Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors.
J Chem Inf Model
American Chemical Society (ACS)
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Allen, T. E., Grayson, M. N., Goodman, J. M., Gutsell, S., & Russell, P. J. (2018). Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors.. J Chem Inf Model, 58 (6), 1266-1271. https://doi.org/10.1021/acs.jcim.8b00130
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.
Humans, Salmonella typhimurium, Imides, Guanine, DNA, Mutagens, Mutagenicity Tests, Mutagenesis, Thermodynamics, Models, Molecular, Halogenation
Unilever (Post-Doctoral Funding to T.E.H.A) and Girton College, Cambridge (Research Fellowship to M.N.G.) for financial support. Part of this work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council.
External DOI: https://doi.org/10.1021/acs.jcim.8b00130
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280173
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/