Minimal Computational Framework for Systematic Identification of Antimicrobial Targets

Systematic identification of antimicrobial targets remains a major challenge, as discovery still relies largely on empirical, resource-intensive approaches with limited efficiency. We present a method for identifying antimicrobial targets based on protein dynamics, enabling rational polypharmacology. The approach spans multiple biological scales, from taxa (genus and species) to biological networks, including network hubs and edges, their constituent proteins, protein binding sites, and their conformational states. It is grounded in the premise that coordinated intervention across multiple, optimally selected targets, using combinations of compounds at safe or submaximal doses, can achieve therapeutic effects while reducing toxicity and limiting mutational escape. A survey of known antimicrobials indicates that a small number of recurrent protein-level mechanisms account for most disruptions of microbial survival. We introduce metrics to detect these mechanisms across a pathogen proteome and describe a streamlined, modular workflow for target identification and prioritization that is optimized for ease of deployment and naturally interfaces with downstream applications such as molecular screening and de novo design.