathan M.; Gutsell, Steve; Russell, Paul J.

In: Journal of Chemical Information and Modeling, Vol. 58, No. 6, 25.06.2018, p. 1266-1271.

Research output: Contribution to journalArticle

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