Uncertainty Quantified Computational Analysis of the Energetics of Virus Capsid Assembly

TL;DR

This paper introduces an uncertainty-aware statistical framework for analyzing virus capsid assembly pathways, accounting for conformational variability and all possible sub-assembly combinations to improve prediction accuracy.

Contribution

It presents a novel probabilistic approach that captures conformational uncertainties and models all sub-assembly interactions, advancing beyond static configuration assumptions.

Findings

Significant differences from static configuration-based predictions.

Uncertainty-aware methods provide more accurate pathway predictions.

Bayesian models effectively represent assembly transition dynamics.

Abstract

Most of the existing research in assembly pathway prediction/analysis of virus cap- sids makes the simplifying assumption that the configuration of the intermediate states can be extracted directly from the final configuration of the entire capsid. This assump- tion does not take into account the conformational changes of the constituent proteins as well as minor changes to the binding interfaces that continues throughout the assembly process until stabilization. This paper presents a statistical-ensemble based approach which provides sufficient samples of the configurational space for each monomer and the relative local orientation between monomers, to capture the uncertainties in their binding and conformations. Furthermore, instead of using larger capsomers (trimers, pentamers) as building blocks, we allow all possible sub-assemblies to bind in all pos- sible combinations. We represent this assembly graph in two different ways. First, we use the Wilcoxon signed rank measure to compare the distributions of binding free energy computed on the sampled conformations to predict likely pathways. Second, we represent chemical equilibrium aspects of the transitions as a Bayesian Factor graph where both associations and dissociations are modeled based on concentrations and the binding free energies. Results from both of these experiments showed significant departure from those one would obtain if only the static configurations of the proteins were considered. Hence, we establish the importance of an uncertainty-aware protocol for pathway analysis, and provide a statistical framework as an important first step towards assembly pathway prediction with high statistical confidence.