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.

13 pages, 4 figures

References in corpus (2)

• The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
• Finite Temperature Phase Behavior of Viral Capsids as Oriented Particle Shells