Discrimination of α-Thrombin and γ-Thrombin Using Aptamer-Functionalized Nanopore Sensing

Area of Science:

• Biotechnology
• Nanotechnology
• Biosensing

Background:

• Single-molecule protein detection is challenging.
• Solid-state nanopores offer sensitive, label-free biosensing.
• Distinguishing closely related proteins requires advanced methods.

Purpose of the Study:

• To discriminate between alpha-thrombin and gamma-thrombin using solid-state nanopore sensing.
• To improve protein discrimination through aptamer functionalization.
• To enhance detection accuracy using machine learning.

Main Methods:

• Utilized solid-state nanopore sensing for protein analysis.
• Functionalized nanopores with aptamers to enhance specificity.
• Applied machine learning algorithms (ensemble methods, seven features) for signal postprocessing.

Main Results:

• Aptamer functionalization significantly improved protein discrimination.
• Observed a notable difference in relative current blockade amplitude between proteins.
• Achieved 98.8% accuracy in distinguishing alpha-thrombin and gamma-thrombin using machine learning.

Conclusions:

• Solid-state nanopore sensing, enhanced by aptamer functionalization and machine learning, enables accurate discrimination of closely related proteins.
• This approach offers a sensitive and label-free method for protein identification at the single-molecule level.
• The study highlights the potential of integrated biosensor and machine learning strategies in biotechnology.