Abstract
Two membrane cell envelopes act as selective permeability barriers in Gram-negative bacteria, protecting cells against antibiotics and other small molecules. Significant efforts are being directed toward understanding how small molecules permeate these barriers. In this study, we developed an approach to analyze the permeation of compounds into Gram-negative bacteria and applied it to Pseudomonas aeruginosa, an important human pathogen notorious for resistance to multiple antibiotics. The approach uses mass spectrometric measurements of accumulation of a library of structurally diverse compounds in four isogenic strains of P. aeruginosa with varied permeability barriers. We further developed a machine learning algorithm that generates a deterministic classification model with minimal synonymity between the descriptors. This model predicted good permeators into P. aeruginosa with an accuracy of 89% and precision above 58%. The good permeators are broadly distributed in the property space and can be mapped to six distinct regions representing diverse chemical scaffolds. We posit that this approach can be used for more detailed mapping of the property space and for rational design of compounds with high Gram-negative permeability.
| Original language | English |
|---|---|
| Article number | 8220 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Funding
This work was supported by Defense Threat Reduction Agency grant HDTRA1-14-1-0019 (H.I.Z. and V.V.R) and National Institutes of Health grant RO1-AI052293 (H.I.Z, J.M.P., JCS. and V.V.R), RO1-AI136795 (H.I.Z, V.V.R and A.S.D) and RO1-AI132836 (H.I.Z.). The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We thank Rosemary O’Shea, James Aggen and John K. Walker for help in selection of compound libraries. This work was supported by Defense Threat Reduction Agency grant HDTRA1-14-1-0019 (H.I.Z. and V.V.R) and National Institutes of Health grant RO1-AI052293 (H.I.Z, J.M.P., JCS. and V.V.R), RO1-AI136795 (H.I.Z, V.V.R and A.S.D) and RO1-AI132836 (H.I.Z.). The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We thank Rosemary O’Shea, James Aggen and John K. Walker for help in selection of compound libraries.