Molecularly imprinted polymers (MIPs), as inexpensive and stable substitutes of antibodies, have shown great promise in immunoassays. Glycoproteins are of significant diagnostic value. To facilitate the application of MIPs in clinical diagnostics, a general and facile imprinting method toward glycoproteins oriented for an enzyme-linked immunosorbent assay (ELISA) in the form of a 96-well microplate is essential but has not been fully explored yet. In this study, a new method called boronate affinity-based oriented surface imprinting was proposed for facile preparation of glycoprotein-imprinted microplates. A template glycoprotein was first immobilized by a boronic acid-modified microplate through boronate affinity binding, and then, a thin layer of polyaniline was formed to cover the microplate surface via in-water self-copolymerization. After the template was removed by an acidic solution, 3D cavities that can rebind the template were fabricated on the microplate surface. Using horseradish peroxidase (HRP) as a model target, the effects of imprinting conditions as well as the properties and performance of the prepared MIPs were investigated. α-Fetoprotein (AFP)-imprinted microplate was then prepared, and thereby, a MIP-based ELISA method was established. The prepared MIPs exhibited several highly favorable features, including excellent specificity, widely applicable binding pH, superb tolerance for interference, high binding strength, fast equilibrium kinetics, and reusability. The MIP-based ELISA method was finally applied to the analysis of AFP in human serum. The result was in good agreement with that by radioimmunoassay, showing a promising prospect of the proposed method in clinical diagnostics.

Enzyme activity assay is an important method in clinical diagnostics. However, conventional enzyme activity assay suffers from apparent interference from the sample matrix. Herein, we present a new format of enzyme activity assay that can effectively eliminate the effects of the sample matrix. The key is a 96-well microplate modified with molecularly imprinted polymer (MIP) prepared according to a newly proposed method called boronate affinity-based oriented surface imprinting. Alkaline phosphatase (ALP), a glycoprotein enzyme that has been routinely used as an indicator for several diseases in clinical tests, was taken as a representative target enzyme. The prepared MIP exhibited strong affinity toward the template enzyme (with a dissociation constant of 10(-10) M) as well as superb tolerance for interference. Thus, the enzyme molecules in a complicated sample matrix could be specifically captured and cleaned up for enzyme activity assay, which eliminated the interference from the sample matrix. On the other hand, because the boronate affinity MIP could well retain the enzymatic activity of glycoprotein enzymes, the enzyme captured by the MIP was directly used for activity assay. Thus, additional assay time and possible enzyme or activity loss due to an enzyme release step required by other methods were avoided. Assay of ALP in human serum was successfully demonstrated, suggesting a promising prospect of the proposed method in real-world applications.

Based on the nanoconfinement effect, two new affinity materials that have a pH-responsive capture–release ability for proteins were developed.

Lectin array is an important tool in the fields of carbohydrate chemistry, glycobiology, and glycomics. Because natural lectins are associated with some apparent disadvantages such as tedious purification and easy loss of activity, artificial materials are applied to overcome such shortages by mimicking and replacing lectins in an artificial lectin array, among which boronate affinity-based materials are very outstanding and widely used. However, complicated synthetic works are often involved to design and create boronate affinity-based lectin-mimics. In this work, a facile and novel method was proposed to establish a virtual lectin array based on boronate affinity-based pH-featured encoding for discrimination of monosaccharides by pattern recognition. The dependence of boronate affinity on environmental pH was selected to encode each monosaccharide for feature generation, and the pH-featured encoding was used to construct the virtual lectin array. On the basis of the virtual array, pattern recognition algorithms were applied for data analysis. Monosaccharides were discriminated by principal component analysis, and the relations in the virtual lectin array were unraveled by cluster analysis. In this proof-of-concept work, without complicated synthesis or preparation, the proposed method was successful in mimicking lectin array and discriminating nine elementary monosaccharides found in nature, and it was also a new way of encoding in expanding the applications of boronate affinity-based materials and methods in the field of biomimetics.