Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0077054
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dc.titleDifferences in the Binding Affinities of ErbB Family: Heterogeneity in the Prediction of Resistance Mutants
dc.contributor.authorPereira, M.
dc.contributor.authorVerma, C.S.
dc.contributor.authorFuentes, G.
dc.date.accessioned2014-10-27T08:26:01Z
dc.date.available2014-10-27T08:26:01Z
dc.date.issued2013-10-23
dc.identifier.citationPereira, M., Verma, C.S., Fuentes, G. (2013-10-23). Differences in the Binding Affinities of ErbB Family: Heterogeneity in the Prediction of Resistance Mutants. PLoS ONE 8 (10) : -. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0077054
dc.identifier.issn19326203
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/100453
dc.description.abstractThe pressure exerted by drugs targeted to a protein in any therapy inevitably leads to the emergence of drug resistance. One major mechanism of resistance involves the mutation of key residues in the target protein. Drugs that competitively replace a natural substrate are often made ineffective by mutations that reduce the drug's affinity relative to that of the natural substrate. Hence atomic level understanding of the mechanisms that underlie this behavior is of utmost importance in efforts to design new drugs that can target such mutant proteins. Methods that can predict these mutations before they appear in clinic would be a major advance in the selection of the appropriate treatment strategy in patients. The present computational approach aims to model this emergence in EGFR and ErbB2 after treatment with the drug lapatinib, by investigating the structural, dynamic and energetic effects on these kinases when bound to the natural substrate ATP and to lapatinib. The study reveals binding modes and subpopulations that are presumably normally cryptic and these have been analyzed extensively here with respect to sites that are predicted to be hotspots for resisting mutations. These positions are compared in the context of currently available data from laboratory-based experiments and mechanistic details, at the atomistic level, of the origin of resistance are developed. The prediction of novel mutations, if validated by their emergence in the clinic, will make these methods as a powerful predictive tool which can be used in the design of new kinase inhibitors. © 2013 Pereira et al.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1371/journal.pone.0077054
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.description.doi10.1371/journal.pone.0077054
dc.description.sourcetitlePLoS ONE
dc.description.volume8
dc.description.issue10
dc.description.page-
dc.description.codenPOLNC
dc.identifier.isiut000326037000037
dc.published.statePublished
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