Computational design of environmental sensors for the potent opioid fentanyl
Abstract
Here, 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 also 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:
-
[1];
[5]
- Department of Biochemistry, University of Washington, Seattle, United States
- Department of Biology, Colorado State University, Fort Collins, United States
- Molecular Biophysics and Integrated Bioimaging, Berkeley Center for Structural Biology, Lawrence Berkeley National Laboratory, Berkeley, United States
- 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
- 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)
- 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. https://doi.org/10.7554/eLife.28909.
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. https://doi.org/10.7554/eLife.28909
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. https://doi.org/10.7554/eLife.28909.
@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 = {Here, 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 also 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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https://doi.org/10.7554/eLife.28909
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