Advanced Molecular Modelling Techniques for Designing High-Sensitivity Biosensors

Wearable sensors for biomarker monitoring hold significant potential for chronic health management, particularly in non-invasive, frequent measurement settings such as sweat-based diagnostics. Among these, non-faradaic electro-chemical sensors are promising but face challenges in material stability and reliability under continuous exposure to body fluids. This study focuses on enhancing sensor performance by identifying proteins with high binding affinity for estradiol using advanced computational methods. The multi-stage approach included molecular docking with GNINA, followed by molecular dynamics (MD) simulations and alchemical free binding energy calculations using the GROMACS Bennett Acceptance Ratio (BAR) method. We identified three proteins (PDB IDs: 6FS4, 2COB, 6FS5) with high binding affinity for estradiol, with 6FS4 demonstrating the most stable interaction. These findings provide a strong foundation for further experimental validation and potential applications in non-invasive biomarker sensing.