Journal article
Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.
Stephanie Portelli, Yoochan Myung, Nicholas Furnham, Sundeep Chaitanya Vedithi, Douglas EV Pires, David B Ascher
Scientific Reports | Nature Publishing Group | Published : 2020
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 corr..
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Awarded by Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG)
Awarded by Jack Brockhoff Foundation
Awarded by C. J. Martin Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia
Funding Acknowledgements
We would like to thank Dr Jody Phelan from the London School of Hygiene and Tropical Medicine for giving us access to the resistant and susceptible mutational data used for the training set. S.P. and Y.M. were funded by the Melbourne Research Scholarship. D.B.A. and D.E.V.P. were funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1). D.B.A. was supported by the Jack Brockhoff Foundation (JBF 4186, 2016), and a C. J. Martin Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1072476). This work was supported in part by the Victorian Government's OIS Program.