Prioritizing Drug Targets in Pathogenic Bacteria by Harnessing Structural Biology, Metabolic Analysis, and Omics Data Integration

Abstract

The increasing incidence of antibiotic resistance is a significant concern, particularly with the emergence of multidrug-resistant bacterial strains. Several mechanisms contribute to pathogens becoming resistant to traditional antibiotics that were once effective against them, influenced by prolonged use of a single therapy and inappropriate practices. To address this challenge, it is essential to explore new strategies and develop innovative compounds. Bioinformatic analysis plays a crucial role in speeding up this process. Integrating high-throughput data, along with information from public databases, has facilitated the implementation of in silico drug discovery pipelines. This involves considering factors such as druggability, essentiality, metabolic context, conservation in clinical strains, and potential off-target effects. Web servers, such as Target-Pathogen, combine diverse data and provide tools to effectively prioritize drug targets. The effectiveness of integrating bioinformatics into drug discovery efforts is underscored by the successful application of this approach in the case of Listeria monocytogenes.