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dc.contributor.authorPortelli, S
dc.contributor.authorMyung, Y
dc.contributor.authorFurnham, N
dc.contributor.authorVedithi, SC
dc.contributor.authorPires, DEV
dc.contributor.authorAscher, DB
dc.date.accessioned2020-11-17T04:06:48Z
dc.date.available2020-11-17T04:06:48Z
dc.date.issued2020-10-22
dc.identifierpii: 10.1038/s41598-020-74648-y
dc.identifier.citationPortelli, S., Myung, Y., Furnham, N., Vedithi, S. C., Pires, D. E. V. & Ascher, D. B. (2020). Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.. Scientific Reports, 10 (1), https://doi.org/10.1038/s41598-020-74648-y.
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/11343/251704
dc.description.abstractRifampicin 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.languageeng
dc.publisherNature Publishing Group
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titlePrediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
dc.typeJournal Article
dc.identifier.doi10.1038/s41598-020-74648-y
melbourne.affiliation.departmentBiochemistry and Molecular Biology
melbourne.affiliation.department
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleScientific Reports
melbourne.source.volume10
melbourne.source.issue1
melbourne.source.pages18120-
melbourne.identifier.nhmrc1072476
dc.rights.licenseCC BY
melbourne.elementsid1468996
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581776
melbourne.contributor.authorPires, Douglas
melbourne.contributor.authorAscher, David
melbourne.contributor.authorMyung, Yoochan
melbourne.contributor.authorPortelli, Stephanie
dc.identifier.eissn2045-2322
melbourne.identifier.fundernameidNHMRC, 1072476
melbourne.accessrightsOpen Access


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