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

Area of Science:

• Biophysics
• Biochemistry
• Molecular Interactions

Background:

• Traditional biosensor analysis assumes homogeneous interactions for biomolecules.
• Human receptor angiotensin-converting enzyme 2 (ACE2) is crucial for SARS-CoV-2 entry.
• Previous studies used surface plasmon resonance and bio-layer interferometry for ACE2-SARS-CoV-2 binding.

Purpose of the Study:

• To reanalyze biosensor data for ACE2-SARS-CoV-2 interactions using a novel algorithm.
• To challenge the simplified homogeneous interaction model in biomolecular binding studies.
• To provide a more accurate understanding of the binding kinetics and affinity.

Main Methods:

• Utilized an advanced four-step approach based on an adaptive interaction distribution algorithm (AIDA).
• AIDA accounts for the complexity of larger biomolecules, providing a 2D distribution of rate constants.
• Reanalyzed existing surface plasmon resonance and bio-layer interferometry data sets.

Main Results:

• The traditional assumption of a single interaction was found to be erroneous for ACE2-SARS-CoV-2 binding.
• AIDA revealed a complex, non-homogeneous interaction landscape.
• In one case, the calculated affinity constant (KD) differed by over 300% from the reported value.

Conclusions:

• Advanced algorithms like AIDA are necessary for accurate analysis of complex biomolecular interactions.
• The findings highlight limitations of traditional homogeneous models in biosensor data interpretation.
• Accurate mechanistic insights into ACE2-SARS-CoV-2 interactions can be gained through sophisticated analysis methods.