Reliable Strategy for Analysis of Complex Biosensor Data.

Area of Science:

• Biomolecular Interaction Analysis
• Biosensor Technology
• Quantitative Kinetics

Background:

• Biosensors analyze biomolecular interactions using sensorgrams, but steady-state complex formation is often not reached.
• Standard global fitting procedures can be unreliable for complex kinetic binding data, especially with antibody interactions.

Purpose of the Study:

• To present and validate a new four-step strategy for reliable processing of complex kinetic binding data from biosensors.
• To compare the new strategy against standard global fitting for accuracy and robustness.

Main Methods:

• A four-step strategy involving dissociation graph calculation, a numerical algorithm (AIDA) for reaction identification, individual sensorgram fitting, and rate constant clustering.
• Evaluation using synthetic and experimental data from QCM biosensor systems with varying kinetics (fast, moderate, slow).

Main Results:

• The new strategy reliably estimates the number of complex formations, particularly for complex and slow dissociation kinetics.
• The strategy demonstrates robustness in handling system drift and deteriorating biosensor chip data.
• Improved accuracy in estimating complex formation rate constants compared to standard global fitting.

Conclusions:

• The proposed four-step strategy offers a more reliable and robust method for analyzing complex biosensor kinetic binding data.
• This approach enhances the understanding of biomolecular interactions, especially when steady-state is not achieved.