Applying Derivative-Free Optimization to Fit Kinetic Parameters of Viral Capsid Self-Assembly Models from Multi-Source Bulk in vitro Data

Abstract

Virus capsid assembly has been a powerful model system for biological self-assembly in general due to the combination of experimental tractability but complicated pathway space. Detailed experimental resolution of viral assembly processes, however, has so far proven impossible. Computational approaches have provided a solution, allowing us to learn models of assembly consistent with indirect experimental measures of bulk in vitro assembly and thus fill the gaps between coarse-grained experimental measurements and detailed theoretical models. Nonetheless, accurate simulation predictions rely on building accurate models, which has proven to be a challenging data-fitting problem due to the high computational cost of simulating capsid assembly trajectories, high stochastic noise inherent to the system, and limited and generally noisy experimental data available. Here, we describe progress in learning accurate kinetic models of capsid assembly systems by computationally fitting assembly simulations to experimental data. We previously developed a heuristic optimization approach to learn rate parameters of coat-coat interactions by minimizing the deviation between real and simulated static light scattering measurements. We now show that one can substantially improve fitting to light scattering data using an alternative class of methods called derivative-free optimization, designed to deal with challenges of costly, noisy computations. Simultaneously, simulated exploration of potential alternative sources of experimental data for monitoring bulk assembly (e.g, non-covalent mass spectrometry) suggest that other feasible technologies providing richer data on assembly progress can more precisely pin down true parameters and assembly pathways. Advancing such simulation-based data fitting methods provides a general technology for greatly enhancing our ability to learn fine-scale details of complex assembly processes from experimental data, a strategy with potential application to developing accurate quantitative models of numerous other assembly systems found through biology.