Machine-learning a virus assembly fitness landscape.
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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 312 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.
SUBMITTER: Dechant PP
PROVIDER: S-EPMC8099058 | biostudies-literature |
REPOSITORIES: biostudies-literature
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