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Morphological Deconvolution of Beta-Lactam Polyspecificity in E. coli.

William J Godinez1,2, Helen Chan2, Imtiaz Hossain1

  • 1Chemical Biology and Therapeutics , Novartis Institutes for BioMedical Research , Basel , Switzerland.

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|June 12, 2019
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This summary is machine-generated.

We developed a novel machine learning assay to profile beta-lactam antibiotic activity in E. coli. This method quantifies how different beta-lactams target specific penicillin-binding proteins (PBPs), aiding antibiotic design.

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Area of Science:

  • Microbiology
  • Biochemistry
  • Computational Biology

Background:

  • Beta-lactam antibiotics are crucial for bacterial cell wall synthesis by inhibiting penicillin-binding proteins (PBPs).
  • Understanding beta-lactam polyspecificity is key to designing effective antibiotics, but traditional assays are low-throughput and performed ex situ.
  • Cellular morphology changes induced by beta-lactams depend on the specific PBPs targeted.

Purpose of the Study:

  • To develop a medium-throughput, image-based assay combined with machine learning to profile beta-lactam activity in live E. coli cells.
  • To automatically and quantitatively relate different beta-lactam antibiotics to their PBP-binding preferences within cells.
  • To demonstrate the utility of this approach for guiding the design of novel PBP inhibitors.

Main Methods:

  • Utilized an image-based assay coupled with machine learning algorithms for automated analysis.
  • Tested beta-lactam activity across a panel of E. coli strains with specific PBP enzyme perturbations.
  • Quantified cellular morphological changes in response to various beta-lactam treatments.

Main Results:

  • Successfully profiled the activity of beta-lactams in E. coli, revealing differential PBP engagement.
  • Established a quantitative relationship between beta-lactam structure and PBP-binding profiles.
  • Predicted the mechanisms of action for two recently reported PBP inhibitors with high accuracy.

Conclusions:

  • The developed assay offers a medium-throughput, in situ method for characterizing beta-lactam-PBP interactions.
  • This approach can guide the rational design of novel beta-lactam antibiotics with tailored PBP-binding specificities.
  • Machine learning integration enhances the ability to predict antibiotic mechanisms and optimize drug development.