Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network.

Constraint-based flux balance analysis (FBA) is a powerful tool for predicting target genes that can be engineered by analyzing the redistribution of metabolic fluxes on specific gene modifications. Specifically, the effects of metabolic gene deletions on flux distribution can be examined by forcing the fluxes of different reactions catalyzed by the corresponding gene product to zero. However, the target enzyme needs to be essential for survival of the organism to ensure that efficient chemical inhibition results in cell stasis or death. Here, we investigate the essentiality of enzymes in iMO1056 metabolic model of nosocomial pathogen Pseudomonas aeruginosa by performing in silico enzyme deletions using FBA. We identified 116/113 essential enzymes in rich medium in P. aeruginosa. These were then compared with human metabolic model to identify nonhomologous enzymes that could be possible drug targets. Here, we present a refined list of 41 novel potential targets for P. aeruginosa. These targets were then matched with the enzymes belonging to 97 correlated clusters through which we propose the concept of "one target per cluster." Our approach relates to the "single drug multiple target (SDMT)" concept and has potential in efficient drug target discovery.