Evidence map›Paper›PMID 42380282›Full record

ArticleNature microbiology2026

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

Irene Lepori, Zichen Liu, Nelson Evbarunegbe, Shasha Feng, Turner P Brown, Kishor Mane, Rachita Dash, Shivangi, Mitchell Wong, Ananya Naick and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Irene Lepori *Department of Microbiology, University of Massachusetts Amherst, Amherst, MA, USA. ilepori@umass.edu.ORCID http://orcid.org/0000-0002-1602-0490
Zichen Liu *Department of Chemistry, University of Virginia, Charlottesville, VA, USA.ORCID http://orcid.org/0009-0001-0295-5076
Nelson Evbarunegbe *Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA.ORCID http://orcid.org/0000-0003-0408-5016
Shasha Feng *Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA.ORCID http://orcid.org/0000-0003-3394-6911
Turner P BrownDepartment of Biological Sciences, Lehigh University, Bethlehem, PA, USA.
Kishor ManeDepartment of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA.
Rachita DashDepartment of Chemistry, University of Virginia, Charlottesville, VA, USA.
ShivangiDepartment of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA.
Mitchell WongDepartment of Microbiology, University of Massachusetts Amherst, Amherst, MA, USA.
Ananya NaickDepartment of Chemistry, University of Virginia, Charlottesville, VA, USA.ORCID http://orcid.org/0009-0005-5669-4317
Amir GeorgeDepartment of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA.
Taijie GuoInstitute of Translational Medicine, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China.
Anil M ShelkeDepartment of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA.
K Barry SharplessDepartment of Chemistry, The Scripps Research Institute, La Jolla, CA, USA.ORCID http://orcid.org/0000-0001-6051-1599
Jiajia DongInstitute of Translational Medicine, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0003-2767-9124
Joel S FreundlichDepartment of Pharmacology, Physiology and Neuroscience, Rutgers University-New Jersey Medical School, Newark, NJ, USA. freundjs@rutgers.edu.ORCID http://orcid.org/0000-0002-3411-3455
Wonpil ImDepartment of Biological Sciences, Lehigh University, Bethlehem, PA, USA. wonpil@lehigh.edu.ORCID http://orcid.org/0000-0001-5642-6041
Anna G GreenManning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA. annagreen@umass.edu.ORCID http://orcid.org/0000-0001-7548-3682
Marcos M PiresDepartment of Chemistry, University of Virginia, Charlottesville, VA, USA. mp7aa@virginia.edu.
M Sloan SiegristDepartment of Microbiology, University of Massachusetts Amherst, Amherst, MA, USA. siegrist@umass.edu.ORCID http://orcid.org/0000-0002-8232-3246

Funding

A Preclinical Program for Targeting Mycobacterium tuberculosis KasAR01AI153145 · NIAID · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI FREUNDLICH, JOEL STEPHEN · 2021 to 2024
$3.2M
Bacterial and Molecular Determinants of Mycobacterial ImpermeabilityR01AI179080 · NIAID · UNIVERSITY OF VIRGINIA · PI Marcos M. Pires, Mary Sloan Siegrist · 2023 to 2026
$2.9M
Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) INV-080847Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID) R01 AI153145Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID) R01 AI179080NIAID NIH HHS R01 AI153145NIAID NIH HHS R01 AI179080
6 · The paper itself

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.

Indexed as

Antitubercular AgentsBacterial Outer MembraneCell MembraneCell Membrane PermeabilityMachine LearningMycobacterium smegmatisMycobacterium tuberculosisAnti-Bacterial AgentsClick ChemistryPermeabilityAnti-Bacterial AgentsAntitubercular Agents

Identifiers

PMID42380282
PMCPMC13524814

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.