Harnessing in Silico Design for Electrochemical Aptasensor Optimization: Detection of Okadaic Acid (OA)
Abstract
The urgent need for advanced analytical tools for environmental monitoring and food safety drives the development of novel biosensing approaches and solutions. A computationally driven workflow for the development of a rapid electrochemical aptasensor for okadaic acid (OA), a critical marine biotoxin, is reported. The core of this strategy is a rational design process, where in silico modeling was employed to optimize the biological recognition element. A 63-nucleotide aptamer was successfully truncated to a highly efficient 31-nucleotide variant. Molecular docking simulations confirmed the high binding affinity of the minimized aptamer and guided the design of the surface immobilization chemistry to ensure robust performance. The fabricated sensor, which utilizes a ferrocene-labeled aptamer, delivered a sensitive response with a detection limit of 2.5 nM (n = 5) over a linear range of 5-200 nM. A significant advantage for practical applications is the remarkably short assay time of 5 min. The sensor's applicability was successfully validated in complex food matrices, achieving excellent recovery rates of 82-103% in spiked mussel samples. This study establishes an integrated computational-experimental methodology that streamlines the development of high-performance biosensors for critical food safety and environmental monitoring challenges.
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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