Computational design of environmental sensors for the potent opioid fentanyl
- University of Washington, United States
- Colorado State University, United States
- Lawrence Berkeley National Laboratory, United States
- Institute of Chemical Sciences and Engineering (ISIC), Switzerland
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.
Article and author information
Author details
Funding
National Cancer Institute (F32CA171572)
- Matthew J Bick
Howard Hughes Medical Institute
- Matthew J Bick
- Per J Greisen
- David La
- David Baker
Defense Threat Reduction Agency (HDTRA1-13-1-0054)
- Matthew J Bick
- Per J Greisen
- Kevin J Morey
- Mauricio S Antunes
- June I Medford
- David Baker
European Molecular Biology Organization (EMBO ALTF 1605-2011)
- Per J Greisen
Carlsbergfondet
- Per J Greisen
National Institutes of Health
- Banumathi Sankaran
National Institute of General Medical Sciences
- Banumathi Sankaran
U.S. Department of Energy (DE-AC02-05CH11231)
- Banumathi Sankaran
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Benjamin F Cravatt, The Scripps Research Institute, United States
Publication history
- Received: May 23, 2017
- Accepted: September 18, 2017
- Accepted Manuscript published: September 19, 2017 (version 1)
- Version of Record published: October 24, 2017 (version 2)
Copyright
© 2017, Bick et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 7,211
- Page views
-
- 1,021
- Downloads
-
- 52
- Citations
Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Computational design of environmental sensors for the potent opioid fentanyl
eLife 6:e28909.
https://doi.org/10.7554/eLife.28909
Further reading
-
- Biochemistry and Chemical Biology
Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.
-
- Biochemistry and Chemical Biology
- Structural Biology and Molecular Biophysics
SARS-CoV-2 emergent variants are characterized by increased viral fitness and each shows multiple mutations predominantly localized to the spike (S) protein. Here, amide hydrogen/deuterium exchange mass spectrometry has been applied to track changes in S dynamics from multiple SARS-CoV-2 variants. Our results highlight large differences across variants at two loci with impacts on S dynamics and stability. A significant enhancement in stabilization first occurred with the emergence of D614G S followed by smaller, progressive stabilization in subsequent variants. Stabilization preceded altered dynamics in the N-terminal domain, wherein Omicron BA.1 S showed the largest magnitude increases relative to other preceding variants. Changes in stabilization and dynamics resulting from S mutations detail the evolutionary trajectory of S in emerging variants. These carry major implications for SARS-CoV-2 viral fitness and offer new insights into variant-specific therapeutic development.
