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An LC-MS/MS assay and complementary web-based tool to quantify and predict compound accumulation in E. coli

Abstract

Novel classes of broad-spectrum antibiotics have been extremely difficult to discover, largely due to the impermeability of the Gram-negative membranes coupled with a poor understanding of the physicochemical properties a compound should possess to promote its accumulation inside the cell. To address this challenge, numerous methodologies for assessing intracellular compound accumulation in Gram-negative bacteria have been established, including classic radiometric and fluorescence-based methods. The recent development of accumulation assays that utilize liquid chromatography–tandem mass spectrometry (LC-MS/MS) have circumvented the requirement for labeled compounds, enabling assessment of a substantially broader range of small molecules. Our unbiased study of accumulation trends in Escherichia coli using an LC-MS/MS-based assay led to the development of the eNTRy rules, which stipulate that a compound is most likely to accumulate in E. coli if it has an ionizable Nitrogen, has low Three-dimensionality and is relatively Rigid. To aid in the implementation of the eNTRy rules, we developed a complementary web tool, eNTRyway, which calculates relevant properties and predicts compound accumulation. Here we provide a comprehensive protocol for analysis and prediction of intracellular accumulation of small molecules in E. coli using an LC-MS/MS-based assay (which takes ~2 d) and eNTRyway, a workflow that is readily adoptable by any microbiology, biochemistry or chemical biology laboratory.

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Fig. 1: Stages of the accumulation workflow.
Fig. 2: Accumulation assay workflow.
Fig. 3: Procedure for prediction of compound accumulation in bacteria using eNTRyway.
Fig. 4: Validation of compound accumulation in E. coli.
Fig. 5: Calibration curve and multiple reaction monitoring.

Data availability

The main data discussed in this protocol are available in the supporting primary research papers (https://doi.org/10.1038/nature22308 and https://doi.org/10.1038/s41564-019-0604-5). Source data are provided with this paper. Additional requests should be addressed to the corresponding authors.

Code availability

Source code for eNTRyway for local use is available on GitHub (https://github.com/HergenrotherLab/entry-cli).

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Acknowledgements

We thank M. Richter for optimization and development of the LC-MS/MS-based accumulation assay, and we thank B. Drown for the creation of the web tool eNTRyway. This work was supported by the University of Illinois and the NIH (R01AI136773).

Author information

Authors and Affiliations

Authors

Contributions

E.J.G. and Z.L. performed experiments. All authors wrote the manuscript and were involved in editing of the final manuscript.

Corresponding author

Correspondence to Paul J. Hergenrother.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Kim Lewis and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol:

Richter, M. F. et al. Nature 545, 299–304 (2017): https://doi.org/10.1038/nature22308

Parker, E. N. et al. Nat. Microbiol. 5, 67–75 (2020): https://doi.org/10.1038/s41564-019-0604-5

Motika, S. E. et al. J. Am. Chem. Soc. 142, 10856–10862 (2020): https://doi.org/10.1021/jacs.0c04427

Extended data

Extended Data Fig. 1 Importance of amine steric accessibility and amphiphilic moment (vsurf_A).

a, Primary amines demonstrate higher accumulation than the mono-methyl amine, di-methyl amine, tri-methyl amine and amide comparisons. Primary amines on primary carbons also show improved accumulation over primary amines on secondary or tertiary carbons. b, Increasing amphiphilic moment trends with increasing accumulation. Accumulation is reported in nmol/1012 CFUs. Data are taken from Richter et al.17 with permission.

Extended Data Fig. 2 Screenshot of the ‘input’ box for SMILES strings.

SMILES strings are canonicalized using Open Babel GUI.

Extended Data Fig. 3 Screenshots of the process of predicting accumulation using the web tool eNTRyway.

SMILES strings are submitted to eNTRyway, and compounds are prioritized for evaluation based on how closely they meet the eNTRy rules. In the example here, both ampicillin and 6-DNM-NH3 meet all of the criteria and are predicted to accumulate, whereas penicillin and DNM are not. A portion of this figure is taken from Parker et al.19 with permission.

Supplementary information

Source data

Source Data Fig. 4

Raw accumulation data.

Source Data Fig. 5

Calibration curve and mass spectrum.

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Geddes, E.J., Li, Z. & Hergenrother, P.J. An LC-MS/MS assay and complementary web-based tool to quantify and predict compound accumulation in E. coli. Nat Protoc 16, 4833–4854 (2021). https://doi.org/10.1038/s41596-021-00598-y

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