Fluorescent discrimination for snake venom via a dual-mode supramolecular sensor array
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Han-Wen Tian
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Dong-Sheng Guo
Abstract
Precise snake venom identification is the prerequisite for clinical treatment, production of antiserum, basic research, and other applications. Unfortunately, it is still a challenging task which mainly originates from their extraordinary complicated protein composition. Differential sensing strategy was applied benefiting from its advantage in discrimination of complex mixtures. Herein, A dual-mode supramolecular fluorescent sensor array based on the heteromultivalent sensing strategy was developed. The heteromultivalent macrocyclic coassemblies with strong and pan-selective binding to proteins were employed to construct sensor units. Fluorescence intensity and anisotropy signals were integrated, which were expected to show multidimensional information of proteins, such as surface groups, protein structures, and molecular weights. Finally, a supramolecular sensor array with the ability of facile, rapid and general species-specific identification and taxonomic classification for snake venom was constructed. The sensor array also demonstrated its advantage in semiquantitative analysis and multi-level identification, suggesting its great potential for practical use.
Funding source: Fundamental Research Funds for the Central Universities
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 22101142
Acknowledgments
Financial supported by NSFC (22101142) and the Fundamental Research Funds for the Central Universities, which are gratefully acknowledged. The authors also thank Prof. Bart Jan Ravoo at Westfälische Wilhelms-Universität Münster for supplying the amphiphilic cyclodextrin used in this work.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: H. Tian and J.Tian conducted the experimental procedures, analyzed the data, and wrote the initial draft. X. Hu and D. Guo were responsible for the topic design and article revision. All authors have accepted responsibility for the entire content of this manuscript and approved its submission. The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: Financial supported by NSFC (22101142) and the Fundamental Research Funds for the Central Universities.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/pac-2024-0320).
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