Data availability
Raw deep sequencing data are deposited in the SRA (BioProject ID PRJNA1256820), and analyzed deep sequencing data are on Zenodo (https://doi.org/10.5281/zenodo.15298584)[68](https://www.nature.com/articles/s41467-025-67994-w#ref-CR68 “Leonard, A. C. et al. Compu…
Data availability
Raw deep sequencing data are deposited in the SRA (BioProject ID PRJNA1256820), and analyzed deep sequencing data are on Zenodo (https://doi.org/10.5281/zenodo.15298584)68. PDB ID of structure used for structural replacement code: 3QN1 All other data are available in the main text or the supplementary materials. Source Data are provided with this paper as “Source Data.xlsx”. Primers, plasmids, and synthetic DNA fragments used in this paper are provided as Supplementary Data 1–3, respectively. Source data are provided with this paper.
Code availability
An automated protocol for the TREMD conformer generation protocol is available on GitHub https://github.com/ajfriedman22/SM_ConfGen. PyRosetta scripts for biosensor design by structural replacement are available at https://github.com/alisoncleonard/Structural-Replacement-Biosensor-Design. Scripts used to generate Supplementary Figs. 1 and 10 and to process raw deep sequencing data are available at https://github.com/WhiteheadGroup/Leonard_ComputationalDesign_Supplemental69.
References
National Institute on Drug Abuse. Drug overdose deaths: facts and figures. Natl. Inst. Drug Abuse. https://nida.nih.gov/research-topics/trends-statistics/overdose-death-rates (2024). 1.
Opioid overdose. https://www.who.int/news-room/fact-sheets/detail/opioid-overdose (2025). 1.
Prekupec, M. P., Mansky, P. A. & Baumann, M. H. Misuse of novel synthetic opioids: a deadly new trend. J. Addict. Med. 11, 256–265 (2017).
CDC. Fentanyl facts. Stop Overdose. https://www.cdc.gov/stop-overdose/caring/fentanyl-facts.html (2024). 1.
New, Dangerous Synthetic Opioid in D.C., Emerging in Tri-State Area. DEA. https://www.dea.gov/stories/2022/2022-06/2022-06-01/new-dangerous-synthetic-opioid-dc-emerging-tri-state-area (2022). 1.
CCENDU Drug Alert: Nitazenes. (Canadian Centre on Substance Use and Addiction, 2022). 1.
De Vrieze, L. M. et al. In vitro structure-activity relationships and forensic case series of emerging 2-benzylbenzimidazole ‘nitazene’ opioids. Arch. Toxicol. 98, 2999–3018 (2024).
Holland, A. et al. Nitazenes-heralding a second wave for the UK drug-related death crisis? Lancet Public Health 9, e71–e72 (2024).
Krausz, R. M. et al. Shifting North American drug markets and challenges for the system of care. Int. J. Ment. Health Syst. 15, 86 (2021).
Uljon, S. Advances in fentanyl testing. Adv. Clin. Chem. 116, 1–30 (2023).
Krieger, M. S. et al. Use of rapid fentanyl test strips among young adults who use drugs. Int. J. Drug Policy 61, 52–58 (2018).
Lieberman, M., Badea, A., Desnoyers, C., Hayes, K. & Park, J. N. An urgent need for community lot testing of lateral flow fentanyl test strips marketed for harm reduction in Northern America. Harm. Reduct. J. 21, 115 (2024).
Green, H. H. New class of opioids that may be more potent than fentanyl emerges globally. The Guardian. https://www.theguardian.com/us-news/2024/sep/25/opioid-crisis-nitazenes-fentanyl (2024). 1.
Steiner, P. J. et al. A closed form model for molecular ratchet-type chemically induced dimerization modules. Biochemistry 62, 281–291 (2023).
Ziegler, M. J. et al. Mandipropamid as a chemical inducer of proximity for in vivo applications. Nat. Chem. Biol. 18, 64–69 (2021).
Park, S. Y. et al. Agrochemical control of plant water use using engineered abscisic acid receptors. Nature 520, 545–548 (2015).
Melcher, K. et al. A gate-latch-lock mechanism for hormone signalling by abscisic acid receptors. Nature 462, 602–608 (2009).
Beltrán, J. et al. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01364-5 (2022). 1.
Park, S.-Y. et al. An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity. Nat. Chem. Biol. 20, 103–110 (2024).
Leonard, A. C. & Whitehead, T. A. Design and engineering of genetically encoded protein biosensors for small molecules. Curr. Opin. Biotechnol. 78, 102787 (2022).
Kalogriopoulos, N. A. et al. Synthetic GPCRs for programmable sensing and control of cell behaviour. Nature 637, 230–239 (2024).
An, L. et al. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 385, 276–282 (2024).
Lee, G. R. et al. Small-molecule binding and sensing with a designed protein family. bioRxiv. https://doi.org/10.1101/2023.11.01.565201 (2023). 1.
Bick, M. J. et al. Computational design of environmental sensors for the potent opioid fentanyl. Elife 6, e28909 (2017). 1.
Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).
Marchand, A. et al. Targeting protein–ligand neosurfaces with a generalizable deep learning tool. Nature 1, 10 (2025).
Glasgow, A. et al. Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor. Nat. Commun. 14, 1179 (2023).
Shover, C. L., Falasinnu, T. O., Freedman, R. B. & Humphreys, K. Emerging characteristics of isotonitazene-involved overdose deaths: a case-control study: a case-control study. J. Addict. Med. 15, 429–431 (2021).
Taoussi, O. et al. Human metabolism of four synthetic benzimidazole opioids: isotonitazene, metonitazene, etodesnitazene, and metodesnitazene. Arch. Toxicol. 98, 2101–2116 (2024).
Kanamori, T. et al. Metabolism of highly potent synthetic opioid nitazene analogs: N-ethyl-N-(1-glucuronyloxyethyl) metabolite formation and degradation to N-desethyl metabolites during enzymatic hydrolysis. Drug Test. Anal. https://doi.org/10.1002/dta.3705 (2024). 1.
Walton, S. E., Krotulski, A. J. & Logan, B. K. A forward-thinking approach to addressing the new synthetic opioid 2-benzylbenzimidazole nitazene analogs by liquid chromatography-tandem quadrupole mass spectrometry (LC-QQQ-MS). J. Anal. Toxicol. 46, 221–231 (2022).
Maguire, J. B. et al. Perturbing the energy landscape for improved packing during computational protein design. Proteins 89, 436–449 (2021).
Robertson, N. R. et al. PYR1 biosensor-driven genome-wide CRISPR screens for improved monoterpenoid production in Kluyveromyces marxianus. bioRxiv 11, 623641 (2024).
Park, S.-Y. et al. Abscisic acid inhibits type 2C protein phosphatases via the PYR/PYL family of START proteins. Science 324, 1068–1071. https://doi.org/10.1126/science.1173041 (2009). 1.
Vaidya, A. S. et al. Click-to-lead design of a picomolar ABA receptor antagonist with potent activity in vivo. Proc. Natl. Acad. Sci. USA 118, e2108281118 (2021). 1.
Daffern, N., Francino-Urdaniz, I. M., Baumer, Z. T. & Whitehead, T. A. Standardizing cassette-based deep mutagenesis by Golden Gate assembly. Biotechnol. Bioeng. 121, 281–290 (2024).
Daffern, N. et al. GMMA can stabilize proteins across different functional constraints. J. Mol. Biol. 436, 168586 (2024).
Dixon, A. S. et al. NanoLuc complementation reporter optimized for accurate measurement of protein interactions in cells. ACS Chem. Biol. 11, 400–408 (2016).
Gagnot, G. et al. Core-modified coelenterazine luciferin analogues: synthesis and chemiluminescence properties. Chemistry 27, 2112–2123 (2021).
De Vrieze, L. M., Stove, C. P. & Vandeputte, M. M. Nitazene test strips: a laboratory evaluation. Harm. Reduct. J. 21, 159 (2024).
Dauparas, J. et al. Atomic context-conditioned protein sequence design using LigandMPNN. Nat. Methods 22, 717–723 (2025).
Kozel, T. R. & Burnham-Marusich, A. R. Point-of-care testing for infectious diseases: past, present, and future. J. Clin. Microbiol. 55, 2313–2320 (2017).
Ni, Y. et al. A plug-and-play platform of ratiometric bioluminescent sensors for homogeneous immunoassays. Nat. Commun. 12, 4586 (2021).
Socha, R. D. & Tokuriki, N. Modulating protein stability—directed evolution strategies for improved protein function. FEBS J. 280, 5582–5595 (2013).
Torres, S. V. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).
Stove, C. New synthetic opioids: advances of receptor assays as tools for pharmacological characterization and analytical screening. Ann. Toxicol. Anal. 36, S18–S19 (2024).
Tian, H. et al. Unusually Broad-spectrum small-molecule sensing using a single protein scaffold. PNAS 122, e2519924122 (2025). 1.
Santiago, J. et al. Modulation of drought resistance by the abscisic acid receptor PYL5 through inhibition of clade A PP2Cs. Plant J. 60, 575–588 (2009).
Dupeux, F. et al. A thermodynamic switch modulates abscisic acid receptor sensitivity. EMBO J. 30, 4171–4184 (2011).
Santiago, J. et al. Structural insights into PYR/PYL/RCAR ABA receptors and PP2Cs. Plant Sci. 182, 3–11 (2012).
Hao, Q. et al. The molecular basis of ABA-independent inhibition of PP2Cs by a subclass of PYL proteins. Mol. Cell 42, 662–672 (2011).
Guo, A. B., Akpinaroglu, D., Kelly, M. J. S. & Kortemme, T. Deep learning guided design of dynamic proteins. Science 388, eadr7094 (2025). 1.
Ferrari, Á. J. R. et al. Large-scale discovery, analysis, and design of protein energy landscapes. bioRxivorg. https://doi.org/10.1101/2025.03.20.644235 (2025). 1.
Kim, S. et al. PubChem 2025 update. Nucleic Acids Res. https://doi.org/10.1093/nar/gkae1059 (2024). 1.
Okumura, H., Gallicchio, E. & Levy, R. M. Conformational populations of ligand-sized molecules by replica exchange molecular dynamics and temperature reweighting. J. Comput. Chem. 31, 1357–1367 (2010).
Leonard, A. C. et al. Rationalizing diverse binding mechanisms to the same protein fold: Insights for ligand recognition and biosensor design. ACS Chem. Biol. 19, 1757–1772 (2024).
Dupeux, F. et al. Modulation of abscisic acid signaling in vivo by an engineered receptor-insensitive protein phosphatase type 2C allele. Plant Physiol. 156, 106–116 (2011).
Beltrán, J. et al. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat. Biotechnol. 40, 1855–1861 (2022).
Daffern, N., Francino-Urdaniz, I., Baumer, Z. T. & Whitehead, T. A. Benchmarking cassette-based deep mutagenesis by Golden Gate assembly. bioRxiv 04, 536781 (2023).
Medina-Cucurella, A. V. & Whitehead, T. A. Characterizing protein-protein interactions using deep sequencing coupled to yeast surface display. Methods Mol. Biol. 1764, 101–121 (2018).
Wrenbeck, E. E. et al. Plasmid-based one-pot saturation mutagenesis. Nat. Methods 13, 928–930 (2016). 1.
Mighell, T. L., Toledano, I. & Lehner, B. SUNi mutagenesis: scalable and uniform nicking for efficient generation of variant libraries. PLoS ONE 18, e0288158 (2023).
Lee, M. E., DeLoache, W. C., Cervantes, B. & Dueber, J. E. A highly characterized yeast toolkit for modular, multipart assembly. ACS Synth. Biol. 4, 975–986 (2015).
Steiner, P. J., Bedewitz, M. A., Medina-Cucurella, A. V., Cutler, S. R. & Whitehead, T. A. A yeast surface display platform for plant hormone receptors: toward directed evolution of new biosensors. AIChE J. 66, e16767 (2020). 1.
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
Edelhoch, H. Spectroscopic determination of tryptophan and tyrosine in proteins. Biochemistry 6, 1948–1954 (1967).
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
Leonard, A. C. et al. Computational design of dynamic biosensors for emerging synthetic opioids. https://doi.org/10.5281/zenodo.15298584 (2025). 1.
Leonard, A. C. et al. Computational design of dynamic biosensors for emerging synthetic opioids, Leonard_ComputationalDesign_2025_Supplemental. https://doi.org/10.5281/zenodo.17885439 (2025).
Acknowledgements
We would like to thank Yves Janin for kindly providing the luciferase prosubstrate, Hikarazine-108. ACL would like to thank the Interdisciplinary Quantitative Biology and Molecular Biophysics programs at the University of Colorado Boulder for ongoing support. TAW would like to thank M. Stammnitz for helpful discussions related to transduction mechanisms for ligand-dependent protein biosensors. Funding for this work was supported by: National Science Foundation NSF Award #2128287 (T.A.W.); National Science Foundation NSF Award #2128016 (S.R.C. and I.W.); National Science Foundation NRT Integrated Data Science Fellowship Award #2022138 (L.M.W.); NSF GRFP Award #1650115 (A.C.L.); NSF GRFP Award #2040434 (Z.T.B.); DARPA CERES Award#D24AC00011-05 (S.R.C., I.W., and T.A.W.); NIH award# R01GM123296 (L.M.W. and A.J.F.); NIH award #5T32GM145437 (L.M.W.); DOE GAANN Awards # P200A210136 and P200A240099 (C.L.M.).
Author information
Author notes
These authors contributed equally: Alison C. Leonard, Chase Lenert-Mondou, Rachel Chayer, Samuel Swift.
Authors and Affiliations
Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
Alison C. Leonard, Rachel Chayer, Samuel Swift, Zachary T. Baumer, Ryan Delaney, Anika J. Friedman, Jordan Wells, Lindsey M. Whitmore, Michael R. Shirts & Timothy A. Whitehead 1.
Department of Biochemistry and Molecular Biology, University of California, Riverside, CA, USA
Chase Lenert-Mondou 1.
Department of Bioengineering, University of California, Riverside, CA, USA
Nicholas R. Robertson & Norman Seder 1.
Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
Sean R. Cutler 1.
Department of Chemical and Environmental Engineering, University of California, Riverside, CA, USA
Ian Wheeldon 1.
Center for Industrial Biotechnology, University of California, Riverside, CA, USA
Ian Wheeldon
Authors
- Alison C. Leonard
- Chase Lenert-Mondou
- Rachel Chayer
- Samuel Swift
- Zachary T. Baumer
- Ryan Delaney
- Anika J. Friedman
- Nicholas R. Robertson
- Norman Seder
- Jordan Wells
- Lindsey M. Whitmore
- Sean R. Cutler
- Michael R. Shirts
- Ian Wheeldon
- Timothy A. Whitehead
Contributions
Non co-1st author trainees are listed in alphabetical order. Conceptualization: A.C.L., C.L.M., N.R.R., I.W., and T.A.W. Methodology: A.C.L., C.L.M., R.C., S.S., R.D., A.J.F., M.R.S., I.W., and T.A.W. Investigation: A.C.L., C.L.M., R.C., S.S., Z.T.B., R.D., A.J.F., N.R.R., N.S., J.W., and L.M.W. Visualization: A.C.L., C.L.M., R.C., S.S., I.W., and A.W. Funding acquisition: A.C.L., Z.T.B., S.C., M.R.S., I.W., and T.A.W. Project administration: I.W. and T.A.W. Supervision: S.C., M.R.S., I.W., and T.A.W. Writing – original draft: A.C.L., C.L.M., R.C., S.S., I.W., and T.A.W. Writing – review & editing: S.C., M.R.S., I.W., and T.A.W.
Corresponding authors
Correspondence to Ian Wheeldon or Timothy A. Whitehead.
Ethics declarations
Competing interests
T.A.W., S.R.C., and I.W. have filed a provisional patent entitled REAGENTS AND SYSTEMS FOR GENERATING BIOSENSORS (US9738902B2; WO2011139798A2) covering some research in the present work. T.A.W. is a consultant for Inari Ag and serves on the scientific advisory board for Metaphore Biotechnologies and Alta Tech. S.R.C. and I.W. are cofounders of Living Sensors Inc., which has interests in sensing technologies. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Christophe Stove, who co-reviewed with Marthe M. Vandeputte, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Leonard, A.C., Lenert-Mondou, C., Chayer, R. et al. Computational design of dynamic biosensors for emerging synthetic opioids. Nat Commun (2026). https://doi.org/10.1038/s41467-025-67994-w
Received: 15 July 2025
Accepted: 15 December 2025
Published: 07 January 2026
DOI: https://doi.org/10.1038/s41467-025-67994-w