Prediction of antibiotic activity

This is an interdisciplinary program involving the Hu Lab and the Davis Research Group.

We are building bacterial knockdown libraries as genetic tools to predict antibiotic activity in new compounds and to determine their mechanism of action. Our collections contain knockdown mutants in essential genes, which usually code for targets of antibiotics. The overarching hypothesis of our research is that chemogenetic profiles of essential gene knockdowns will better predict antibiotic activity and mode of action (MOA) of novel compounds, accelerating antibiotic discovery. Our knockdown CRISPRi libraries are valuable tools for machine learning-based prediction of antibiotic activity and mode of action of any active antimicrobial compound. The kncokdown mutants have different susceptibility to molecules with antibiotic activity, which can signal drug-target interactions.

Drug-gene interaction matrices can help us develop a machine learning platform (MLP) that can be used for in silico screening of ultra-large, chemically diverse virtual libraries to accurately predict the bioactivity and MOA of the compounds. This approach will unlock the possibilities of virtually testing billions of ‘drug-like’ compounds and exploration of broader targets (essential genome) and chemical scaffolds that are not available otherwise and thus can increase the probability of finding new drug classes.

Funded by