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Novel functional monomer for the electrochemical synthesis of highly affine epitope-imprinted polymers
Electroanalysis ; 2023.
Article in English | Scopus | ID: covidwho-2252494
ABSTRACT
To address the lack of functional monomer diversity for the electrosynthesis of protein-selective molecularly imprinted polymers (MIPs), we introduce a new concept able to lead to a new class of functional monomers. This is based on conjugating an electropolymerizable monomer unit (umbelliferone) to an amino acid for closer mimicking of protein-based natural affinity ligands such as antibodies. As the first representative of this class of monomers an aspartate-umbelliferone (Asp-UMB) conjugate was synthesized and here we provide the proof for its suitability to generate highly affine MIPs for proteins by epitope imprinting. As model we used a heptapeptide (GFNCYFP) stemming from the receptor binding domain (RBD) of the SARS-CoV-2 spike protein to generate epitope-imprinted polymers able to recognize the parent RBD protein. For rapid optimization and assessment of the binding kinetics we prepared MIP microarrays on surface plasmon resonance imaging (SPRi) chips. First the peptides were microspotted on the bare gold surface of the chips followed by the electropolymerization of Asp-UMB. This resulted in ca. 2 nm thick, highly uniform, and electrically insulating polymer film, well suited both for hierarchical epitope imprinting and SPRi read-out. Taking advantage also of the on-chip optimization enabled by the microarray format the increased functional diversity of the new monomer resulted in highly affine MIPs with equilibrium dissociation constants in the lower picomolar range. © 2023 The Authors. Electroanalysis published by Wiley-VCH GmbH.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Electroanalysis Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: Electroanalysis Year: 2023 Document Type: Article