Title: Machine-learning a virus assembly fitness landscape,
Abstract: Realistic evolutionary fitness landscapes are notoriously difficult to
construct. A recent cutting-edge model of virus assembly consists of a
dodecahedral capsid with $12$ corresponding packaging signals in three affinity
bands. This whole genome/phenotype space consisting of $3^{12}$ genomes has
been explored via computationally expensive stochastic assembly models, giving
a fitness landscape in terms of the assembly efficiency. Using latest
machine-learning techniques by establishing a neural network, we show that the
intensive computation can be short-circuited in a matter of minutes to
astounding accuracy.