通过用于预测 RNA 病毒受限特征的硅学管道确定甲型流感病毒包装相关信号和其他功能元素的位置和结构

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2024-04-01 DOI:10.1371/journal.pcbi.1012009
Emma Beniston, J. Skittrall
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引用次数: 0

摘要

甲型流感病毒的分段基因组中有一些区域与将分段包装成病毒的能力有关,但许多此类区域的特征还不清楚。我们采用最近改进的 RNA 病毒受限区域划分方法,并应用结构预测算法,对这些包装相关区域的关键位置及其结构进行了详细预测。我们发现并描述了甲型流感基因组中的其他已知受限区域,包括与 PA-X 框变相关的区域、与替代剪接相关的区域,以及与禽类病毒相关的截短 PB1 蛋白 PB1-N92 启动基序周围的受限区域。我们还进一步预测了以前未曾描述过的受限区域的存在。我们的工作提供了额外的特征,使我们能够研究这些关键区域的药物靶点潜力,并指出片段之间包装兼容性的决定因素。
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Locations and structures of influenza A virus packaging-associated signals and other functional elements via an in silico pipeline for predicting constrained features in RNA viruses
Influenza A virus contains regions of its segmented genome associated with ability to package the segments into virions, but many such regions are poorly characterised. We provide detailed predictions of the key locations within these packaging-associated regions, and their structures, by applying a recently-improved pipeline for delineating constrained regions in RNA viruses and applying structural prediction algorithms. We find and characterise other known constrained regions within influenza A genomes, including the region associated with the PA-X frameshift, regions associated with alternative splicing, and constraint around the initiation motif for a truncated PB1 protein, PB1-N92, associated with avian viruses. We further predict the presence of constrained regions that have not previously been described. The extra characterisation our work provides allows investigation of these key regions for drug target potential, and points towards determinants of packaging compatibility between segments.
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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