Computational design of environmental sensors for the potent opioid fentanyl

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This article describes the computational design of proteins that bind the potent analgesic fentanyl.

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23 p.

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Bick, Matthew J.; Greisen, Per J.; Morey, Kevin J.; Antunes, Mauricio S.; La, David; Sankaran, Banumathi et al. September 19, 2017.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Science to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 55 times. More information about this article can be viewed below.

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Description

This article describes the computational design of proteins that bind the potent analgesic fentanyl.

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23 p.

Notes

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.

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  • eLife, 2017. Cambridge, UK: eLife Sciences Publications, Ltd

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  • Publication Title: eLife
  • Volume: 6
  • Page Start: 1
  • Page End: 23
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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Creation Date

  • September 19, 2017

Submitted Date

  • May 23, 2017

Accepted Date

  • September 18, 2017

Added to The UNT Digital Library

  • March 15, 2019, 11:51 a.m.

Description Last Updated

  • March 16, 2021, 10:18 a.m.

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Bick, Matthew J.; Greisen, Per J.; Morey, Kevin J.; Antunes, Mauricio S.; La, David; Sankaran, Banumathi et al. Computational design of environmental sensors for the potent opioid fentanyl, article, September 19, 2017; Cambridge, United Kingdom. (https://digital.library.unt.edu/ark:/67531/metadc1459146/: accessed June 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Science.

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