key: cord-0881476-bu17gjgm
authors: Forssén, Patrik; Samuelsson, Jörgen; Lacki, Karol; Fornstedt, Torgny
title: Advanced Analysis of Biosensor Data for SARS-CoV-2 RBD and ACE2 Interactions
date: 2020-08-10
journal: Anal Chem
DOI: 10.1021/acs.analchem.0c02475
sha: d182eedb84ea1d7c8c9bf6890ac73a3488bead99
doc_id: 881476
cord_uid: bu17gjgm

[Image: see text] 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 K(D) 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.

Interactions that contribute very little to the total response in the studied time range cannot be resolved/detected. For example, if the studied association time is too short, an interaction with slow association and slow dissociation, i.e., slowly forming stable complexes, might be entirely missed in the presence of a fast forming but unstable complexes. Initially the fast forming unstable complexes will entirely dominate the contribution to the detected response but after some, potentially very long, association time the slowly forming stable complexes will instead dominate the contribution to the detected response, see below, Figure S3 : An example of a system with a fast and slow interaction. To the left a short injection, 100 seconds, and to the right a long injection, ~28 hours.

As can be seen during the short injection the fast interaction dominates the contribution to the total sensorgram response which makes the slow interaction hard to resolve/detect. But during the long injection the slow interaction instead dominates the contribution to the total sensorgram response as its' response continues to increase when the fast interaction has already reached its' steady state response.

Detecting slowly forming stable complexes in the presence of fast forming unstable complexes is very hard for practical reasons. To be able to do that one has to reach, or be very close, to steady state, i.e., flat line association phase sensorgram response. But if this takes hours, or even days, it is not practical both in terms of time and analyte required when, for example, using an SPR instrument. This might indeed lead to wrong conclusions about the interaction mechanism. For example, when one is being constantly exposed to a substance the most relevant interaction might be the one that forms the most stable complexes with a receptor. But in the presence of fast forming unstable complex reactions, these interactions are not detectable in ordinary biosensor experiments if they have slow association. 

Structure of the SARS-CoV-2 Spike Receptor-Binding Domain Bound to the ACE2 Receptor

Potent Binding of 2019 Novel Coronavirus Spike Protein by a SARS Coronavirus-Specific Human Monoclonal Antibody