Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches
Authors
Furnham, Nicholas
Vedithi, Sundeep Chaitanya
Publication Date
2020-10-22Journal Title
Scientific Reports
Publisher
Nature Publishing Group UK
Volume
10
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
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
Description
Funder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265
Funder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925
Abstract
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/.
Keywords
Article, /631/114/2397, /631/114/663, article
Identifiers
s41598-020-74648-y, 74648
External DOI: https://doi.org/10.1038/s41598-020-74648-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329771
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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