A systems chemical biology approach to identify targets of antibacterial agents: A case study of Chelerythrine and Rhein

Mycobacterium tuberculosis (MTB) and Staphylococcus aureus (STA) are very common and complicated diseases that infect both humans and animals. The emergence of multidrug-resistant bacterium represents an urgent need to identify new drug targets and therapeutic drugs. In this study, we propose a systems chemical biology method to identify targets of anti-bacterial agents. This method integrates gene expression profiles of MTB upon chemical treatment and prior knowledge of protein-protein interactions (PPI) to predict antibacterial targets. We first validate the method by examining the targets of two approved anti-MTB drugs. Then, we predict the targets for Chelerythrine and Rhein, an anti-MTB natural product extracted from Chelidonium majus and a natural anti-bacterial agent for STA respectively. The identified targets are further evaluated visually by molecular docking and molecular dynamics simulation. The results show that our method is applicable for predicting the potential targets of anti-bacterial agents and has stable performance among multiple datasets across different species. Our strategy is expected to be used in target identification for other antibacterial.