Reliable Strategy for Analysis of Complex Biosensor Data

When using biosensors, analyte biomolecules of several different concentrations are percolated over a chip with immobilized ligand molecules that form complexes with analytes. However, in many cases of biological interest, e.g., in antibody interactions, complex formation steady-state is not reached. The data measured are so-called sensorgram, one for each analyte concentration, with total complex concentration vs time. Here we present a new four-step strategy for more reliable processing of this complex kinetic binding data and compare it with the standard global fitting procedure. In our strategy, we first calculate a dissociation graph to reveal if there are any heterogeneous interactions. Thereafter, a new numerical algorithm, AIDA, is used to get the number of different complex formation reactions for each analyte concentration level. This information is then used to estimate the corresponding complex formation rate constants by fitting to the measured sensorgram one by one. Finally, all estimated rate constants are plotted and clustered, where each cluster represents a complex formation. Synthetic and experimental data obtained from three different QCM biosensor experimental systems having fast (close to steady-state), moderate, and slow kinetics (far from steady-state) were evaluated using the four-step strategy and standard global fitting. The new strategy allowed us to more reliably estimate the number of different complex formations, especially for cases of complex and slow dissociation kinetics. Moreover, the new strategy proved to be more robust as it enables one to handle system drift, i.e., data from biosensor chips that deteriorate over time.