The imminent need for the development of new antibacterial drugs led us to develop inhibitors targeted against components in protein translation. Specifically, we used a fragment-based screening workflow in which the first step was the novel exploitation of NMR transverse relaxation times to identify fragment molecules that bind specifically to RNA hairpin 91 in the ribosomal PTC of M. tuberculosis. This initial screening was followed by computational optimization of the fragment molecules into larger molecules with drug-like properties. Specifically, a virtual filtration followed by a high-throughput docking procedure yielded drug-sized molecules. We trained various machine-learning models for predicting the docking binding free energy as a function of geometric features extracted from each of the above molecules. As superior inhibitors, the machine-learning model predicted two molecules that exhibited IC50 values superior to that of chloramphenicol, an antibiotic drug that acts on the ribosomal PTC. Finding of this study are recently published (Tam B. et al, Chemical Science 2019). Intrigued by these recent results we have synthesized derivatives with improved binding/inhibitory properties. Our studies funded by the BSF will yield new antituberculous agents and will provide new tools for fragment-based lead discovery.
|Effective start/end date
|1/01/16 → …
- United States-Israel Binational Science Foundation (BSF)