A parameter estimation technique for stochastic self-assembly systems and its application to human papillomavirus...

Area of Science:

• Biophysics
• Computational Biology
• Virology

Background:

• Virus capsid assembly is a complex self-assembly process, crucial for understanding viral replication.
• Accurate determination of kinetic parameters in viral assembly is challenging due to system size and rapid dynamics.
• Existing experimental methods struggle to directly measure coat protein binding rates.

Purpose of the Study:

• To develop a computational strategy for deducing coat-coat binding rate parameters in viral capsid assembly.
• To overcome limitations in directly measuring kinetic parameters for large, rapidly assembling viral systems.
• To provide a method for gaining insights into viral assembly mechanisms.

Main Methods:

• Developed a computational strategy combining quadratic response surface and quasi-gradient descent approximations.
• Fitted stochastic simulation trajectories to experimental measures of assembly progress.
• Applied the method to in vitro assembly data for human papillomavirus (HPV).

Main Results:

• The computational method successfully deduced coat-coat binding rate parameters for viral capsid assembly.
• The derived parameters produced accurate curve fits for experimental assembly progress data.
• Results showed good concordance with prior analyses, validating the approach.

Conclusions:

• The developed computational strategy effectively determines kinetic parameters for viral capsid assembly.
• The method provides insights into in vitro assembly mechanisms and a basis for comparing in vitro and in vivo assembly.
• This work advances the modeling of complex biological self-assembly systems.