Carter, Joshua;
Walker, Timothy M;
Walker, Ann;
Whitfield, Michael G;
Morlock, Glenn P;
Lynch, Charlotte I;
Adlard, Dylan;
... Fowler, Philip W; + view all
(2024)
Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches.
JAC-Antimicrobial Resistance
, 6
(2)
, Article dlae037. 10.1093/jacamr/dlae037.
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Abstract
Background: Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form. Methods: We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features. Results: The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates. Conclusions: This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.
| Type: | Article |
|---|---|
| Title: | Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/jacamr/dlae037 |
| Publisher version: | https://doi.org/10.1093/jacamr/dlae037 |
| Language: | English |
| Additional information: | © The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10187483 |
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