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

Accepted version
Peer-reviewed

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Type

Article

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Authors

Portelli, Stephanie  ORCID logo  https://orcid.org/0000-0003-3515-4301
Furnham, Nicholas 
Vedithi, Sundeep Chaitanya 

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/ .

Description

Keywords

Antitubercular Agents, Bacterial Proteins, Drug Resistance, Bacterial, Humans, Leprosy, Machine Learning, Mutation, Missense, Mycobacterium leprae, Mycobacterium tuberculosis, Rifampin, Staphylococcal Infections, Staphylococcus aureus, Tuberculosis

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

10

Publisher

Springer Science and Business Media LLC

Rights

All rights reserved
Sponsorship
Medical Research Council (MR/M026302/1)
by the Melbourne Research Scholarship, Fundação de Amparo à Pesquisa do Estado de Minas Gerais