Antibiotic resistant infections are projected to cause over 10 million deaths by 2050, yet the development of new antibiotics has slowed. This points to an urgent need for methodologies for the rapid development of antibiotics against emerging drug resistant pathogens. We report on a generalizable combined computational and synthetic approach, called antibody-recruiting proteincatalyzed capture agents (AR-PCCs), to address this challenge. We applied the combinatorial PCC technology to identify macrocyclic peptide ligands against highly conserved surface protein epitopes of carbapenem-resistant Klebsiella pneumoniae, an opportunistic gram-negative pathogen with drug resistant strains. Multi-omic data combined with bioinformatic analyses identified epitopes of the highly expressed MrkA surface protein of K. pneumoniae for targeting in PCC screens. The top-performing ligand exhibited high-affinity (EC50~50 nM) to full-length MrkA, and selectively bound to MrkAexpressing K. pneumoniae, but not to other pathogenic bacterial species. AR-PCCs conjugated with immunogens promoted antibody recruitment to K. pneumoniae, leading to phagocytosis and phagocytic killing by macrophages. The rapid development of this highly targeted antibiotic implies that the integrated computational and synthetic toolkit described here can be used for the accelerated production of antibiotics against drug resistant bacteria. resistant K. pneumoniae, and that the basic technology might provide a route towards drugging "undruggable" pathogenic bacteria.
Results and DiscussionMulti-omic analyses to select target a protein on K. pneumoniae Here we describe the algorithm used to identify protein targets, and epitopes on those targets, for drugging K. pneumoniae using AR-PCCs. Traditional drugging strategies tend to rely on disrupting the function of, for example, an enzyme by competing for occupancy within a strategic hydrophobic binding pocket. Our requirements are very different. Instead, favorable aspects of target proteins are high expression levels on only the pathogen of interest, plus localization of that protein to the outer membrane or extracellular space of the pathogen. Further, once such a target protein is identified, there are additional considerations regarding which epitopes of that protein present the greatest opportunities for exploiting AR-PCCs. The flow diagram in Figure 2A delineates the strategy for identifying MrkA as an ideal target protein. Protein expression levels can vary across environments and growth phases, so we analyzed the transcriptional data reported by Guilhen et al(40) to identify proteins