Machine-learning a virus assembly fitness landscape.

Machine-learning a virus assembly fitness landscape.

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 expl...

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Main Authors: Pierre-Philippe Dechant, Yang-Hui He
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0250227
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spelling doaj-6c1272c2bcbb43979a7f4ccce64984a42021-05-21T04:30:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01165e025022710.1371/journal.pone.0250227Machine-learning a virus assembly fitness landscape.Pierre-Philippe DechantYang-Hui HeRealistic 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.https://doi.org/10.1371/journal.pone.0250227
collection DOAJ
language English
format Article
sources DOAJ
author Pierre-Philippe Dechant
Yang-Hui He
spellingShingle Pierre-Philippe Dechant
Yang-Hui He
Machine-learning a virus assembly fitness landscape.
PLoS ONE
author_facet Pierre-Philippe Dechant
Yang-Hui He
author_sort Pierre-Philippe Dechant
title Machine-learning a virus assembly fitness landscape.
title_short Machine-learning a virus assembly fitness landscape.
title_full Machine-learning a virus assembly fitness landscape.
title_fullStr Machine-learning a virus assembly fitness landscape.
title_full_unstemmed Machine-learning a virus assembly fitness landscape.
title_sort machine-learning a virus assembly fitness landscape.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description 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.
url https://doi.org/10.1371/journal.pone.0250227
work_keys_str_mv AT pierrephilippedechant machinelearningavirusassemblyfitnesslandscape
AT yanghuihe machinelearningavirusassemblyfitnesslandscape
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