PT - JOURNAL ARTICLE AU - Joshua J Carter AU - Timothy M Walker AU - A Sarah Walker AU - Michael G. Whitfield AU - Timothy EA Peto AU - Derrick W Crook AU - Philip W Fowler TI - Prediction of pyrazinamide resistance in <em>Mycobacterium tuberculosis</em> using structure-based machine learning approaches AID - 10.1101/518142 DP - 2019 Jan 01 TA - bioRxiv PG - 518142 4099 - http://biorxiv.org/content/early/2019/01/11/518142.short 4100 - http://biorxiv.org/content/early/2019/01/11/518142.full AB - Pyrazinamide is one of four first-line antibiotics currently used to treat tuberculosis and has been included in newer treatment regimens undergoing clinical trials due to its unique sterilizing effects and synergy with newer drugs. However, phenotypic antibiotic susceptibility testing for pyrazinamide is problematic. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, which encodes 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 studies of clinical isolates and in vitro/in vivo screening studies and then trained machine learning models to predict pyrazinamide resistance based on sequence- and structure-based features of each missense mutation. The clinical relevance of the models was tested by predicting the binary resistance phenotype of 2,292 clinical isolates harboring missense mutations in PncA to pyrazinamide. The probabilities of resistance predicted by the model were also compared with in vitro pyrazinamide minimum inhibitory concentrations of 27 isolates to determine whether the machine learning model could predict the degree of resistance. Finally, we predicted the effect on pyrazinamide resistance of the remaining 814 possible missense mutations caused by single nucleotide polymorphisms in PncA that have not yet been observed in public databases. Overall, this work offers an approach to improve the sensitivity and specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows for tuberculosis, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs such as bedaquiline.