Computational Design of Environmental Sensors for the Potent Opioid Fentanyl

Elife. 2017 Sep 19;6:e28909. doi: 10.7554/eLife.28909.

Abstract

We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.

Keywords: A. thaliana; E. coli; S. cerevisiae; biochemistry; biosensors; computational biology; protein design; systems biology; transgenic plants.

Publication types

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

MeSH terms

  • Computational Biology / methods*
  • Crystallography, X-Ray
  • Fentanyl / metabolism*
  • Gene Expression
  • Membrane Proteins / genetics
  • Membrane Proteins / metabolism
  • Narcotics / metabolism*
  • Protein Binding
  • Protein Conformation
  • Recombinant Proteins / chemistry
  • Recombinant Proteins / genetics*
  • Recombinant Proteins / metabolism*
  • Saccharomyces cerevisiae / genetics
  • Saccharomyces cerevisiae / metabolism

Substances

  • Membrane Proteins
  • Narcotics
  • Recombinant Proteins
  • Fentanyl