Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
AuthorPortelli, S; Myung, Y; Furnham, N; Vedithi, SC; Pires, DEV; Ascher, DB
Source TitleScientific Reports
PublisherNature Publishing Group
AffiliationBiochemistry and Molecular Biology
Computing and Information Systems
Document TypeJournal Article
CitationsPortelli, 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), pp.18120-. https://doi.org/10.1038/s41598-020-74648-y.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581776
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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