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Published March 20, 2023 | Supplemental Material + Submitted
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Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids


To develop more sensitive fluorescent protein sensors for nicotine, we combined computational protein design, site-saturated, site-directed, and combinatorial mutagenesis with fluorescence assays, molecular dynamics simulations, and absorbance measurements. The data showed that the resulting molecules, iNicSnFR11 and iNicSnFR12, have higher sensitivity to nicotine than previously reported constructs. In the linear portion of the dose-response relation at sub-μM [nicotine] for iNicSnFR12, ∆F/F₀ increased with a proportionality constant (S-slope) of 2.6 μM⁻¹, representing a 6.5-fold higher sensitivity than iNicSnFR3a. Molecular dynamics calculations enabled identification of a binding pose for nicotine previously indeterminate from experimental data. Further comparative simulations based on this model revealed a tilt in helix 4 in the optimized sensor, likely altering allosteric networks involving the ligand binding site. The absorbance data showed that the fluorescence activation results from increased absorption rather than increased quantum yield for fluorescence. iNicSnFR12 resolved nicotine in diluted mouse and human serum at the peak concentration (100-200 nM) that occurs during smoking or vaping, but also at the decaying concentrations (< 100 nM) during the intervals between smoking or vaping sessions. NicSnFR12 was roughly as sensitive to varenicline or acetylcholine as to nicotine; the sensitivity to choline was at least one order of magnitude less. None of these drugs would markedly distort measurements in human biofluids such as sweat and interstitial fluid. Therefore, iNicSnFR12 is a promising candidate as the molecular sensor that could underlie a continuous nicotine monitor for human biofluids.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license. A.L.N. was supported by California Tobacco-Related Disease Research Program (TRDRP) Grant 27FT-0022. H.A.L. was supported by California TRDRP Grant 27IP-0057, National Institute on Drug Abuse Grant DA049140, and National Institute of General Medical Sciences (NIGMS) Grant GM123582. D.A.D. was supported by California TRDRP Grant T29IR0455. S. M. was supported by a grant from the Rosen Bioengineering Center at Caltech. H. A. L. and S. M. were supported by a grant from the Merkin Institute for Translational Research at Caltech. E. J. F. was supported by a Caltech Summer Undergraduate Research Fellowship from Paraskeva N. Danailov. MD simulations were performed using the computing facilities of Karolina Supercomputer and were supported by the Knut and Alice Wallenberg Foundation, the Swedish Research Council (2019-02433, 2021-05806), the Swedish e-Science Research Centre (SeRC), and the BioExcel Center of Excellence (EU-823830), with compute resources provided by the Swedish National Infrastructure for Computing (SNIC) and Euro-HPC (EHPC-REG-2021R0074). We thank Zoe Beatty, Kallol Bera, Nicholas Friesenhahn, Heather Lukas,and Anand Muthusamy for advice and guidance. We thank Purnima Deshpande for lab management. Data availability. The data that support the findings of this study are available from the corresponding author upon reasonable request. The raw MD simulation trajectories can be found in: 10.5281/zenodo.7529914 The authors have declared no competing interest.

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Submitted - 20230116-524298v1full.pdf

Submitted - 20230116-524298v2full.pdf

Supplemental Material - media-1.pdf


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