Abstract
Tuberculosis (TB) caused by Mycobacterium tuberculosis is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasingly common multi-drug resistant TB. Topoisomerase I is essential for the viability of M. tuberculosis and has been validated as a new target for the discovery of novel treatment against TB resistant to the currently available drugs. Virtual high-throughput screening based on machine learning was used in this study to identify small molecules that target the binding site of divalent ion near the catalytic tyrosine of M. tuberculosis topoisomerase I. From the virtual screening of more than 2 million commercially available compounds, 96 compounds were selected for testing in topoisomerase I relaxation activity assay. The top hit that has IC(50) of 7 µM was further investigated. Commercially available analogs of the top hit were purchased and tested with the in vitro enzyme assay to gain further insights into the molecular scaffold required for topoisomerase inhibition. Results from this project demonstrated that novel small molecule inhibitors of bacterial topoisomerase I can be identified starting with the machine-learning-based virtual screening approach.