RT Journal Article SR Electronic T1 Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches JF bioRxiv FD Cold Spring Harbor Laboratory SP 518142 DO 10.1101/518142 A1 Joshua J Carter A1 Timothy M Walker A1 A Sarah Walker A1 Michael G. Whitfield A1 Glenn P. Morlock A1 Timothy EA Peto A1 James E. Posey A1 Derrick W Crook A1 Philip W Fowler YR 2019 UL http://biorxiv.org/content/early/2019/12/18/518142.abstract AB Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis, however antibiotic susceptibility testing for pyrazinamide is problematic. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, an enzyme that converts pyrazinamide into its active form. We curated a derivation dataset of 291 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 based on sequence- and structure-based features of each missense mutation. The clinical performance of the models was estimated by predicting the binary pyrazinamide resistance phenotype of 2,292 clinical isolates harboring missense mutations in pncA. Overall, this work offers an approach to improve 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 such as bedaquiline.