(150c) Investigate the Druggability of Protein Targets for Listeria Monocytogenes for Drug Discovery

It is reported that approximately 48 million cases of foodborne illnesses take place in the U.S. every year. Among them are approximately 1600 cases of listeriosis that result in about 260 deaths. Vulnerable populations, including the elderly, children, and pregnant women, face a fatality rate as high as 30%. Listeriosis, caused by Listeria monocytogenes, ranks as the third leading cause of death due to foodborne pathogens. L. monocytogenes, a resilient facultative intracellular pathogen, is prevalent worldwide and poses significant challenges to the food industry due to its presence in various environments and its transmission via contaminated foods and surfaces. The primary treatment for listeriosis involves ampicillin, yet concerns persist regarding the emergence of antibiotic resistance due to lateral gene transfer.

Efforts to control L. monocytogenes focus on disrupting stress response, virulence, and antibiotic resistance mechanisms. Enhancements in these areas could improve food preservation techniques, diminish virulence, and counteract antibiotic resistance, thereby positively impacting L. monocytogenes treatment. Developing novel treatments presents challenges, with Phase II drug failures often attributed to lack of efficacy. Effective drug targets should play a functional role in the disease process and be druggable. However, little work has been done to evaluate the druggability of potential targets for L. monocytogenes. This study aims to address this gap by developing a druggability-evaluation model based on protein binding pocket structure features. In particular, a ligand-protein computational platform was implemented to extract structural features of 535 binding pockets for known antimicrobials. These features were analyzed to identify the key features that can be used as the input of a logistic regression model. The pockets bound with known antimicrobials were regarded as druggable, with a value of one for druggability. On the contrary, a zero value of druggability was assigned to the pockets without binding ligands in the same protein target. The druggability is used as the output of the logic regression model. After the model was validated by existing data, it was applied to evaluate 23 key proteins from L. monocytogenes identified in existing literature. Notably, protein groEL, fliH/fliI complex, fliG, flhB, flgL, flgK, inlA, mogR, and prfA emerged as high-potential druggable targets. These findings lay the groundwork for future research aimed at identifying compounds that inhibit these druggable targets and designing experimental studies to confirm their effectiveness as drug targets.