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SummaryPyrazinamide 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 inpncA,an enzyme that converts pyrazinamide into its active form. We curated a derivation dataset of 291 non-redundant, missense amino acid mutations inpncAwith 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 inpncA. 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.

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