Breaching the Gram negative bacterial cellular envelope

Texas A&M University Health Science Ctr, College Station TX

Investigators

Abstract

The cellular envelope of GN bacterium, consisting of the outer membrane, inner membrane, and an array of specific and multidrug efflux transporters, presents a formidable barrier for the development of antimicrobials with cytosolic targets. Insight into the physicochemical properties that enable the entrance of compounds is critical to direct future effort in the development of effective treatment against GN pathogens. Toward this goal, methods to accurately quantify the accumulation of small molecule compounds, independent of their antibacterial activity, will be developed. Compound accumulation in Acinetobacter baumannii, an ESKAPE pathogen, will be determined. In addition, novel computational algorithms will be developed to explore the chemical space, describe and predict physicochemical properties that favors entry and evade efflux in GN bacteria. The computer model will be validated and modified through the selection and characterization of hundreds of compounds. Specifically, the following aims will be pursued: Aim 1) To develop and validate assays to quantify compound partition. A panel of commercially available antibiotics will be used to develop and validate assays to accurately determine the accumulation of small molecule compounds in different cellular compartments of A. baumannii using LC/MS/MS. The contribution of efflux and OM obstruction will be dissected using strategic mutant strains. These assays will be used in combination of in silico evaluation of compound accumulation, and experimental data will feed into the screening and modeling effort to iteratively improve the computational algorism. Aim 2) To develop a computational algorithm to identify compounds that are potentially good penetrators for GN bacteria and elucidate the role of permeability and efflux. A new and highly accurate computational algorithm through machine learning will be developed, which can systematically recognize all favorable structural factors of the compounds effective for GN bacteria. In addition, candidate compounds emerge from the analysis will be subjected to MD simulation to further analyze their potential interaction with the major efflux transporter. Selected compounds will be experimentally characterized in Aim 3, the knowledge from which will be incorporated back into refining the computer algorithms to generate a new round of learning/prediction. This cycle will be repeated three time, with the testing of 300-400 compounds each cycle. Aim 3) To measure the accumulation and subcellular distribution of selected compounds and to validate rules on compound accumulation. With the assays from Aim 1, the accumulation data of compounds selected through Aim 2 will be determined. Data from these analyses will be used to further polish the computer algorisms in Aim 2. Structure features that facilitate accumulation in GN bacteria will be derived. Outcome from the proposed study will help bridge the gap between a potent inhibitor and an effective antibacterial drug. The combined use of the detailed experimental assays and the new computational algorithm will empower drug discovery efforts that aim to identify promising drug candidates targeting GN bacteria.