Machine-learning a virus assembly fitness landscape

Dechant, P.-P.; He, Y.-H.

doi:10.1101/567610

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bioRxiv · 10.1101/567610

Machine-learning a virus assembly fitness landscape

Dechant, P.-P.·He, Y.-H.

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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.

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Dechant, P.-P., He, Y.-H.. 2019-03-04. Machine-learning a virus assembly fitness landscape. https://doi.org/10.1101/567610

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