Biosensor-Enabled Deconvolution of the Avidity-Induced Affinity Enhancement for the SARS-CoV-2 Spike Protein and ACE2 Interaction.

Avidity is an effective and frequent phenomenon employed by nature to achieve extremely high-affinity interactions. As more drug discovery efforts aim to disrupt protein-protein interactions, it is becoming increasingly common to encounter systems that utilize avidity effects and to study these systems using surface-based technologies, such as surface plasmon resonance (SPR) or biolayer interferometry. However, heterogeneity introduced from multivalent binding interactions complicates the analysis of the resulting sensorgram. A frequently applied practice is to fit the data based on a 1:1 binding model, and if the fit does not describe the data adequately, then the experimental setup is changed to favor a 1:1 binding interaction. This reductionistic approach is informative but not always biologically relevant. Therefore, we aimed to develop an SPR-based assay that would reduce the heterogeneity to enable the determination of the kinetic rate constants for multivalent binding interactions using the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human receptor angiotensin-converting enzyme 2 (ACE2) as a model system. We employed a combinatorial approach to generate a sensor surface that could distinguish between monovalent and multivalent interactions. Using advanced data analysis algorithms to analyze the resulting sensorgrams, we found that controlling the surface heterogeneity enabled the deconvolution of the avidity-induced affinity enhancement for the SARS-CoV-2 spike protein and ACE2 interaction.

Advanced Analysis of Biosensor Data for SARS-CoV-2 RBD and ACE2 Interactions.

The traditional approach for analyzing interaction data from biosensors instruments is based on the simplified assumption that also larger biomolecules interactions are homogeneous. It was recently reported that the human receptor angiotensin-converting enzyme 2 (ACE2) plays a key role for capturing SARS-CoV-2 into the human target body, and binding studies were performed using biosensors techniques based on surface plasmon resonance and bio-layer interferometry. The published affinity constants for the interactions, derived using the traditional approach, described a single interaction between ACE2 and the SARS-CoV-2 receptor binding domain (RBD). We reanalyzed these data sets using our advanced four-step approach based on an adaptive interaction distribution algorithm (AIDA) that accounts for the great complexity of larger biomolecules and gives a two-dimensional distribution of association and dissociation rate constants. Our results showed that in both cases the standard assumption about a single interaction was erroneous, and in one of the cases, the value of the affinity constant KD differed more than 300% between the reported value and our calculation. This information can prove very useful in providing mechanistic information and insights about the mechanism of interactions between ACE2 and SARS-CoV-2 RBD or similar systems.