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本文引用的文献

1
A computational framework for distinguishing direct versus indirect interactions in human functional protein-protein interaction networks.一种用于区分人类功能性蛋白质-蛋白质相互作用网络中直接与间接相互作用的计算框架。
Integr Biol (Camb). 2017 Jul 17;9(7):595-606. doi: 10.1039/c7ib00013h.
2
Similarity in viral and host promoters couples viral reactivation with host cell migration.病毒和宿主启动子的相似性将病毒的重新激活与宿主细胞的迁移联系起来。
Nat Commun. 2017 May 2;8:15006. doi: 10.1038/ncomms15006.
3
Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.利用基因本体论注释研究蛋白质序列相似性和语义相似性之间的相关性。
IEEE/ACM Trans Comput Biol Bioinform. 2018 May-Jun;15(3):905-912. doi: 10.1109/TCBB.2017.2695542. Epub 2017 Apr 18.
4
Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.使用信息内容和基因本体论图的拓扑属性评估蛋白质之间的语义相似性。
IEEE/ACM Trans Comput Biol Bioinform. 2018 May-Jun;15(3):839-849. doi: 10.1109/TCBB.2017.2689762. Epub 2017 Mar 31.
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Structure-based prediction of host-pathogen protein interactions.基于结构的宿主-病原体蛋白相互作用预测。
Curr Opin Struct Biol. 2017 Jun;44:119-124. doi: 10.1016/j.sbi.2017.02.007. Epub 2017 Mar 17.
6
Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.利用蛋白质-蛋白质相互作用网络预测埃博拉病毒感染相关人类基因
Comb Chem High Throughput Screen. 2017;20(7):638-646. doi: 10.2174/1386207320666170310114816.
7
Prediction of virus-host protein-protein interactions mediated by short linear motifs.由短线性基序介导的病毒-宿主蛋白质-蛋白质相互作用的预测
BMC Bioinformatics. 2017 Mar 9;18(1):163. doi: 10.1186/s12859-017-1570-7.
8
Multitask Matrix Completion for Learning Protein Interactions Across Diseases.用于跨疾病学习蛋白质相互作用的多任务矩阵补全
J Comput Biol. 2017 Jun;24(6):501-514. doi: 10.1089/cmb.2016.0201. Epub 2017 Jan 27.
9
Screening for clusters of charge in human virus proteomes.人类病毒蛋白质组中电荷簇的筛选。
BMC Genomics. 2016 Oct 17;17(Suppl 9):758. doi: 10.1186/s12864-016-3086-3.
10
An improved method for predicting interactions between virus and human proteins.一种预测病毒与人类蛋白质之间相互作用的改进方法。
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病毒-宿主蛋白相互作用预测的计算方法综述:以新型埃博拉病毒-人类相互作用为例

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.

机构信息

Department of Computer Science and Engineering, Jadavpur University, India.

出版信息

Brief Funct Genomics. 2018 Nov 26;17(6):381-391. doi: 10.1093/bfgp/elx026.

DOI:10.1093/bfgp/elx026
PMID:29028879
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7109800/
Abstract

Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

摘要

鉴定潜在的病毒-宿主相互作用对于控制具有高度传染性的病毒疾病非常有用和至关重要。这有助于开发治疗病毒感染的新药。最近,已经发表了一小部分人类蛋白质与埃博拉病毒 (EBOV) 之间经过临床和实验验证的相互作用的数据库记录。利用已知的 EBOV 人类相互作用伙伴的信息,我们的主要目标是确定一组可能与 EBOV 蛋白相互作用的蛋白质。在这里,我们首先回顾了用于预测传染病新型病毒-宿主相互作用的最先进的计算方法,然后对 EBOV-人类相互作用进行了案例研究。评估结果表明,预测的人类宿主蛋白在结构和语义上与 EBOV 的已知人类相互作用伙伴非常相似,并且负责相似的生化活性、途径和宿主-病原体关系。