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Title: Computational design of environmental sensors for the potent opioid fentanyl

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.

Authors:
ORCiD logo [1];  [1];  [2];  [2];  [1];  [3];  [4];  [4];  [2]; ORCiD logo [5]
+ Show Author Affiliations
  1. Department of Biochemistry, University of Washington, Seattle, United States
  2. Department of Biology, Colorado State University, Fort Collins, United States
  3. Molecular Biophysics and Integrated Bioimaging, Berkeley Center for Structural Biology, Lawrence Berkeley National Laboratory, Berkeley, United States
  4. Ecole Polytechnique Fédérale de Lausanne, Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, Department of Chemical Biology, Max-Planck-Institute for Medical Research, Heidelberg, Germany
  5. Department of Biochemistry, University of Washington, Seattle, United States, Howard Hughes Medical Institute, University of Washington, Seattle, United States
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1392706
Alternate Identifier(s):
OSTI ID: 1392707; OSTI ID: 1416923
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Published Article
Journal Name:
eLife
Additional Journal Information:
Journal Name: eLife Journal Volume: 6; Journal ID: ISSN 2050-084X
Publisher:
eLife Sciences Publications, Ltd.
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Bick, Matthew J., Greisen, Per J., Morey, Kevin J., Antunes, Mauricio S., La, David, Sankaran, Banumathi, Reymond, Luc, Johnsson, Kai, Medford, June I., and Baker, David. Computational design of environmental sensors for the potent opioid fentanyl. United States: N. p., 2017. Web. doi:10.7554/eLife.28909.
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Bick, Matthew J., Greisen, Per J., Morey, Kevin J., Antunes, Mauricio S., La, David, Sankaran, Banumathi, Reymond, Luc, Johnsson, Kai, Medford, June I., & Baker, David. Computational design of environmental sensors for the potent opioid fentanyl. United States. doi:10.7554/eLife.28909.
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Bick, Matthew J., Greisen, Per J., Morey, Kevin J., Antunes, Mauricio S., La, David, Sankaran, Banumathi, Reymond, Luc, Johnsson, Kai, Medford, June I., and Baker, David. Tue . "Computational design of environmental sensors for the potent opioid fentanyl". United States. doi:10.7554/eLife.28909.
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@article{osti_1392706,
title = {Computational design of environmental sensors for the potent opioid fentanyl},
author = {Bick, Matthew J. and Greisen, Per J. and Morey, Kevin J. and Antunes, Mauricio S. and La, David and Sankaran, Banumathi and Reymond, Luc and Johnsson, Kai and Medford, June I. and Baker, David},
abstractNote = {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.},
doi = {10.7554/eLife.28909},
journal = {eLife},
number = ,
volume = 6,
place = {United States},
year = {2017},
month = {9}
}
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Journal Article:
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DOI: 10.7554/eLife.28909
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