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

Abstract

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.

Diversity of ACE2 and its interaction with SARS-CoV-2 receptor binding domain

“…and Tian et.al. and found evidence of multimeric interactions, similar to our study(45). Since the RBD exists in close proximity to two other RBD subunits in the spike protein trimer, it is plausible that cooperative interactions between RBDs exist during ACE2 interaction events.…”

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus

“…However, in numerical analysis, the sensing region RI is considered as, , where the RI of PBS (pH 7.4) is 1.3348 and is fluctuating due to the different concentrations of ligand-analyte interaction onto the sensing surface. The adaptive interaction distribution algorithm (AIDA) algorithm has been used to solve the following reference [ 76 , 77 , 78 ]. To realize the interaction mechanism of the SARS-CoV-2 spike RBD, a rate constant distribution (RCD) algorithm with AIDA was established in reference [ 76 , 77 ].…”

“…The adaptive interaction distribution algorithm (AIDA) algorithm has been used to solve the following reference [ 76 , 77 , 78 ]. To realize the interaction mechanism of the SARS-CoV-2 spike RBD, a rate constant distribution (RCD) algorithm with AIDA was established in reference [ 76 , 77 ]. The real experimental interaction mechanism of SARS-CoV-2 spike RBD with human ACE2 is the same or almost similar to the algorithm [ 76 ].…”

Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features

“…Evidently, there are considerations to be taken about how to classify kinetic data [ 22 , 23 ] in many cases of biological interest, e.g., in antibody interactions, complex formation steady-state is not reached [ 22 ]; to distinguish between binding sites; and that analyzing interaction data from biosensor instruments is based on the simplified assumption that larger biomolecules interactions are homogeneous [ 23 ]. Also, that for the CoMPARA challenge [ 6 ], the organizers (EPA) used the thresholds determined in the CERAPP project and applied them to AR concentration-response values (AC 50 ) from the literature, using the following scheme among several possible: Strong: Activity concentration < 0.09 μM Moderate: Activity concentration 0.09–0.18 μM Weak: Activity concentration 0.18–20 μM Very Weak: Activity concentration 20–800 μM Inactive: Activity concentration > 800 μM …”