A model for viral assembly around an explicit RNA sequence generates an Implicit fitness landscape

Previously, a stochastic model of ssRNA virus assembly was created to model the cooperative effects between capsid proteins and genomic RNA that would occur in a packaging signal-mediated assembly process. In such a assembly scenario, multiple secondary structural elements from within the RNA, termed packaging signals (PS), contact coat proteins and facilitate efficient capsid assembly. In this work, the assembly model is extended to incorporate explicit nucleotide sequence information as well as simple aspects of RNA folding which would be occurring during the RNA/capsid co-assembly process. Applying this new paradigm to a dodecahedral viral capsid, a computer derived nucleotide sequence is evolved de novo that is optimal for packaging the RNA into capsids, while also containing capacity for coding for a viral protein. Analysis of the effects of mutations on the ability of the RNA sequence to successfully package into a viral capsid reveals a complex fitness landscape where the majority of mutations are neutral with respect to packaging efficiency with a small number of mutations resulting in a near complete loss of RNA packaging. Moreover, the model shows how attempts to ablate PSs in the viral RNA sequence may result in redundant PSs already present in the genome fulfilling their packaging role. This explains why recent experiments that attempt to ablate putative PSs may not see an effect on packaging. This modelling framework presents an example of how an implicit mapping can be made from genotype to a fitness parameter important for viral biology, i.e. viral capsid yield, with potential applications to theoretical models of viral evolution.