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本文引用的文献

1
AligNet: alignment of protein-protein interaction networks.AligNet:蛋白质-蛋白质相互作用网络的对齐。
BMC Bioinformatics. 2020 Nov 18;21(Suppl 6):265. doi: 10.1186/s12859-020-3502-1.
2
Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks.通过整数线性规划对生物网络进行比对:病毒-宿主蛋白质-蛋白质相互作用网络
BMC Bioinformatics. 2020 Nov 18;21(Suppl 6):434. doi: 10.1186/s12859-020-03733-w.
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A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.一种 SARS-CoV-2 蛋白相互作用图谱揭示了药物再利用的靶标。
Nature. 2020 Jul;583(7816):459-468. doi: 10.1038/s41586-020-2286-9. Epub 2020 Apr 30.
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The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2.严重急性呼吸综合征相关冠状病毒:将 2019-nCoV 进行分类并命名为 SARS-CoV-2。
Nat Microbiol. 2020 Apr;5(4):536-544. doi: 10.1038/s41564-020-0695-z. Epub 2020 Mar 2.
5
GOnet: a tool for interactive Gene Ontology analysis.GOnet:一个用于交互式基因本体论分析的工具。
BMC Bioinformatics. 2018 Dec 7;19(1):470. doi: 10.1186/s12859-018-2533-3.
6
UniProt: a worldwide hub of protein knowledge.UniProt:蛋白质知识的全球枢纽。
Nucleic Acids Res. 2019 Jan 8;47(D1):D506-D515. doi: 10.1093/nar/gky1049.
7
GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms.GO-GO:一种改进的基因本体术语间语义相似度测量算法。
Sci Rep. 2018 Oct 10;8(1):15107. doi: 10.1038/s41598-018-33219-y.
8
Computational analysis of protein interaction networks for infectious diseases.传染病蛋白质相互作用网络的计算分析
Brief Bioinform. 2016 May;17(3):517-26. doi: 10.1093/bib/bbv059. Epub 2015 Aug 10.
9
L-GRAAL: Lagrangian graphlet-based network aligner.L-GRAAL:基于拉格朗日图元的网络对齐工具。
Bioinformatics. 2015 Jul 1;31(13):2182-9. doi: 10.1093/bioinformatics/btv130. Epub 2015 Feb 28.
10
VirHostNet 2.0: surfing on the web of virus/host molecular interactions data.VirHostNet 2.0:畅游病毒/宿主分子相互作用数据网络
Nucleic Acids Res. 2015 Jan;43(Database issue):D583-7. doi: 10.1093/nar/gku1121. Epub 2014 Nov 11.

基于整数线性规划的病毒-宿主蛋白-蛋白相互作用网络对齐:SARS-CoV-2。

Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2.

机构信息

Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain.

Algorithms, Bioinformatics, Complexity and Formal Methods Research Group, Technical University of Catalonia, Barcelona, Spain.

出版信息

PLoS One. 2020 Dec 7;15(12):e0236304. doi: 10.1371/journal.pone.0236304. eCollection 2020.

DOI:10.1371/journal.pone.0236304
PMID:33284827
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7721128/
Abstract

MOTIVATION

Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship.

RESULTS

We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

摘要

动机

除了社会经济问题,由新型冠状病毒 SARS-CoV-2 引起的传染病 COVID-19 大流行,对科学界造成了深远的影响,促使科学界加大力度研究新病毒的感染策略。在过去几个月进行的广泛而重要的研究中,病毒-宿主关系的分析在药物发现中起着重要作用。病毒-宿主蛋白-蛋白相互作用是病毒复制的活性因子,病毒-宿主蛋白-蛋白相互作用网络的分析是研究病毒-宿主关系的基础。

结果

我们对用于蛋白-蛋白互作网络比对的整数线性规划模型进行了调整和实现,并得到了 SARS-CoV-1 和 SARS-CoV-2 病毒-宿主蛋白-蛋白互作网络的一致比对。尽管这些病毒-宿主网络中没有共享的人类蛋白,并且保留的病毒-宿主相互作用数量较少,但一致比对揭示了与病毒感染相关功能的人类蛋白,以及与 SARS-CoV-1 和 SARS-CoV-2 蛋白相互作用的具有高功能相似性的人类蛋白,这些蛋白的比对可以保留这些病毒-宿主相互作用。