Glycoprotein assay based on the optimized immittance signal of a redox tagged and lectin-based receptive interface

Abstract

Glycoproteins play important roles in biological systems such as in process related to cell binding, signaling and disease. Consequently, novel, potentially quantitative, and rapid electroanalytical approaches capable of detecting protein binding are welcome. Herein, we introduce a methodology that is both fast and sensitive, and capable of quantification of the binding affinity in glycoprotein-lectin molecular models. The proposed methodology is based on the electrochemical impedance spectroscopy technique focused on the immittance function approach, wherein a library of analytical parameters can be computed from the raw impedance data obtained, and automatically processed in a label-free, quantifiable and very sensitive assay platform. This approach also avoids redox probe pre-doping of the analytical sample. Avoiding redox pre-doping of the analytical sample is achievable designing an appropriate redox-tagging monolayer containing lectin interface (a carbohydrate binding protein, herein ArtinM) as the bio-receptor, endowing high sensitivity of electrochemical signal when specifically detecting glycoproteins of interest (presently horseradish peroxidase, HRP, a mannose glycoprotein) as the biochemical target for ArtinM. The electroanalytical curves demonstrated that the binding affinity constant could be evaluated as equivalent for all library (immittance function) parameters, allowing optimized single frequency (or a range of frequencies) assessment with high sensitivity. In other words, binding affinity constants between ArtinM and HRP for each of the parameters in the immittance function library at given optimized frequencies were similar, independently of the parameter. Thus, the feasibility of using this immittance function approach for electroanalytical glycoarrays by accessing bio-recognition processes on a rapid (optimized) single frequency and highly multiplexable platform was demonstrated. (C) 2016 Elsevier B.V. All rights reserved.