Modelling proteins' hidden conformations to predict antibiotic resistance

Nat Commun. 2016 Oct 6;7:12965. doi: 10.1038/ncomms12965.

Abstract

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Anti-Bacterial Agents / chemistry*
  • Cefotaxime / chemistry
  • Crystallography, X-Ray
  • Drug Design
  • Drug Resistance, Bacterial*
  • Escherichia coli / enzymology*
  • Kinetics
  • Markov Chains
  • Mass Spectrometry
  • Molecular Dynamics Simulation
  • Mutagenesis, Site-Directed
  • Mutation
  • Protein Binding
  • Protein Conformation
  • Sensitivity and Specificity
  • Solvents
  • beta-Lactamases / chemistry*

Substances

  • Anti-Bacterial Agents
  • Solvents
  • beta-Lactamases
  • beta-lactamase TEM-1
  • Cefotaxime