Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights

Abstract

Graphene-based field-effect transistors (GFETs) are rapidly gaining recognition as powerful tools for biochemical analysis due to their exceptional sensitivity and specificity. In this study, we utilize a GFET system to explore the peroxidase-based biocatalytic behavior of horseradish peroxidase (HRP) and the heme molecule, the latter serving as the core component responsible for HRP’s enzymatic activity. Our primary objective is to evaluate the effectiveness of GFETs in analyzing the peroxidase activity of these compounds. We highlight the superior sensitivity of graphene-based FETs in detecting subtle variations in enzyme activity, which is critical for accurate biochemical analysis. Using the transconductance measurement system of GFETs, we investigate the mechanisms of enzymatic reactions, focusing on suicide inactivation in HRP and heme bleaching under two distinct scenarios. In the first scenario, we investigate the inactivation of HRP in the presence of hydrogen peroxide and ascorbic acid as cosubstrate. In the second scenario, we explore the bleaching of the heme molecule under conditions of hydrogen peroxide exposure, without the addition of any cosubstrate. Our findings demonstrate that this advanced technique enables precise monitoring and comprehensive analysis of these enzymatic processes. Additionally, we employed a machine learning algorithm based on a multilayer perceptron deep learning architecture to detect the enzyme parameters under various chemical and environmental conditions. Integrating machine learning and probabilistic methods significantly enhances the accuracy of enzyme behavior predictions.