Python-driven impedance profiling on peptide-functionalized biosensor for detection of HIV gp41 envelope protein

Area of Science:

• Biomedical Engineering
• Biosensor Technology
• Diagnostic Development

Background:

• Early detection of HIV is crucial for effective management and treatment.
• Existing diagnostic methods can be time-consuming or require complex sample preparation.
• Novel biosensor platforms are needed for rapid and sensitive HIV detection.

Purpose of the Study:

• To develop and characterize the first label-free impedimetric biosensor for HIV envelope protein gp41 detection.
• To utilize antimicrobial peptides (AMPs) as specific biorecognition elements.
• To employ a deep learning algorithm for enhanced data analysis.

Main Methods:

• Fabrication of a biosensor interface using thiolated polystyrene and gold nanospheres.
• Electrochemical impedance spectroscopy (EIS) for monitoring electrode modification.
• Scanning electron microscopy (SEM) for surface morphology validation.
• Application of a Python-based deep learning algorithm for impedance data analysis.
• Chronoimpedimetric (CI) detection for binding kinetics.

Main Results:

• High sensitivity achieved with a linear detection range of 5-600 pg/mL.
• Low limit of detection (LOD) of 1.62 pg/mL and limit of quantification (LOQ) of 4.91 pg/mL.
• Rapid gp41 binding detection within 350 s.
• Excellent reproducibility (CV = 0.22%), good selectivity in spiked serum (<12% variation), and robust stability.
• High regression coefficient (R² = 0.9946) indicating reliable performance.

Conclusions:

• The developed label-free impedimetric biosensor is a promising tool for early HIV detection.
• The biosensor demonstrates high sensitivity, specificity, reproducibility, and stability.
• This platform offers potential for rapid and reliable point-of-care HIV diagnostics.