Unmasking complex kinetics in viral entry by inferring hypoexponential models.

Area of Science:

• Biophysics
• Virology
• Computational Biology

Background:

• Single-event completion times offer insights into kinetic models but can oversimplify complex reaction pathways.
• Current methods like gamma distributions assume homogenous processes, limiting their ability to capture rate-limiting behaviors in complex systems.

Purpose of the Study:

• To introduce and validate hypoexponential analysis for estimating heterogeneous kinetic processes.
• To apply this novel method to understand the kinetics of SARS-CoV-2 entry.

Main Methods:

• Development and application of hypoexponential models to analyze single-event completion time data.
• Estimation of kinetic parameters, including rate constants separated by orders of magnitude.
• Analysis of SARS-CoV-2 entry kinetics in the presence and absence of the ACE2 receptor.

Main Results:

• Hypoexponential models successfully estimate rate constants for heterogeneous kinetic processes.
• SARS-CoV-2 entry exhibits heterogeneous kinetics, with rate constants differing by two to three orders of magnitude.
• The ACE2 receptor reduces the number of rate-limiting steps in SARS-CoV-2 entry but does not alter the intrinsic rates.

Conclusions:

• Hypoexponential analysis is a robust method for inferring complex kinetic models from single-event data.
• SARS-CoV-2 entry is proposed to be driven by a combination of ACE2-accelerated and ACE2-independent spike protein activation.
• Detecting heterogeneous kinetic processes is crucial for accurate mechanistic modeling of viral entry.