{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T21:44:13Z","timestamp":1791582253646,"version":"4.3.4"},"reference-count":38,"publisher":"European Respiratory Society (ERS)","issue":"3","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013373","name":"NIHR Oxford Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013373","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018336","name":"National Institute for Health Research Health Protection Research Unit","doi-asserted-by":"publisher","award":["NIHR200915"],"award-info":[{"award-number":["NIHR200915"]}],"id":[{"id":"10.13039\/100018336","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["823712"],"award-info":[{"award-number":["823712"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["ERJ Open Res"],"accepted":{"date-parts":[[2024,12,31]]},"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Rifampicin remains a key antibiotic in the treatment of tuberculosis. Despite advances in cataloguing resistance-associated variants (RAVs), novel and rare mutations in the relevant gene,\n                      <jats:italic>rpoB<\/jats:italic>\n                      , will be encountered in clinical samples, complicating the task of using genetics to predict whether a sample is resistant or not to rifampicin. We have trained a series of machine learning models with the aim of complementing genetics-based drug susceptibility testing.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We built a Test+Train dataset comprising 219 susceptible mutations and 46 RAVs. Features derived from the structure of the RNA polymerase or the change in chemistry introduced by the mutation were considered; however, only a few, notably the distance from the rifampicin binding site, were found to be predictive on their own. Owing to the paucity of RAVs we used Monte Carlo cross-validation with 50 repeats to train four different machine learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All four models behaved similarly with sensitivities and specificities in the range 0.84\u20130.88 and 0.94\u20130.97, although we preferred the ensemble of decision tree models as they are easy to inspect and understand. We showed that measuring distances from molecular dynamics simulations did not improve performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      It is possible to predict whether a mutation in\n                      <jats:italic>rpoB<\/jats:italic>\n                      confers resistance to rifampicin using a machine learning model trained on a combination of structural, chemical and evolutionary features; however, performance is moderate and training is complicated by the lack of data.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1183\/23120541.00952-2024","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T12:00:21Z","timestamp":1737028821000},"page":"00952-2024","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Predicting rifampicin resistance in\n                    <i>Mycobacterium tuberculosis<\/i>\n                    using machine learning informed by protein structural and chemical features"],"prefix":"10.1183","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6619-6331","authenticated-orcid":false,"given":"Charlotte I.","family":"Lynch","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dylan","family":"Adlard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0912-4483","authenticated-orcid":false,"given":"Philip W.","family":"Fowler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"2025063011301696000_11.3.00952-2024.1","unstructured":"World Health Organisation . Global Tuberculosis Report 2023. Geneva, World Health Organization, 2023."},{"key":"2025063011301696000_11.3.00952-2024.2","first-page":"CD012768","article-title":"Xpert MTB\/RIF Ultra and Xpert MTB\/RIF assays for extrapulmonary tuberculosis and rifampicin resistance in adults","volume":"1","author":"Kohli","year":"2021","journal-title":"Cochrane Database Syst Rev"},{"key":"2025063011301696000_11.3.00952-2024.3","doi-asserted-by":"crossref","first-page":"e0249734","DOI":"10.1371\/journal.pone.0249734","article-title":"Performance of Xpert MTB\/RIF Ultra for diagnosis of pulmonary and extra-pulmonary tuberculosis, one year of use in a multi-centric hospital laboratory in Brussels, Belgium","volume":"16","author":"Mekkaoui","year":"2021","journal-title":"PLoS ONE"},{"key":"2025063011301696000_11.3.00952-2024.4","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00157212"},{"key":"2025063011301696000_11.3.00952-2024.5","unstructured":"World Health Organization . Catalogue of Mutations in Mycobacterium tuberculosis complex and Their Association with Drug Resistance. 1st Edn. Geneva, World Health Organization, 2021."},{"key":"2025063011301696000_11.3.00952-2024.6","unstructured":"World Health Organization . Catalogue of Mutations in Mycobacterium tuberculosis complex and Their Association with Drug Resistance. 2nd Edn. Geneva, World Health Organization, 2023."},{"key":"2025063011301696000_11.3.00952-2024.7","doi-asserted-by":"crossref","first-page":"e265","DOI":"10.1016\/S2666-5247(21)00301-3","article-title":"The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis","volume":"3","author":"Walker","year":"2022","journal-title":"Lancet Microbe"},{"key":"2025063011301696000_11.3.00952-2024.8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz067"},{"key":"2025063011301696000_11.3.00952-2024.9","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty949"},{"key":"2025063011301696000_11.3.00952-2024.10","doi-asserted-by":"crossref","first-page":"102332","DOI":"10.1016\/j.bjid.2022.102332","article-title":"Tuberculosis drug resistance profiling based on machine learning: a literature review","volume":"26","author":"Sharma","year":"2022","journal-title":"Braz J Infect Dis"},{"key":"2025063011301696000_11.3.00952-2024.11","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1093\/jamia\/ocaa233","article-title":"An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis","volume":"28","author":"Zhang","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"2025063011301696000_11.3.00952-2024.12","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1038\/s41598-022-06449-4","article-title":"Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN","volume":"12","author":"Kuang","year":"2022","journal-title":"Sci Rep"},{"key":"2025063011301696000_11.3.00952-2024.13","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1111\/jam.14413","article-title":"Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method","volume":"127","author":"Chowdhury","year":"2019","journal-title":"J Appl Microbiol"},{"key":"2025063011301696000_11.3.00952-2024.14","doi-asserted-by":"crossref","first-page":"dlae037","DOI":"10.1093\/jacamr\/dlae037","article-title":"Prediction of pyrazinamide resistance in Mycobacterium","volume":"6","author":"Carter","year":"2024","journal-title":"JAC Antimicrob Resist"},{"key":"2025063011301696000_11.3.00952-2024.15","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1038\/s41598-020-58635-x","article-title":"Structure guided prediction of pyrazinamide resistance mutations in pncA","volume":"10","author":"Karmakar","year":"2020","journal-title":"Sci Rep"},{"key":"2025063011301696000_11.3.00952-2024.16","doi-asserted-by":"crossref","first-page":"18120","DOI":"10.1038\/s41598-020-74648-y","article-title":"Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches","volume":"10","author":"Portelli","year":"2020","journal-title":"Sci Rep"},{"key":"2025063011301696000_11.3.00952-2024.17","doi-asserted-by":"publisher","DOI":"10.1016\/j.molcel.2017.03.001"},{"key":"2025063011301696000_11.3.00952-2024.18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3001721"},{"key":"2025063011301696000_11.3.00952-2024.19","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-022-02714-x"},{"key":"2025063011301696000_11.3.00952-2024.20","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.00239-2022"},{"key":"2025063011301696000_11.3.00952-2024.21","doi-asserted-by":"publisher","DOI":"10.1099\/mic.0.000733"},{"key":"2025063011301696000_11.3.00952-2024.22","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.75046"},{"key":"2025063011301696000_11.3.00952-2024.23","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(15)00062-6"},{"key":"2025063011301696000_11.3.00952-2024.24","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731363"},{"key":"2025063011301696000_11.3.00952-2024.25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(00)00122-2","article-title":"Monte Carlo cross validation","volume":"56","author":"Xu","year":"2001","journal-title":"Chemom Intell Lab Sys"},{"key":"2025063011301696000_11.3.00952-2024.26","unstructured":"Pedregosa F , Varoquaux G , Gramfort A , et al . Scikit-learn: Machine Learning in Python. J Machine Learning Res 2011: 2825\u20132830."},{"key":"2025063011301696000_11.3.00952-2024.27","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21787"},{"key":"2025063011301696000_11.3.00952-2024.28","doi-asserted-by":"crossref","unstructured":"Gowers R , Linke M , Barnoud J , et al. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. In: Proceedings of the 15th Python in Science Conference, 2016, pp. 98\u2013105.","DOI":"10.25080\/Majora-629e541a-00e"},{"key":"2025063011301696000_11.3.00952-2024.29","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkh429"},{"key":"2025063011301696000_11.3.00952-2024.30","doi-asserted-by":"crossref","first-page":"189","DOI":"10.12688\/f1000research.7931.1","article-title":"FreeSASA: an open source C library for solvent accessible surface area calculations","volume":"5","author":"Mitternacht","year":"2016","journal-title":"F1000Res"},{"key":"2025063011301696000_11.3.00952-2024.31","doi-asserted-by":"publisher","DOI":"10.1093\/protein\/14.7.459"},{"key":"2025063011301696000_11.3.00952-2024.32","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1186\/1471-2164-16-S8-S1","article-title":"Better prediction of functional effects for sequence variants","volume":"16: Suppl. 8","author":"Hecht","year":"2015","journal-title":"BMC Genomics"},{"key":"2025063011301696000_11.3.00952-2024.33","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00697"},{"key":"2025063011301696000_11.3.00952-2024.34","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.82593"},{"key":"2025063011301696000_11.3.00952-2024.35","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1002\/jcc.26979","article-title":"Predicting antibiotic resistance in complex protein targets using alchemical free energy methods","volume":"43","author":"Brankin","year":"2022","journal-title":"J Comput Chem"},{"key":"2025063011301696000_11.3.00952-2024.36","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.chembiol.2017.12.009","article-title":"Robust prediction of resistance to trimethoprim in Staphylococcus aureus","volume":"25","author":"Fowler","year":"2018","journal-title":"Cell Chem Biol"},{"key":"2025063011301696000_11.3.00952-2024.37","doi-asserted-by":"crossref","first-page":"20190141","DOI":"10.1098\/rsfs.2019.0141","article-title":"How quickly can we predict trimethoprim resistance using alchemical free energy methods?","volume":"10","author":"Fowler","year":"2020","journal-title":"Interface Focus"},{"key":"2025063011301696000_11.3.00952-2024.38","first-page":"001187","article-title":"Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis","volume":"10","author":"Brunner","year":"2024","journal-title":"Microb Genom"}],"container-title":["ERJ Open Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1183\/23120541.00952-2024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T14:30:32Z","timestamp":1751293832000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.ersnet.org\/lookup\/doi\/10.1183\/23120541.00952-2024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,16]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6,30]]},"published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1183\/23120541.00952-2024"],"URL":"https:\/\/doi.org\/10.1183\/23120541.00952-2024","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.08.15.608097","asserted-by":"object"}]},"ISSN":["2312-0541"],"issn-type":[{"value":"2312-0541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,16]]}}}