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Combining Excellent Selectivity with Broad Target Scope: Biosensing with Arrayed Deep Cavitand Hosts.

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    Deep cavitands offer a versatile biosensing platform, enabling selective detection of various biomolecules like post-translational modifications and DNA structures. This advanced chemical sensing rivals biological tools, expanding applications in complex biological media.

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    Area of Science:

    • Supramolecular chemistry
    • Chemical biology
    • Analytical chemistry

    Background:

    • Macrocyclic hosts like cucurbiturils and calixarenes are used in biosensing but face selectivity-scope trade-offs.
    • Indicator displacement assays with these hosts can lack selectivity for similar targets.
    • Water-soluble, self-folding deep cavitands offer a solution to overcome these limitations.

    Purpose of the Study:

    • To develop a versatile and selective biosensing platform using deep cavitands.
    • To demonstrate the ability of deep cavitands to recognize a broad range of biorelevant species.
    • To explore the application of deep cavitands in enzyme assays and oligonucleotide structure sensing.

    Main Methods:

    • Utilizing water-soluble, self-folding deep cavitands as host molecules.
    • Functionalizing cavitands at upper and lower rims for tailored recognition.
    • Employing arrayed formats with multiple hosts and fluorescent dyes for pattern recognition.
    • Exploiting both cavity-based (e.g., NMe3+) and non-cavity-based interactions.
    • Integrating machine learning algorithms for data analysis and classification.

    Main Results:

    • Achieved site-selective recognition of post-translational lysine methylations.
    • Demonstrated selective sensing of serine phosphorylation, lysine acetylation, and arginine citrullination.
    • Successfully detected heavy metals, drugs of abuse, and protein isoforms.
    • Applied cavitands in supramolecular tandem assays for enzyme function analysis (kinases, phosphatases).
    • Enabled discrimination of oligonucleotide secondary structures, including G-quadruplexes with variations.
    • Developed a machine learning model for accurate prediction of DNA folding states.

    Conclusions:

    • Deep cavitands provide a powerful and tunable sensing platform for diverse biorelevant targets.
    • The system overcomes the selectivity-scope trade-off inherent in traditional macrocyclic hosts.
    • Multiple sensing mechanisms and array formats enable high selectivity in complex biological media.
    • This approach offers a simple yet effective alternative to biological recognition tools.