Molecular Dynamics Model for Developing Wearable Biosensors.

Area of Science:

• Biomolecular modeling and wearable biosensor development.

Background:

• Eccrine sweat is a rich source of biomarkers, driving demand for wearable sensors.
• Detecting trace analytes like glucose in sweat requires highly specific and sensitive biosensors.
• Computational models are needed to optimize biosensor design by analyzing environmental factors.

Purpose of the Study:

• To develop a comprehensive molecular model for eccrine sweat biosensors.
• To investigate the influence of solvents, interfering species, and temperature on biosensor performance.
• To assess the impact of bioreceptor immobilization on gold substrates and its effect on binding affinity.

Main Methods:

• A molecular model was created incorporating bioreceptors, target ligands, and a gold substrate in an eccrine sweat simulant.
• The model included protein dynamics and explicit solvent calculations.
• The effects of bioreceptor immobilization on tertiary structure and binding site conformation were analyzed.

Main Results:

• The computational model successfully simulated biomolecular interactions within eccrine sweat.
• It demonstrated the ability to assess the impact of substrate immobilization on bioreceptor structure and binding affinity.
• The model provides insights into optimizing sensor specificity and sensitivity for analytes like glucose.

Conclusions:

• The developed molecular model is a valuable tool for designing and refining wearable sweat biosensors.
• It bridges the gap between computational simulations and experimental sensor development.
• This approach facilitates the creation of advanced wearable devices for real-time analyte monitoring, exemplified by glucose detection.