Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4393
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dc.contributor.authorFARHEEN, NIDAen_US
dc.contributor.authorThattai, Mukunden_US
dc.date.accessioned2020-01-31T04:40:09Z
dc.date.available2020-01-31T04:40:09Z
dc.date.issued2019-11en_US
dc.identifier.citationJournal of the Royal Society Interface, 16(160).en_US
dc.identifier.issn1742-5689en_US
dc.identifier.issn1742-56891742-5662en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4393-
dc.identifier.urihttps://doi.org/10.1098/rsif.2019.0411en_US
dc.description.abstractThe genome of the influenza virus consists of eight distinct single-stranded RNA segments, each encoding proteins essential for the viral life cycle. When the virus infects a host cell, these segments must be replicated and packaged into new budding virions. The viral genome is assembled with remarkably high fidelity: experiments reveal that most virions contain precisely one copy of each of the eight RNA segments. Cell-biological studies suggest that genome assembly is mediated by specific reversible and irreversible interactions between the RNA segments and their associated proteins. However, the precise inter-segment interaction network remains unresolved. Here, we computationally predict that tree-like irreversible interaction networks guarantee high-fidelity genome assembly, while cyclic interaction networks lead to futile or frustrated off-pathway products. We test our prediction against multiple experimental datasets. We find that tree-like networks capture the nearest-neighbour statistics of RNA segments in packaged virions, as observed by electron tomography. Just eight tree-like networks (of a possible 262 144) optimally capture both the nearest-neighbour data and independently measuredRNA-RNAbinding and co-localization propensities. These eight do not include the previously proposed hub-and-spoke and linear networks. Rather, each predicted network combines hub-like and linear features, consistent with evolutionary models of interaction gain and loss.en_US
dc.language.isoenen_US
dc.publisherThe Royal Societyen_US
dc.subjectSegmented virusen_US
dc.subjectInfluenzaen_US
dc.subjectSelf-assemblyen_US
dc.subjectNetwork evolutionen_US
dc.subjectTOC-JAN-2020en_US
dc.subject2019en_US
dc.titleFrustration and fidelity in influenza genome assemblyen_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Biologyen_US
dc.identifier.sourcetitleJournal of the Royal Society Interfaceen_US
dc.publication.originofpublisherForeignen_US
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