Integrating machine learning and electrochemistry to develop a glucose biosensor assembled with Ganoderma applanatum...

Fungal lectins have enormous biotechnological potential, but limited knowledge about their biochemical and biophysical features prevents their proper use. Herein, we report an innovative alternative to use Ganoderma applanatum lectin (GAL) as a glucose biorecognition element, after identifying the ideal electroanalytical conditions by machine learning studies performed with a homologous agglutinin from the same macrofungus. The research revealed that GAL has moderate resistance to pH (4-8) and temperature (20-60 °C) variations, but its hemagglutinating activity (376.5 HU mg^-1 GAL at 20 °C) was better conserved under physiological conditions. Integrating electrochemical data and semi-empirical molecular modeling, biocompatible and electrostatically favorable conditions were found to immobilize the lectin on Prussian blue-modified glassy carbon electrode, after thermal activation of the metal-complex film. The glucose dose-response relationship obtained with the developed biosensor, defined as GAL/ta-PB/GCE, showed a typical Hill equation correlation, suggesting electrodic interactions represented by a sigmoidal mathematical function. GAL/ta-PB/GCE achieved remarkable electroanalytical performance, with emphasis on the detection limit (10.2 pM) and sensitivity (0.012 µA µM^-1cm^-2). The biosensor was successfully used to quantify glucose in pharmaceutical formulations, reiterating that the association of theoretical and experimental information drives important advances in bioelectrochemical studies.