Detection of enzyme biomarkers originating from either bio-fluids or contaminating microorganisms is of utmost importance in clinical diagnostics and food safety. Herein, we present a simple, low-cost and easy-to-use sensing approach based on the switchable peroxidase-mimicking activity of plasmonic gold nanoparticles (AuNPs) that can catalyse for the oxidation of 3, 3o, 5o5-tetramethylbenzidine (TMB) for the determination of protease enzyme. The AuNP surface is modified with casein, showing dual functionalities. The first function of the coating molecule is to suppress the intrinsic peroxidase-mimicking activity of AuNPs by up to 77.1%, due to surface shielding effects. Secondly, casein also functions as recognition sites for the enzyme biomarker. In the presence of protease, the enzyme binds to and catalyses the degradation of the coating layer on the AuNP surface, resulting in the recovery of peroxidase-mimicking activity. This is shown visually in the development of a blue colored product (oxidised TMB) or spectroscopically as an increase in absorbance at 370 and 650 nm. This mechanism allows for the detection of protease at 44 ngdmL-1 in 90 min. The nanosensor circumvents issues associated with current methods of detection in terms of ease of use, compatibility with point-of-care testing, low-cost production and short analysis time. The sensing approach has also been applied for the detection of protease spiked in ultra-heat treated (UHT) milk and synthetic human urine samples at a limit of detection of 490 and 176 ngdmL-1, respectively, showing great potential in clinical diagnostics, food safety and quality control.
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The author C. M. and N. L. thank the PhD studentship support from the Department of Employment and Learning for Northern Ireland (DEL); C. C. thanks the strong support from the Central Research Support Funds of Queen's University Belfast via a start-up grant, the support from the Agri-Food Quest Competence Centre R&D funding programme sponsored by Invest Northern Ireland Agency (Invest NI), and the support from the Queen's University of Belfast AMR Network (QUBAN) sponsored by the UK's Engineering and Physical Sciences Research Council (EPSRC).
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