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

• Published in: Scientific reports Vol. 10; no. 1; p. 18120
• Main Authors: Portelli, Stephanie, Myung, Yoochan, Furnham, Nicholas, Vedithi, Sundeep Chaitanya, Pires, Douglas E. V., Ascher, David B.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.10.2020; Nature Publishing Group
• Subjects: 631/114/2397; 631/114/663; Antitubercular Agents - pharmacology; Bacterial Proteins - chemistry; Bacterial Proteins - genetics; Computer applications; DNA-directed RNA polymerase; Drug Resistance, Bacterial - genetics; Humanities and Social Sciences; Humans; Learning algorithms; Leprosy; Leprosy - drug therapy; Leprosy - microbiology; Machine Learning; Missense mutation; multidisciplinary; Mutation; Mutation, Missense; Mycobacterium leprae - drug effects; Mycobacterium leprae - genetics; Mycobacterium tuberculosis - drug effects; Mycobacterium tuberculosis - genetics; Nucleic acids; Rifampin; Rifampin - pharmacology; RNA polymerase; RpoB protein; Science; Science (multidisciplinary); Staphylococcal Infections - drug therapy; Staphylococcal Infections - microbiology; Staphylococcus aureus - drug effects; Staphylococcus aureus - genetics; Tuberculosis; Tuberculosis - drug therapy; Tuberculosis - microbiology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-74648-y

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/ .