Blocking of the High-Affinity Interaction-Synapse Between SARS-CoV-2 Spike and Human ACE2 Proteins Likely Requires Multiple High-Affinity Antibodies: An Immune Perspective

The pandemic of Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 has induced global eagerness to develop vaccines and therapeutics for treating COVID-19, including neutralizing antibodies. To develop effective therapeutic antibodies against SARS-CoV-2, it is critical to understand the interaction between viral and host's proteins. The human ACE2 (hACE2) protein is the crucial target for the SARS-CoV's Spike protein that allows the virus to adhere to host epithelial cells. X-ray crystal structures and biophysical properties of protein-protein interactions reveal a large interaction surface with high binding-affinity between SARS-CoV-2 and hACE2 (18 interactions), at least 15-fold stronger than between SARS-CoV-1 and hACE2 (eight interactions). This suggests that antibodies against CoV-1 infection might not be very efficient against CoV-2. Furthermore, interspecies comparisons indicate that ACE2 proteins of man and cat are far closer than dog, ferret, mouse, and rat with significant differences in binding-affinity between Spike and ACE2 proteins. This strengthens the notion of productive SARS-CoV-2 transmission between felines and humans and that classical animal models are not optimally suited for evaluating therapeutic antibodies. The large interaction surface with strong affinity between SARS-CoV-2 and hACE2 (dG−12.4) poses a huge challenge to develop reliable antibody therapy that truly blocks SARS-CoV-2 adherence and infection. We gauge that single antibodies against single epitopes might not sufficiently interfere with the strong interaction-synapse between Spike and hACE2 proteins. Instead, appropriate combinations of high-affinity neutralizing antibodies against different epitopes might be needed, preferably of IgA-class for optimal and prolonged activity at epithelial layers of respiratory and intestine tracts.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.