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    Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.

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    Author
    Portelli, S; Myung, Y; Furnham, N; Vedithi, SC; Pires, DEV; Ascher, DB
    Date
    2020-10-22
    Source Title
    Scientific Reports
    Publisher
    Nature Publishing Group
    University of Melbourne Author/s
    Pires, Douglas; Ascher, David; Myung, Yoochan; Portelli, Stephanie
    Affiliation
    Biochemistry and Molecular Biology

    Computing and Information Systems
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Portelli, 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.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251704
    DOI
    10.1038/s41598-020-74648-y
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581776
    NHMRC Grant code
    NHMRC/1072476
    Abstract
    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/ .

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