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Related Experiment Video

Updated: Apr 19, 2026

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection
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Protein design algorithms predict viable resistance to an experimental antifolate.

Stephanie M Reeve1, Pablo Gainza2, Kathleen M Frey1

  • 1Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269; and.

Proceedings of the National Academy of Sciences of the United States of America
|January 2, 2015
PubMed
Summary

This study used a protein design algorithm to predict bacterial mutations conferring drug resistance. The findings demonstrate a novel method for designing more resilient drug candidates against evolving pathogens.

Keywords:
DHFRMRSAantifolatedrug resistanceprotein design

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Area of Science:

  • Biochemistry
  • Microbiology
  • Structural Biology

Background:

  • Predicting drug target mutations is crucial for developing resilient drug candidates.
  • Antibiotic resistance in bacteria poses a significant public health threat.
  • Dihydrofolate reductase (DHFR) from Staphylococcus aureus is a key drug target.

Purpose of the Study:

  • To prospectively identify mutations conferring resistance to a DHFR inhibitor using a structure-based protein design algorithm.
  • To investigate the catalytic competence, resistance, and fitness of predicted mutations.
  • To elucidate the structural basis of resistance and compensatory mutations.

Main Methods:

  • Utilized the K* algorithm within the OSPREY suite for structure-based protein design.
  • Employed enzyme kinetics, microbiology assays, and X-ray crystallography.
  • Performed in vitro selection of resistant bacteria.

Main Results:

  • Identified single-nucleotide polymorphisms conferring resistance to an experimental DHFR inhibitor.
  • Four top-ranked mutations were catalytically competent and resistant.
  • Two predicted mutations emerged in conjunction with a compensatory mutation.
  • Determined the fitness of mutant enzymes and strains, and the structural basis of resistance.

Conclusions:

  • Protein design algorithms can prospectively predict viable bacterial resistance mutations under antibiotic pressure.
  • This approach aids in the design of more resilient first-generation drug candidates.
  • Understanding compensatory mutations is key to combating antibiotic resistance.