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

Abstract

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.