Small-Molecule Penetration and Efflux in Gram-Negative Bacteria - PROJECT SUMMARY Small-Molecule Penetration and Efflux in Gram-Negative Bacteria Gram-negative bacterial infections are increasing in incidence and novel antibiotics are urgently needed to combat this growing threat to public health. These pathogens have high intrinsic resistance to antibiotics due to their combination of a two-membrane cell envelope, which presents a permeability barrier to small molecules, and prevalent efflux pumps, which eject molecules that have successfully penetrated the barrier. A major obstacle to the development of novel antibiotics is our poor understanding of the structural features of small molecules that correlate with penetration and efflux across this barrier. As a result, large screening campaigns of existing discovery libraries have mostly failed to provide new antibacterials. Similarly, while potent biochemical inhibitors can often be identified for new targets, converting them into compounds with whole-cell antibacterial activity has proven challenging. To address this critical problem, we have developed a comprehensive experimental and computational platform to evaluate and model Small-molecule Penetration and Efflux in Antibiotic-Resistant Gram-Negative bacteria (SPEAR-GN, “speargun”). We have established all of the key enabling technologies required and strong proof of concept for the effectiveness of this platform. We will now use our platform to assemble the larger datasets required to train robust machine learning models of penetration and efflux that will be of broad utility in antibacterial drug discovery. Herein, we will design and synthesize chemical libraries to map bacterial penetration and efflux space, analyze compound accumulation in Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii using isogenic strain sets that decouple outer-membrane penetration from efflux, evaluate the role of the BamA outer membrane protein in facilitated permeation of antibiotics, develop machine learning models based on the assay data to predict compound accumulation, and use the models to design and synthesize new molecules with improved accumulation in Gram-negative bacteria. Success in this project will provide a major advance in the field of antibacterial drug discovery to address this major public health threat. This project will be carried out by an established multidisciplinary team with a proven track record in this field and extensive combined expertise in library synthesis and medicinal chemistry, biochemistry and microbiology, high-throughput assays and mass spectrometry, cheminformatics and machine learning, and antibacterial drug discovery.
