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dc.contributor.authorAllen, Timothy
dc.contributor.authorGrayson, Matthew N
dc.contributor.authorGoodman, Jonathan
dc.contributor.authorGutsell, Steve
dc.contributor.authorRussell, Paul J
dc.date.accessioned2018-09-11T17:31:19Z
dc.date.available2018-09-11T17:31:19Z
dc.date.issued2018-06-25
dc.identifier.issn1549-9596
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280173
dc.description.abstractThe 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.
dc.description.sponsorshipUnilever (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.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherAmerican Chemical Society (ACS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHumans
dc.subjectSalmonella typhimurium
dc.subjectImides
dc.subjectGuanine
dc.subjectDNA
dc.subjectMutagens
dc.subjectMutagenicity Tests
dc.subjectMutagenesis
dc.subjectThermodynamics
dc.subjectModels, Molecular
dc.subjectHalogenation
dc.titleUsing Transition State Modeling To Predict Mutagenicity for Michael Acceptors.
dc.typeArticle
prism.endingPage1271
prism.issueIdentifier6
prism.publicationDate2018
prism.publicationNameJ Chem Inf Model
prism.startingPage1266
prism.volume58
dc.identifier.doi10.17863/CAM.27541
dcterms.dateAccepted2018-05-30
rioxxterms.versionofrecord10.1021/acs.jcim.8b00130
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-06-11
dc.contributor.orcidAllen, Timothy [0000-0001-7369-0901]
dc.contributor.orcidGrayson, Matthew N [0000-0003-2116-7929]
dc.contributor.orcidGoodman, Jonathan [0000-0002-8693-9136]
dc.identifier.eissn1549-960X
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-05-30


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International