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dc.contributor.authorPortelli, Stephanie
dc.contributor.authorMyung, Yoochan
dc.contributor.authorFurnham, Nicholas
dc.contributor.authorVedithi, Sundeep Chaitanya
dc.contributor.authorPires, Douglas E. V.
dc.contributor.authorAscher, David B.
dc.date.accessioned2021-10-22T15:51:04Z
dc.date.available2021-10-22T15:51:04Z
dc.date.issued2020-10-22
dc.date.submitted2020-07-11
dc.identifier.others41598-020-74648-y
dc.identifier.other74648
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/329771
dc.descriptionFunder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265
dc.descriptionFunder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925
dc.description.abstractAbstract: Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/.
dc.languageen
dc.publisherNature Publishing Group UK
dc.subjectArticle
dc.subject/631/114/2397
dc.subject/631/114/663
dc.subjectarticle
dc.titlePrediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches
dc.typeArticle
dc.date.updated2021-10-22T15:51:03Z
prism.issueIdentifier1
prism.publicationNameScientific Reports
prism.volume10
dc.identifier.doi10.17863/CAM.77216
dcterms.dateAccepted2020-09-21
rioxxterms.versionofrecord10.1038/s41598-020-74648-y
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidPortelli, Stephanie [0000-0003-3515-4301]
dc.contributor.orcidMyung, Yoochan [0000-0002-6763-9808]
dc.contributor.orcidPires, Douglas E. V. [0000-0002-3004-2119]
dc.contributor.orcidAscher, David B. [0000-0003-2948-2413]
dc.identifier.eissn2045-2322


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