Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning

The ability of compounds to permeate and accumulate in bacterial cells is a critical determinant of antibiotic efficacy. Better therapeutics are urgently needed for the human pathogen Mycobacterium tuberculosis, yet the cell envelope, including the mycobacterial outer membrane, represents a significant barrier for drug entry, and the chemical features governing permeation remain poorly understood. Here we used the bioorthogonal click chemistry-based PAC-MAN assay to profile mycomembrane permeation of 1,572 azide-tagged compounds in M. tuberculosis and the model organism M. smegmatis. Cheminformatics and machine learning identified chemical features associated with mycomembrane permeation, which in turn had predictive value in three molecule series. Chemical predictors of mycomembrane permeation include nitrogen-containing aromatic scaffolds, such as indole, which in some cases were associated with increased anti-M. tuberculosis activity. Our data suggest a rational framework for improving mycomembrane permeation and whole-cell activity of antibiotics targeting M. tuberculosis.

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Publication Details

Journal
Nature Microbiology
Published
2026-06-30
DOI
https://doi.org/10.1038/s41564-026-02412-5
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning

Z W Liu, Taijie Guo, Irene Lepori, Marcos M. Pires et al.
Nature Microbiology
Tuberculosis Research and Epidemiology
article

Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning

Z W Liu, Taijie Guo, Irene Lepori, Marcos M. Pires, Shasha Feng, Wonpil Im, Joel S. Freundlich, Nelson Iyore Evbarunegbe, Jiajia Dong, Anil M. Shelke, Anna G. Green, K. Barry Sharpless, M. Sloan Siegrist, Rachita Dash, Kishor Mane, Amir George, Ananya Naick, Mitchell Wong, Shivangi, Turner P. Brown
article en

Abstract

The ability of compounds to permeate and accumulate in bacterial cells is a critical determinant of antibiotic efficacy. Better therapeutics are urgently needed for the human pathogen Mycobacterium tuberculosis, yet the cell envelope, including the mycobacterial outer membrane, represents a significant barrier for drug entry, and the chemical features governing permeation remain poorly understood. Here we used the bioorthogonal click chemistry-based PAC-MAN assay to profile mycomembrane permeation of 1,572 azide-tagged compounds in M. tuberculosis and the model organism M. smegmatis. Cheminformatics and machine learning identified chemical features associated with mycomembrane permeation, which in turn had predictive value in three molecule series. Chemical predictors of mycomembrane permeation include nitrogen-containing aromatic scaffolds, such as indole, which in some cases were associated with increased anti-M. tuberculosis activity. Our data suggest a rational framework for improving mycomembrane permeation and whole-cell activity of antibiotics targeting M. tuberculosis.

Nature Microbiology
Rutgers, The State University of New Jersey (US), Scripps Research Institute (US), Merck & Co., Inc., Rahway, NJ, USA (United States) (US), Amherst College (US), Shanghai Jiao Tong University (CN), Lehigh University (US), University of Massachusetts Amherst (US), Shanghai Advanced Research Institute (CN), Rutgers New Jersey Medical School (US), University of Virginia (US)
Bill and Melinda Gates Foundation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases
Openalex Percentile: Top 9%
Tuberculosis Research and Epidemiology
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