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Empirical ways to identify novel Bedaquiline resistance mutations in AtpE.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Rodrigues, Carlos HM  ORCID logo  https://orcid.org/0000-0002-4420-6401
Denholm, Justin 

Abstract

Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.

Description

Keywords

Algorithms, Amino Acid Sequence, Antitubercular Agents, Bacterial Proteins, Diarylquinolines, Drug Resistance, Bacterial, Humans, Machine Learning, Mutation, Missense, Mycobacterium tuberculosis, Sequence Homology, Tuberculosis

Journal Title

PLoS One

Conference Name

Journal ISSN

1932-6203
1932-6203

Volume Title

14

Publisher

Public Library of Science (PLoS)
Sponsorship
Medical Research Council (MR/M026302/1)
M.K was funded by the Melbourne Research Scholarship. D.B.A was funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1), 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). The Vietnam genomic dataset was funded by a NHMRC Australia grant (APP1056689) to SJD and KEH. Supported in part by the Victorian Government's OIS Program.