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Machine-learning a virus assembly fitness landscape

Dechant, P-P. & He, Y-H. ORCID: 0000-0002-0787-8380 (2021). Machine-learning a virus assembly fitness landscape. PLoS One, 16(5), doi: 10.1371/journal.pone.0250227

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.

Publication Type: Article
Additional Information: Copyright: © 2021 Dechant, He. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher Keywords: Computational Biology; Machine Learning; Mutation; Phenotype; Virus Assembly
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
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