If they undergo new mutations at each replication cycle, why are RNA viral genomes so fragile, with most mutations being either strongly deleterious or lethal? Here we provide theoretical and numerical evidence for the hypothesis that genetic fragility is partly an evolutionary response to the multiple population bottlenecks experienced by viral populations at various stages of their life cycles. Modelling within-host viral populations as multi-type branching processes, we show that mutational fragility lowers the rate at which Muller’s ratchet clicks and increases the survival probability through multiple bottlenecks. In the context of a susceptible-exposed-infectious-recovered epidemiological model, we find that the attack rate of fragile viral strains can exceed that of more robust strains, particularly at low infectivities and high mutation rates. Our findings highlight the importance of demographic events such as transmission bottlenecks in shaping the genetic architecture of viral pathogens.
Background
We study in this work the inverse folding problem for RNA, which is the discovery of sequences that fold into given target secondary structures.
Results
We implement a Lévy mutation scheme in an updated version of an evolutionary inverse folding algorithm and apply it to the design of RNAs with and without pseudoknots. We find that the Lévy mutation scheme increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. Compared to , CPU time is higher but more successful in finding designed sequences that fold correctly into the target structures.
Conclusion
We propose that a Lévy flight offers a better standard mutation scheme for optimizing RNA design. Our new version of is available on GitHub as a python script and the benchmark results show improved performance on both and the datasets, compared to existing inverse folding tools.
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