Translating Bulk Measures of Capsid Assembly Progress into Insights on Fine-Scale Kinetics and Pathways

Abstract

Virus capsid assembly has become a key model system for studies of complicated self-assembly processes, attracting considerable interest from the biophysical modeling community. Simulation methods have proven valuable for gaining insight into the space of possible kinetics and mechanisms of capsid assembly, but they have so far been able to say little about the assembly kinetics of any specific virus. It is not currently possible to directly measure the detailed interaction rates needed to parameterize a model and there is only a limited amount of experimental evidence of assembly kinetics available to constrain possible pathways, almost all of it gathered from in vitro studies of purified coat proteins. We have developed methods to address this problem that use data fitting algorithms to learn rate parameters consistent with both structure-based rule sets and experimental light scattering data on bulk assembly progress in vitro. Our method combines ideas from gradient-based and response-surface local optimization methods with a heuristic global search strategy to find parameter fits that can approximately reproduce experimental measures of assembly progress. We have applied these methods to data from three capsid systems - human papillomavirus (HPV), hepatitis B virus (HBV), and cowpea chlorotic mottle virus (CCMV) - with the resulting fits suggesting three very different assembly mechanisms. Work is continuing to refine the learned rate parameters and pathways and explore how these mechanisms might change when computationally translated into more realistic representations of the assembly environment in vivo in order to more accurately model the assembly of viral capsids in living cells.