Advanced Molecular Modelling Techniques for Designing High-Sensitivity Biosensors.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Materials Science

Background:

• Wearable sensors offer potential for chronic disease management through non-invasive biomarker monitoring.
• Non-faradaic electrochemical sensors in sweat diagnostics face challenges with material stability and reliability.

Purpose of the Study:

• To enhance wearable sensor performance by identifying proteins with high estradiol binding affinity.
• To address material stability issues in continuous body fluid exposure for biosensing applications.

Main Methods:

• Utilized computational approaches including molecular docking (GNINA) and molecular dynamics (MD) simulations.
• Employed alchemical free binding energy calculations via the GROMACS Bennett Acceptance Ratio (BAR) method.
• Screened protein candidates for estradiol binding affinity.

Main Results:

• Identified three proteins (PDB IDs: 6FS4, 2COB, 6FS5) exhibiting high binding affinity for estradiol.
• Protein 6FS4 demonstrated the most stable interaction with estradiol among the candidates.
• Computational analysis provided insights into protein-ligand interactions for sensor development.

Conclusions:

• The identified proteins, particularly 6FS4, offer a promising foundation for developing stable and reliable wearable biosensors.
• These findings support further experimental validation for advanced non-invasive biomarker sensing.
• The study contributes to the advancement of electrochemical sensor technology for chronic health management.