Computational design of environmental sensors for the potent opioid fentanyl

• DOI: 10.7554/elife.28909
• Publication Date: 2017-09-19T12:00:13Z
• Authors: Matthew J Bick; Per J Greisen; Kevin J Morey; Mauricio S Antunes; David La; Banumathi Sankaran; Luc Reymond; Kai Johnsson; June I Medford; David Baker
• Keywords: Narcotics; Biomedical and clinical sciences; QH301-705.5; Protein Conformation; Science; S. cerevisiae; Gene Expression; Bioengineering; Saccharomyces cerevisiae; transgenic plants; Crystallography, X-Ray; fentanyl; Substance Misuse; 03 medical and health sciences; computational biology; Biochemistry and Chemical Biology; Health Sciences; biochemistry; Biology (General); protein design; 0303 health sciences; Crystallography; Biomedical and Clinical Sciences; Opioid Misuse and Addiction; Q; E. coli; R; Health sciences; Computational Biology; Membrane Proteins; systems biology; Biological Sciences; 540; biosensors; Recombinant Proteins; 3. Good health; plant sensors; Opioids; Fentanyl; Biological sciences; A. thaliana; X-Ray; Medicine; Generic health relevance; Biochemistry and Cell Biology; hydrophobic compounds; Protein Binding

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.