NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets

被引:3
|
作者
Zhu, Lijuan [1 ]
Zhang, Ju [2 ,3 ]
Zhang, Yi [4 ]
Lang, Jidong [5 ]
Xiang, Ju [6 ,7 ,8 ]
Bai, Xiaogang [4 ]
Yan, Na [5 ]
Tian, Geng [5 ]
Zhang, Huajun [1 ]
Yang, Jialiang [5 ]
机构
[1] Zhejiang Normal Univ, Coll Math & Comp Sci, Jinhua, Zhejiang, Peoples R China
[2] Capital Med Univ, Beijing Ditan Hosp, Inst Infect Dis, Beijing, Peoples R China
[3] Beijing Key Lab Emerging Infect Dis, Beijing, Peoples R China
[4] Hebei Univ Sci & Technol, Dept Math, Shijiazhuang, Hebei, Peoples R China
[5] Geneis Beijing Co Ltd, Beijing, Peoples R China
[6] Changsha Med Univ, Neurosci Res Ctr, Changsha, Peoples R China
[7] Changsha Med Univ, Dept Basic Med Sci, Changsha, Peoples R China
[8] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
基金
中国国家自然科学基金;
关键词
PPI network; network alignment; gene ontology; graphlets; functional ortholog; PROTEIN-INTERACTION NETWORKS; GLOBAL ALIGNMENT; YEAST; CONTEXT;
D O I
10.3389/fbioe.2020.00547
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
With the development of high throughput technologies, there are more and more protein-protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary relationship across species or biological entities. However, network alignment is an NP-complete problem, and previous algorithms are usually slow or less accurate in aligning big networks like human vs. yeast. In this study, we proposed a fast yet accurate algorithm called Network Alignment by Integrating Biological Process (NAIGO). Specifically, we first divided the networks into subnets taking the advantage of known prior knowledge, such as gene ontology. For each subnet pair, we then developed a novel method to align them by considering both protein orthologous information and their local structural information. After that, we expanded the obtained local network alignments in a greedy manner. Taking the aligned pairs as seeds, we formulated the global network alignment problem as an assignment problem based on similarity matrix, which was solved by the Hungarian method. We applied NAIGO to align human andSaccharomyces cerevisiaeS288c PPI network and compared the results with other popular methods like IsoRank, GRAAL, SANA, and NABEECO. As a result, our method outperformed the competitors by aligning more orthologous proteins or matched interactions. In addition, we found a few potential functional orthologous proteins such as RRM2B in human and DNA2 inS. cerevisiaeS288c, which are related to DNA repair. We also identified a conserved subnet with six orthologous proteins EXO1, MSH3, MSH2, MLH1, MLH3, and MSH6, and six aligned interactions. All these proteins are associated with mismatch repair. Finally, we predicted a few proteins ofS. cerevisiaeS288c potentially involving in certain biological processes like autophagosome assembly.
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页数:14
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